* using log directory 'd:/RCompile/CRANincoming/R-devel/edgemodelr.Rcheck'
* using R Under development (unstable) (2025-08-30 r88742 ucrt)
* using platform: x86_64-w64-mingw32
* R was compiled by
    gcc.exe (GCC) 14.2.0
    GNU Fortran (GCC) 14.2.0
* running under: Windows Server 2022 x64 (build 20348)
* using session charset: UTF-8
* checking for file 'edgemodelr/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'edgemodelr' version '0.1.0'
* package encoding: UTF-8
* checking CRAN incoming feasibility ... NOTE
Maintainer: 'Pawan Rama Mali <prm@outlook.in>'

New submission

Possibly misspelled words in DESCRIPTION:
  GGUF (8:73)
  GPT (9:6)
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'edgemodelr' can be installed ... WARNING
Found the following significant warnings:
  D:/rtools45/x86_64-w64-mingw32.static.posix/lib/gcc/x86_64-w64-mingw32.static.posix/14.2.0/include/c++/bits/regex.h:1922:55: warning: array subscript -3 is outside array bounds of 'std::__cxx11::sub_match<const wchar_t*> [384307168202282325]' [-Warray-bounds=]
  D:/rtools45/x86_64-w64-mingw32.static.posix/lib/gcc/x86_64-w64-mingw32.static.posix/14.2.0/include/c++/bits/regex.h:1922:55: warning: array subscript -3 is outside array bounds of 'std::__cxx11::sub_match<const char*> [384307168202282325]' [-Warray-bounds=]
See 'd:/RCompile/CRANincoming/R-devel/edgemodelr.Rcheck/00install.out' for details.
* used C++ compiler: 'g++.exe (GCC) 14.2.0'
* checking C++ specification ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking for future file timestamps ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking whether startup messages can be suppressed ... OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd line widths ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking compilation flags in Makevars ... OK
* checking for GNU extensions in Makefiles ... OK
* checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
* checking use of PKG_*FLAGS in Makefiles ... OK
* checking use of SHLIB_OPENMP_*FLAGS in Makefiles ... OK
* checking pragmas in C/C++ headers and code ... OK
* checking compilation flags used ... OK
* checking compiled code ... OK
* checking examples ... OK
* checking for unstated dependencies in 'tests' ... OK
* checking tests ... [221s] ERROR
  Running 'testthat.R' [220s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > library(testthat)
  > library(edgemodelr)
  > 
  > test_check("edgemodelr")
  Model file not found: does_not_exist.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found:  
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: dummy.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: dummy.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: dummy.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: dummy.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  gguf_init_from_file_impl: invalid magic characters: 'This', expected 'GGUF'
  llama_model_load: error loading model: llama_model_loader: failed to load model from D:\RCompile\CRANincoming\R-devel\edgemodelr.Rcheck\tests\testthat\fake_model.gguf
  llama_model_load_from_file_impl: failed to load model
  Model file not found: nonexistent.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: clearly_nonexistent_file_xyz123.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent1.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent2.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent3.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent4.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent5.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent6.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent7.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent8.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent9.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent10.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent11.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent12.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent13.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent14.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent15.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent16.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent17.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent18.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent19.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent20.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: fake.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Running on: Windows x86-64
  Model file not found: nonexistent_model.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found:  
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  gguf_init_from_file: failed to open GGUF file 'D:\temp\2025_08_31_19_40_17_21872\Rtmpm0HH0m'
  llama_model_load: error loading model: llama_model_loader: failed to load model from D:\temp\2025_08_31_19_40_17_21872\Rtmpm0HH0m
  llama_model_load_from_file_impl: failed to load model
  gguf_init_from_file_impl: invalid magic characters: 'not ', expected 'GGUF'
  llama_model_load: error loading model: llama_model_loader: failed to load model from D:\temp\2025_08_31_19_40_17_21872\Rtmpm0HH0m\file10de04c34da.txt
  llama_model_load_from_file_impl: failed to load model
  Model file not found: nonexistent.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: nonexistent.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: test<model.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: test>model.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: test:model.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: test"model.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: test|model.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: test?model.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: test*model.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: test_中文.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  gguf_init_from_file_impl: invalid magic characters: '????', expected 'GGUF'
  llama_model_load: error loading model: llama_model_loader: failed to load model from D:\temp\2025_08_31_19_40_17_21872\Rtmpm0HH0m\file10de0678732c5.bin
  llama_model_load_from_file_impl: failed to load model
  Created cache directory: D:\temp\2025_08_31_19_40_17_21872\Rtmpm0HH0m/test_cache_dir_12345 
  Downloading model...
  From: https://huggingface.co/fake/model/resolve/main/fake.gguf 
  To:   D:\temp\2025_08_31_19_40_17_21872\Rtmpm0HH0m/test_cache_dir_12345/fake.gguf 
  trying URL 'https://huggingface.co/fake/model/resolve/main/fake.gguf'
  Download method auto failed: cannot open URL 'https://huggingface.co/fake/model/resolve/main/fake.gguf' 
    % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                   Dload  Upload   Total   Spent    Left  Speed
  
    0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
  100    29  100    29    0     0    209      0 --:--:-- --:--:-- --:--:--   211
  100    29  100    29    0     0    209      0 --:--:-- --:--:-- --:--:--   211
  --2025-08-31 19:45:43--  https://huggingface.co/fake/model/resolve/main/fake.gguf
  Loaded CA certificate '/usr/ssl/certs/ca-bundle.crt'
  Resolving huggingface.co (huggingface.co)... 52.222.136.89, 52.222.136.92, 52.222.136.117, ...
  Connecting to huggingface.co (huggingface.co)|52.222.136.89|:443... connected.
  HTTP request sent, awaiting response... 401 Unauthorized
  
  Username/Password Authentication Failed.
  Download method wget failed: 'wget' call had nonzero exit status 
  trying URL 'https://huggingface.co/fake/model/resolve/main/fake.gguf'
  Download method wininet failed: cannot open URL 'https://huggingface.co/fake/model/resolve/main/fake.gguf' 
  Download failed. Possible solutions:
  1. Check your internet connection
  2. Try downloading manually:
     curl -L -o ' D:\temp\2025_08_31_19_40_17_21872\Rtmpm0HH0m/test_cache_dir_12345/fake.gguf ' ' https://huggingface.co/fake/model/resolve/main/fake.gguf '
  3. Or use a different model from edge_list_models()
  Setting up TinyLlama-1.1B ...
  Created cache directory: C:\Users\CRAN\Documents/.cache/edgemodelr 
  Downloading model...
  From: https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf 
  To:   C:\Users\CRAN\Documents/.cache/edgemodelr/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf 
  trying URL 'https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf'
  Content type 'binary/octet-stream' length 668788096 bytes (637.8 MB)
  ==================================================
  downloaded 637.8 MB
  
  Download completed successfully!
  Model size: 637.8 MB
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 2048
  llama_context: n_ctx_per_seq = 2048
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 2048 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    44.00 MiB
  llama_kv_cache: size =   44.00 MiB (  2048 cells,  22 layers,  1/1 seqs), K (f16):   22.00 MiB, V (f16):   22.00 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =   149.01 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  Setting up TinyLlama-OpenOrca ...
  Downloading model...
  From: https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_K_M.gguf 
  To:   C:\Users\CRAN\Documents/.cache/edgemodelr/tinyllama-1.1b-1t-openorca.Q4_K_M.gguf 
  trying URL 'https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_K_M.gguf'
  Content type 'binary/octet-stream' length 667814368 bytes (636.9 MB)
  ==================================================
  downloaded 636.9 MB
  
  Download completed successfully!
  Model size: 636.9 MB
  llama_model_loader: loaded meta data with 19 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-1t-openorca.Q4_K_M.gguf (version GGUF V2)
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = jeff31415_tinyllama-1.1b-1t-openorca
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                          general.file_type u32              = 15
  llama_model_loader: - kv  11:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  12:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  13:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  14:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  15:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  16:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  17:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  18:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V2
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = jeff31415_tinyllama-1.1b-1t-openorca
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ......................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 2048
  llama_context: n_ctx_per_seq = 2048
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 2048 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    44.00 MiB
  llama_kv_cache: size =   44.00 MiB (  2048 cells,  22 layers,  1/1 seqs), K (f16):   22.00 MiB, V (f16):   22.00 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =   149.01 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  Setting up llama3.2-1b ...
  Downloading model...
  From: https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_K_M.gguf 
  To:   C:\Users\CRAN\Documents/.cache/edgemodelr/Llama-3.2-1B-Instruct-Q4_K_M.gguf 
  trying URL 'https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_K_M.gguf'
  Content type 'text/plain; charset=utf-8' length 807694464 bytes (770.3 MB)
  ==================================================
  downloaded 770.3 MB
  
  Download completed successfully!
  Model size: 770.3 MB
  llama_model_loader: loaded meta data with 35 key-value pairs and 147 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\Llama-3.2-1B-Instruct-Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.type str              = model
  llama_model_loader: - kv   2:                               general.name str              = Llama 3.2 1B Instruct
  llama_model_loader: - kv   3:                           general.finetune str              = Instruct
  llama_model_loader: - kv   4:                           general.basename str              = Llama-3.2
  llama_model_loader: - kv   5:                         general.size_label str              = 1B
  llama_model_loader: - kv   6:                            general.license str              = llama3.2
  llama_model_loader: - kv   7:                               general.tags arr[str,6]       = ["facebook", "meta", "pytorch", "llam...
  llama_model_loader: - kv   8:                          general.languages arr[str,8]       = ["en", "de", "fr", "it", "pt", "hi", ...
  llama_model_loader: - kv   9:                          llama.block_count u32              = 16
  llama_model_loader: - kv  10:                       llama.context_length u32              = 131072
  llama_model_loader: - kv  11:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv  12:                  llama.feed_forward_length u32              = 8192
  llama_model_loader: - kv  13:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv  14:              llama.attention.head_count_kv u32              = 8
  llama_model_loader: - kv  15:                       llama.rope.freq_base f32              = 500000.000000
  llama_model_loader: - kv  16:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  17:                 llama.attention.key_length u32              = 64
  llama_model_loader: - kv  18:               llama.attention.value_length u32              = 64
  llama_model_loader: - kv  19:                          general.file_type u32              = 15
  llama_model_loader: - kv  20:                           llama.vocab_size u32              = 128256
  llama_model_loader: - kv  21:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv  22:                       tokenizer.ggml.model str              = gpt2
  llama_model_loader: - kv  23:                         tokenizer.ggml.pre str              = llama-bpe
  llama_model_loader: - kv  24:                      tokenizer.ggml.tokens arr[str,128256]  = ["!", "\"", "#", "$", "%", "&", "'", ...
  llama_model_loader: - kv  25:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
  llama_model_loader: - kv  26:                      tokenizer.ggml.merges arr[str,280147]  = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
  llama_model_loader: - kv  27:                tokenizer.ggml.bos_token_id u32              = 128000
  llama_model_loader: - kv  28:                tokenizer.ggml.eos_token_id u32              = 128009
  llama_model_loader: - kv  29:                    tokenizer.chat_template str              = {{- bos_token }}\n{%- if custom_tools ...
  llama_model_loader: - kv  30:               general.quantization_version u32              = 2
  llama_model_loader: - kv  31:                      quantize.imatrix.file str              = /models_out/Llama-3.2-1B-Instruct-GGU...
  llama_model_loader: - kv  32:                   quantize.imatrix.dataset str              = /training_dir/calibration_datav3.txt
  llama_model_loader: - kv  33:             quantize.imatrix.entries_count i32              = 112
  llama_model_loader: - kv  34:              quantize.imatrix.chunks_count i32              = 125
  llama_model_loader: - type  f32:   34 tensors
  llama_model_loader: - type q4_K:   96 tensors
  llama_model_loader: - type q6_K:   17 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 762.81 MiB (5.18 BPW) 
  init_tokenizer: initializing tokenizer for type 2
  load: control token: 128254 '<|reserved_special_token_246|>' is not marked as EOG
  load: control token: 128249 '<|reserved_special_token_241|>' is not marked as EOG
  load: control token: 128246 '<|reserved_special_token_238|>' is not marked as EOG
  load: control token: 128243 '<|reserved_special_token_235|>' is not marked as EOG
  load: control token: 128242 '<|reserved_special_token_234|>' is not marked as EOG
  load: control token: 128241 '<|reserved_special_token_233|>' is not marked as EOG
  load: control token: 128240 '<|reserved_special_token_232|>' is not marked as EOG
  load: control token: 128235 '<|reserved_special_token_227|>' is not marked as EOG
  load: control token: 128231 '<|reserved_special_token_223|>' is not marked as EOG
  load: control token: 128230 '<|reserved_special_token_222|>' is not marked as EOG
  load: control token: 128228 '<|reserved_special_token_220|>' is not marked as EOG
  load: control token: 128225 '<|reserved_special_token_217|>' is not marked as EOG
  load: control token: 128218 '<|reserved_special_token_210|>' is not marked as EOG
  load: control token: 128214 '<|reserved_special_token_206|>' is not marked as EOG
  load: control token: 128213 '<|reserved_special_token_205|>' is not marked as EOG
  load: control token: 128207 '<|reserved_special_token_199|>' is not marked as EOG
  load: control token: 128206 '<|reserved_special_token_198|>' is not marked as EOG
  load: control token: 128204 '<|reserved_special_token_196|>' is not marked as EOG
  load: control token: 128200 '<|reserved_special_token_192|>' is not marked as EOG
  load: control token: 128199 '<|reserved_special_token_191|>' is not marked as EOG
  load: control token: 128198 '<|reserved_special_token_190|>' is not marked as EOG
  load: control token: 128196 '<|reserved_special_token_188|>' is not marked as EOG
  load: control token: 128194 '<|reserved_special_token_186|>' is not marked as EOG
  load: control token: 128193 '<|reserved_special_token_185|>' is not marked as EOG
  load: control token: 128188 '<|reserved_special_token_180|>' is not marked as EOG
  load: control token: 128187 '<|reserved_special_token_179|>' is not marked as EOG
  load: control token: 128185 '<|reserved_special_token_177|>' is not marked as EOG
  load: control token: 128184 '<|reserved_special_token_176|>' is not marked as EOG
  load: control token: 128180 '<|reserved_special_token_172|>' is not marked as EOG
  load: control token: 128179 '<|reserved_special_token_171|>' is not marked as EOG
  load: control token: 128178 '<|reserved_special_token_170|>' is not marked as EOG
  load: control token: 128177 '<|reserved_special_token_169|>' is not marked as EOG
  load: control token: 128176 '<|reserved_special_token_168|>' is not marked as EOG
  load: control token: 128175 '<|reserved_special_token_167|>' is not marked as EOG
  load: control token: 128171 '<|reserved_special_token_163|>' is not marked as EOG
  load: control token: 128170 '<|reserved_special_token_162|>' is not marked as EOG
  load: control token: 128169 '<|reserved_special_token_161|>' is not marked as EOG
  load: control token: 128168 '<|reserved_special_token_160|>' is not marked as EOG
  load: control token: 128165 '<|reserved_special_token_157|>' is not marked as EOG
  load: control token: 128162 '<|reserved_special_token_154|>' is not marked as EOG
  load: control token: 128158 '<|reserved_special_token_150|>' is not marked as EOG
  load: control token: 128156 '<|reserved_special_token_148|>' is not marked as EOG
  load: control token: 128155 '<|reserved_special_token_147|>' is not marked as EOG
  load: control token: 128154 '<|reserved_special_token_146|>' is not marked as EOG
  load: control token: 128151 '<|reserved_special_token_143|>' is not marked as EOG
  load: control token: 128149 '<|reserved_special_token_141|>' is not marked as EOG
  load: control token: 128147 '<|reserved_special_token_139|>' is not marked as EOG
  load: control token: 128146 '<|reserved_special_token_138|>' is not marked as EOG
  load: control token: 128144 '<|reserved_special_token_136|>' is not marked as EOG
  load: control token: 128142 '<|reserved_special_token_134|>' is not marked as EOG
  load: control token: 128141 '<|reserved_special_token_133|>' is not marked as EOG
  load: control token: 128138 '<|reserved_special_token_130|>' is not marked as EOG
  load: control token: 128136 '<|reserved_special_token_128|>' is not marked as EOG
  load: control token: 128135 '<|reserved_special_token_127|>' is not marked as EOG
  load: control token: 128134 '<|reserved_special_token_126|>' is not marked as EOG
  load: control token: 128133 '<|reserved_special_token_125|>' is not marked as EOG
  load: control token: 128131 '<|reserved_special_token_123|>' is not marked as EOG
  load: control token: 128128 '<|reserved_special_token_120|>' is not marked as EOG
  load: control token: 128124 '<|reserved_special_token_116|>' is not marked as EOG
  load: control token: 128123 '<|reserved_special_token_115|>' is not marked as EOG
  load: control token: 128122 '<|reserved_special_token_114|>' is not marked as EOG
  load: control token: 128119 '<|reserved_special_token_111|>' is not marked as EOG
  load: control token: 128115 '<|reserved_special_token_107|>' is not marked as EOG
  load: control token: 128112 '<|reserved_special_token_104|>' is not marked as EOG
  load: control token: 128110 '<|reserved_special_token_102|>' is not marked as EOG
  load: control token: 128109 '<|reserved_special_token_101|>' is not marked as EOG
  load: control token: 128108 '<|reserved_special_token_100|>' is not marked as EOG
  load: control token: 128106 '<|reserved_special_token_98|>' is not marked as EOG
  load: control token: 128103 '<|reserved_special_token_95|>' is not marked as EOG
  load: control token: 128102 '<|reserved_special_token_94|>' is not marked as EOG
  load: control token: 128101 '<|reserved_special_token_93|>' is not marked as EOG
  load: control token: 128097 '<|reserved_special_token_89|>' is not marked as EOG
  load: control token: 128091 '<|reserved_special_token_83|>' is not marked as EOG
  load: control token: 128090 '<|reserved_special_token_82|>' is not marked as EOG
  load: control token: 128089 '<|reserved_special_token_81|>' is not marked as EOG
  load: control token: 128087 '<|reserved_special_token_79|>' is not marked as EOG
  load: control token: 128085 '<|reserved_special_token_77|>' is not marked as EOG
  load: control token: 128081 '<|reserved_special_token_73|>' is not marked as EOG
  load: control token: 128078 '<|reserved_special_token_70|>' is not marked as EOG
  load: control token: 128076 '<|reserved_special_token_68|>' is not marked as EOG
  load: control token: 128075 '<|reserved_special_token_67|>' is not marked as EOG
  load: control token: 128073 '<|reserved_special_token_65|>' is not marked as EOG
  load: control token: 128068 '<|reserved_special_token_60|>' is not marked as EOG
  load: control token: 128067 '<|reserved_special_token_59|>' is not marked as EOG
  load: control token: 128065 '<|reserved_special_token_57|>' is not marked as EOG
  load: control token: 128063 '<|reserved_special_token_55|>' is not marked as EOG
  load: control token: 128062 '<|reserved_special_token_54|>' is not marked as EOG
  load: control token: 128060 '<|reserved_special_token_52|>' is not marked as EOG
  load: control token: 128059 '<|reserved_special_token_51|>' is not marked as EOG
  load: control token: 128057 '<|reserved_special_token_49|>' is not marked as EOG
  load: control token: 128054 '<|reserved_special_token_46|>' is not marked as EOG
  load: control token: 128046 '<|reserved_special_token_38|>' is not marked as EOG
  load: control token: 128045 '<|reserved_special_token_37|>' is not marked as EOG
  load: control token: 128044 '<|reserved_special_token_36|>' is not marked as EOG
  load: control token: 128043 '<|reserved_special_token_35|>' is not marked as EOG
  load: control token: 128038 '<|reserved_special_token_30|>' is not marked as EOG
  load: control token: 128036 '<|reserved_special_token_28|>' is not marked as EOG
  load: control token: 128035 '<|reserved_special_token_27|>' is not marked as EOG
  load: control token: 128032 '<|reserved_special_token_24|>' is not marked as EOG
  load: control token: 128028 '<|reserved_special_token_20|>' is not marked as EOG
  load: control token: 128027 '<|reserved_special_token_19|>' is not marked as EOG
  load: control token: 128024 '<|reserved_special_token_16|>' is not marked as EOG
  load: control token: 128023 '<|reserved_special_token_15|>' is not marked as EOG
  load: control token: 128022 '<|reserved_special_token_14|>' is not marked as EOG
  load: control token: 128021 '<|reserved_special_token_13|>' is not marked as EOG
  load: control token: 128018 '<|reserved_special_token_10|>' is not marked as EOG
  load: control token: 128016 '<|reserved_special_token_8|>' is not marked as EOG
  load: control token: 128015 '<|reserved_special_token_7|>' is not marked as EOG
  load: control token: 128013 '<|reserved_special_token_5|>' is not marked as EOG
  load: control token: 128011 '<|reserved_special_token_3|>' is not marked as EOG
  load: control token: 128005 '<|reserved_special_token_2|>' is not marked as EOG
  load: control token: 128004 '<|finetune_right_pad_id|>' is not marked as EOG
  load: control token: 128002 '<|reserved_special_token_0|>' is not marked as EOG
  load: control token: 128252 '<|reserved_special_token_244|>' is not marked as EOG
  load: control token: 128190 '<|reserved_special_token_182|>' is not marked as EOG
  load: control token: 128183 '<|reserved_special_token_175|>' is not marked as EOG
  load: control token: 128137 '<|reserved_special_token_129|>' is not marked as EOG
  load: control token: 128182 '<|reserved_special_token_174|>' is not marked as EOG
  load: control token: 128040 '<|reserved_special_token_32|>' is not marked as EOG
  load: control token: 128048 '<|reserved_special_token_40|>' is not marked as EOG
  load: control token: 128092 '<|reserved_special_token_84|>' is not marked as EOG
  load: control token: 128215 '<|reserved_special_token_207|>' is not marked as EOG
  load: control token: 128107 '<|reserved_special_token_99|>' is not marked as EOG
  load: control token: 128208 '<|reserved_special_token_200|>' is not marked as EOG
  load: control token: 128145 '<|reserved_special_token_137|>' is not marked as EOG
  load: control token: 128031 '<|reserved_special_token_23|>' is not marked as EOG
  load: control token: 128129 '<|reserved_special_token_121|>' is not marked as EOG
  load: control token: 128201 '<|reserved_special_token_193|>' is not marked as EOG
  load: control token: 128074 '<|reserved_special_token_66|>' is not marked as EOG
  load: control token: 128095 '<|reserved_special_token_87|>' is not marked as EOG
  load: control token: 128186 '<|reserved_special_token_178|>' is not marked as EOG
  load: control token: 128143 '<|reserved_special_token_135|>' is not marked as EOG
  load: control token: 128229 '<|reserved_special_token_221|>' is not marked as EOG
  load: control token: 128007 '<|end_header_id|>' is not marked as EOG
  load: control token: 128055 '<|reserved_special_token_47|>' is not marked as EOG
  load: control token: 128056 '<|reserved_special_token_48|>' is not marked as EOG
  load: control token: 128061 '<|reserved_special_token_53|>' is not marked as EOG
  load: control token: 128153 '<|reserved_special_token_145|>' is not marked as EOG
  load: control token: 128152 '<|reserved_special_token_144|>' is not marked as EOG
  load: control token: 128212 '<|reserved_special_token_204|>' is not marked as EOG
  load: control token: 128172 '<|reserved_special_token_164|>' is not marked as EOG
  load: control token: 128160 '<|reserved_special_token_152|>' is not marked as EOG
  load: control token: 128041 '<|reserved_special_token_33|>' is not marked as EOG
  load: control token: 128181 '<|reserved_special_token_173|>' is not marked as EOG
  load: control token: 128094 '<|reserved_special_token_86|>' is not marked as EOG
  load: control token: 128118 '<|reserved_special_token_110|>' is not marked as EOG
  load: control token: 128236 '<|reserved_special_token_228|>' is not marked as EOG
  load: control token: 128148 '<|reserved_special_token_140|>' is not marked as EOG
  load: control token: 128042 '<|reserved_special_token_34|>' is not marked as EOG
  load: control token: 128139 '<|reserved_special_token_131|>' is not marked as EOG
  load: control token: 128173 '<|reserved_special_token_165|>' is not marked as EOG
  load: control token: 128239 '<|reserved_special_token_231|>' is not marked as EOG
  load: control token: 128157 '<|reserved_special_token_149|>' is not marked as EOG
  load: control token: 128052 '<|reserved_special_token_44|>' is not marked as EOG
  load: control token: 128026 '<|reserved_special_token_18|>' is not marked as EOG
  load: control token: 128003 '<|reserved_special_token_1|>' is not marked as EOG
  load: control token: 128019 '<|reserved_special_token_11|>' is not marked as EOG
  load: control token: 128116 '<|reserved_special_token_108|>' is not marked as EOG
  load: control token: 128161 '<|reserved_special_token_153|>' is not marked as EOG
  load: control token: 128226 '<|reserved_special_token_218|>' is not marked as EOG
  load: control token: 128159 '<|reserved_special_token_151|>' is not marked as EOG
  load: control token: 128012 '<|reserved_special_token_4|>' is not marked as EOG
  load: control token: 128088 '<|reserved_special_token_80|>' is not marked as EOG
  load: control token: 128163 '<|reserved_special_token_155|>' is not marked as EOG
  load: control token: 128113 '<|reserved_special_token_105|>' is not marked as EOG
  load: control token: 128250 '<|reserved_special_token_242|>' is not marked as EOG
  load: control token: 128125 '<|reserved_special_token_117|>' is not marked as EOG
  load: control token: 128053 '<|reserved_special_token_45|>' is not marked as EOG
  load: control token: 128224 '<|reserved_special_token_216|>' is not marked as EOG
  load: control token: 128247 '<|reserved_special_token_239|>' is not marked as EOG
  load: control token: 128251 '<|reserved_special_token_243|>' is not marked as EOG
  load: control token: 128216 '<|reserved_special_token_208|>' is not marked as EOG
  load: control token: 128006 '<|start_header_id|>' is not marked as EOG
  load: control token: 128211 '<|reserved_special_token_203|>' is not marked as EOG
  load: control token: 128077 '<|reserved_special_token_69|>' is not marked as EOG
  load: control token: 128237 '<|reserved_special_token_229|>' is not marked as EOG
  load: control token: 128086 '<|reserved_special_token_78|>' is not marked as EOG
  load: control token: 128227 '<|reserved_special_token_219|>' is not marked as EOG
  load: control token: 128058 '<|reserved_special_token_50|>' is not marked as EOG
  load: control token: 128100 '<|reserved_special_token_92|>' is not marked as EOG
  load: control token: 128209 '<|reserved_special_token_201|>' is not marked as EOG
  load: control token: 128084 '<|reserved_special_token_76|>' is not marked as EOG
  load: control token: 128071 '<|reserved_special_token_63|>' is not marked as EOG
  load: control token: 128070 '<|reserved_special_token_62|>' is not marked as EOG
  load: control token: 128049 '<|reserved_special_token_41|>' is not marked as EOG
  load: control token: 128197 '<|reserved_special_token_189|>' is not marked as EOG
  load: control token: 128072 '<|reserved_special_token_64|>' is not marked as EOG
  load: control token: 128000 '<|begin_of_text|>' is not marked as EOG
  load: control token: 128223 '<|reserved_special_token_215|>' is not marked as EOG
  load: control token: 128217 '<|reserved_special_token_209|>' is not marked as EOG
  load: control token: 128111 '<|reserved_special_token_103|>' is not marked as EOG
  load: control token: 128203 '<|reserved_special_token_195|>' is not marked as EOG
  load: control token: 128051 '<|reserved_special_token_43|>' is not marked as EOG
  load: control token: 128030 '<|reserved_special_token_22|>' is not marked as EOG
  load: control token: 128117 '<|reserved_special_token_109|>' is not marked as EOG
  load: control token: 128010 '<|python_tag|>' is not marked as EOG
  load: control token: 128238 '<|reserved_special_token_230|>' is not marked as EOG
  load: control token: 128255 '<|reserved_special_token_247|>' is not marked as EOG
  load: control token: 128202 '<|reserved_special_token_194|>' is not marked as EOG
  load: control token: 128132 '<|reserved_special_token_124|>' is not marked as EOG
  load: control token: 128248 '<|reserved_special_token_240|>' is not marked as EOG
  load: control token: 128167 '<|reserved_special_token_159|>' is not marked as EOG
  load: control token: 128127 '<|reserved_special_token_119|>' is not marked as EOG
  load: control token: 128105 '<|reserved_special_token_97|>' is not marked as EOG
  load: control token: 128039 '<|reserved_special_token_31|>' is not marked as EOG
  load: control token: 128232 '<|reserved_special_token_224|>' is not marked as EOG
  load: control token: 128166 '<|reserved_special_token_158|>' is not marked as EOG
  load: control token: 128130 '<|reserved_special_token_122|>' is not marked as EOG
  load: control token: 128114 '<|reserved_special_token_106|>' is not marked as EOG
  load: control token: 128234 '<|reserved_special_token_226|>' is not marked as EOG
  load: control token: 128191 '<|reserved_special_token_183|>' is not marked as EOG
  load: control token: 128064 '<|reserved_special_token_56|>' is not marked as EOG
  load: control token: 128140 '<|reserved_special_token_132|>' is not marked as EOG
  load: control token: 128096 '<|reserved_special_token_88|>' is not marked as EOG
  load: control token: 128098 '<|reserved_special_token_90|>' is not marked as EOG
  load: control token: 128192 '<|reserved_special_token_184|>' is not marked as EOG
  load: control token: 128093 '<|reserved_special_token_85|>' is not marked as EOG
  load: control token: 128150 '<|reserved_special_token_142|>' is not marked as EOG
  load: control token: 128222 '<|reserved_special_token_214|>' is not marked as EOG
  load: control token: 128233 '<|reserved_special_token_225|>' is not marked as EOG
  load: control token: 128220 '<|reserved_special_token_212|>' is not marked as EOG
  load: control token: 128034 '<|reserved_special_token_26|>' is not marked as EOG
  load: control token: 128033 '<|reserved_special_token_25|>' is not marked as EOG
  load: control token: 128253 '<|reserved_special_token_245|>' is not marked as EOG
  load: control token: 128195 '<|reserved_special_token_187|>' is not marked as EOG
  load: control token: 128099 '<|reserved_special_token_91|>' is not marked as EOG
  load: control token: 128189 '<|reserved_special_token_181|>' is not marked as EOG
  load: control token: 128210 '<|reserved_special_token_202|>' is not marked as EOG
  load: control token: 128174 '<|reserved_special_token_166|>' is not marked as EOG
  load: control token: 128083 '<|reserved_special_token_75|>' is not marked as EOG
  load: control token: 128080 '<|reserved_special_token_72|>' is not marked as EOG
  load: control token: 128104 '<|reserved_special_token_96|>' is not marked as EOG
  load: control token: 128082 '<|reserved_special_token_74|>' is not marked as EOG
  load: control token: 128219 '<|reserved_special_token_211|>' is not marked as EOG
  load: control token: 128017 '<|reserved_special_token_9|>' is not marked as EOG
  load: control token: 128050 '<|reserved_special_token_42|>' is not marked as EOG
  load: control token: 128205 '<|reserved_special_token_197|>' is not marked as EOG
  load: control token: 128047 '<|reserved_special_token_39|>' is not marked as EOG
  load: control token: 128164 '<|reserved_special_token_156|>' is not marked as EOG
  load: control token: 128020 '<|reserved_special_token_12|>' is not marked as EOG
  load: control token: 128069 '<|reserved_special_token_61|>' is not marked as EOG
  load: control token: 128245 '<|reserved_special_token_237|>' is not marked as EOG
  load: control token: 128121 '<|reserved_special_token_113|>' is not marked as EOG
  load: control token: 128079 '<|reserved_special_token_71|>' is not marked as EOG
  load: control token: 128037 '<|reserved_special_token_29|>' is not marked as EOG
  load: control token: 128244 '<|reserved_special_token_236|>' is not marked as EOG
  load: control token: 128029 '<|reserved_special_token_21|>' is not marked as EOG
  load: control token: 128221 '<|reserved_special_token_213|>' is not marked as EOG
  load: control token: 128066 '<|reserved_special_token_58|>' is not marked as EOG
  load: control token: 128120 '<|reserved_special_token_112|>' is not marked as EOG
  load: control token: 128014 '<|reserved_special_token_6|>' is not marked as EOG
  load: control token: 128025 '<|reserved_special_token_17|>' is not marked as EOG
  load: control token: 128126 '<|reserved_special_token_118|>' is not marked as EOG
  load: printing all EOG tokens:
  load:   - 128001 ('<|end_of_text|>')
  load:   - 128008 ('<|eom_id|>')
  load:   - 128009 ('<|eot_id|>')
  load: special tokens cache size = 256
  load: token to piece cache size = 0.7999 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 131072
  print_info: n_embd           = 2048
  print_info: n_layer          = 16
  print_info: n_head           = 32
  print_info: n_head_kv        = 8
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 4
  print_info: n_embd_k_gqa     = 512
  print_info: n_embd_v_gqa     = 512
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 8192
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 500000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 131072
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.24 B
  print_info: general.name     = Llama 3.2 1B Instruct
  print_info: vocab type       = BPE
  print_info: n_vocab          = 128256
  print_info: n_merges         = 280147
  print_info: BOS token        = 128000 '<|begin_of_text|>'
  print_info: EOS token        = 128009 '<|eot_id|>'
  print_info: EOT token        = 128009 '<|eot_id|>'
  print_info: EOM token        = 128008 '<|eom_id|>'
  print_info: LF token         = 198 'Ċ'
  print_info: EOG token        = 128001 '<|end_of_text|>'
  print_info: EOG token        = 128008 '<|eom_id|>'
  print_info: EOG token        = 128009 '<|eot_id|>'
  print_info: max token length = 256
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   762.81 MiB
  .............................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 2048
  llama_context: n_ctx_per_seq = 2048
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 500000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (2048) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.49 MiB
  create_memory: n_ctx = 2048 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    64.00 MiB
  llama_kv_cache: size =   64.00 MiB (  2048 cells,  16 layers,  1/1 seqs), K (f16):   32.00 MiB, V (f16):   32.00 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1176
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =   254.50 MiB
  llama_context: graph nodes  = 566
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 128
  llama_context: n_ctx_per_seq = 128
  llama_context: n_batch       = 128
  llama_context: n_ubatch      = 128
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (128) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 128 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =     2.75 MiB
  llama_kv_cache: size =    2.75 MiB (   128 cells,  22 layers,  1/1 seqs), K (f16):    1.38 MiB, V (f16):    1.38 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 128, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  128, n_seqs =  1, n_outputs =  128
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  128, n_seqs =  1, n_outputs =  128
  llama_context:        CPU compute buffer size =    16.63 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 128
  llama_context: n_ctx_per_seq = 128
  llama_context: n_batch       = 128
  llama_context: n_ubatch      = 128
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (128) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 128 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =     2.75 MiB
  llama_kv_cache: size =    2.75 MiB (   128 cells,  22 layers,  1/1 seqs), K (f16):    1.38 MiB, V (f16):    1.38 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 128, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  128, n_seqs =  1, n_outputs =  128
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  128, n_seqs =  1, n_outputs =  128
  llama_context:        CPU compute buffer size =    16.63 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 128
  llama_context: n_ctx_per_seq = 128
  llama_context: n_batch       = 128
  llama_context: n_ubatch      = 128
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (128) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 128 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =     2.75 MiB
  llama_kv_cache: size =    2.75 MiB (   128 cells,  22 layers,  1/1 seqs), K (f16):    1.38 MiB, V (f16):    1.38 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 128, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  128, n_seqs =  1, n_outputs =  128
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  128, n_seqs =  1, n_outputs =  128
  llama_context:        CPU compute buffer size =    16.63 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_batch is less than GGML_KQ_MASK_PAD - increasing to 64
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 16
  llama_context: n_ctx_per_seq = 16
  llama_context: n_batch       = 64
  llama_context: n_ubatch      = 64
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (16) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 32 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =     0.69 MiB
  llama_kv_cache: size =    0.69 MiB (    32 cells,  22 layers,  1/1 seqs), K (f16):    0.34 MiB, V (f16):    0.34 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 32, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =   32, n_seqs =  1, n_outputs =   32
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =   32, n_seqs =  1, n_outputs =   32
  llama_context:        CPU compute buffer size =     4.16 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 8192
  llama_context: n_ctx_per_seq = 8192
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (8192) > n_ctx_train (2048) -- possible training context overflow
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 8192 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =   176.00 MiB
  llama_kv_cache: size =  176.00 MiB (  8192 cells,  22 layers,  1/1 seqs), K (f16):   88.00 MiB, V (f16):   88.00 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =   545.01 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 64
  llama_context: n_ctx_per_seq = 64
  llama_context: n_batch       = 64
  llama_context: n_ubatch      = 64
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (64) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 64 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =     1.38 MiB
  llama_kv_cache: size =    1.38 MiB (    64 cells,  22 layers,  1/1 seqs), K (f16):    0.69 MiB, V (f16):    0.69 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 64, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  llama_context:        CPU compute buffer size =     8.31 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 64
  llama_context: n_ctx_per_seq = 64
  llama_context: n_batch       = 64
  llama_context: n_ubatch      = 64
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (64) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 64 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =     1.38 MiB
  llama_kv_cache: size =    1.38 MiB (    64 cells,  22 layers,  1/1 seqs), K (f16):    0.69 MiB, V (f16):    0.69 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 64, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  llama_context:        CPU compute buffer size =     8.31 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 64
  llama_context: n_ctx_per_seq = 64
  llama_context: n_batch       = 64
  llama_context: n_ubatch      = 64
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (64) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 64 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =     1.38 MiB
  llama_kv_cache: size =    1.38 MiB (    64 cells,  22 layers,  1/1 seqs), K (f16):    0.69 MiB, V (f16):    0.69 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 64, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  llama_context:        CPU compute buffer size =     8.31 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 64
  llama_context: n_ctx_per_seq = 64
  llama_context: n_batch       = 64
  llama_context: n_ubatch      = 64
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (64) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 64 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =     1.38 MiB
  llama_kv_cache: size =    1.38 MiB (    64 cells,  22 layers,  1/1 seqs), K (f16):    0.69 MiB, V (f16):    0.69 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 64, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  llama_context:        CPU compute buffer size =     8.31 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 64
  llama_context: n_ctx_per_seq = 64
  llama_context: n_batch       = 64
  llama_context: n_ubatch      = 64
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (64) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 64 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =     1.38 MiB
  llama_kv_cache: size =    1.38 MiB (    64 cells,  22 layers,  1/1 seqs), K (f16):    0.69 MiB, V (f16):    0.69 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 64, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  llama_context:        CPU compute buffer size =     8.31 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 64
  llama_context: n_ctx_per_seq = 64
  llama_context: n_batch       = 64
  llama_context: n_ubatch      = 64
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (64) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 64 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =     1.38 MiB
  llama_kv_cache: size =    1.38 MiB (    64 cells,  22 layers,  1/1 seqs), K (f16):    0.69 MiB, V (f16):    0.69 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 64, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  llama_context:        CPU compute buffer size =     8.31 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 64
  llama_context: n_ctx_per_seq = 64
  llama_context: n_batch       = 64
  llama_context: n_ubatch      = 64
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (64) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 64 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =     1.38 MiB
  llama_kv_cache: size =    1.38 MiB (    64 cells,  22 layers,  1/1 seqs), K (f16):    0.69 MiB, V (f16):    0.69 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 64, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  llama_context:        CPU compute buffer size =     8.31 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 64
  llama_context: n_ctx_per_seq = 64
  llama_context: n_batch       = 64
  llama_context: n_ubatch      = 64
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (64) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 64 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =     1.38 MiB
  llama_kv_cache: size =    1.38 MiB (    64 cells,  22 layers,  1/1 seqs), K (f16):    0.69 MiB, V (f16):    0.69 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 64, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  llama_context:        CPU compute buffer size =     8.31 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 64
  llama_context: n_ctx_per_seq = 64
  llama_context: n_batch       = 64
  llama_context: n_ubatch      = 64
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (64) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 64 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =     1.38 MiB
  llama_kv_cache: size =    1.38 MiB (    64 cells,  22 layers,  1/1 seqs), K (f16):    0.69 MiB, V (f16):    0.69 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 64, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  llama_context:        CPU compute buffer size =     8.31 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 64
  llama_context: n_ctx_per_seq = 64
  llama_context: n_batch       = 64
  llama_context: n_ubatch      = 64
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (64) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 64 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =     1.38 MiB
  llama_kv_cache: size =    1.38 MiB (    64 cells,  22 layers,  1/1 seqs), K (f16):    0.69 MiB, V (f16):    0.69 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 64, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =   64, n_seqs =  1, n_outputs =   64
  llama_context:        CPU compute buffer size =     8.31 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  Model file not found: C:\nonexistent\model.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: \\server\share\model.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: ./nonexistent.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  Model file not found: ../nonexistent.gguf 
  Try these options:
     1. Download a model: edge_download_model('TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF', 'tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf')
     2. Quick setup: edge_quick_setup('TinyLlama-1.1B')
     3. List models: edge_list_models()
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 256
  llama_context: n_ctx_per_seq = 256
  llama_context: n_batch       = 256
  llama_context: n_ubatch      = 256
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (256) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 256 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =     5.50 MiB
  llama_kv_cache: size =    5.50 MiB (   256 cells,  22 layers,  1/1 seqs), K (f16):    2.75 MiB, V (f16):    2.75 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 256, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  256, n_seqs =  1, n_outputs =  256
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  256, n_seqs =  1, n_outputs =  256
  llama_context:        CPU compute buffer size =    33.25 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 512
  llama_context: n_ctx_per_seq = 512
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (512) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 512 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    11.00 MiB
  llama_kv_cache: size =   11.00 MiB (   512 cells,  22 layers,  1/1 seqs), K (f16):    5.50 MiB, V (f16):    5.50 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =    66.50 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 512
  llama_context: n_ctx_per_seq = 512
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (512) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 512 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    11.00 MiB
  llama_kv_cache: size =   11.00 MiB (   512 cells,  22 layers,  1/1 seqs), K (f16):    5.50 MiB, V (f16):    5.50 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =    66.50 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 512
  llama_context: n_ctx_per_seq = 512
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (512) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 512 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    11.00 MiB
  llama_kv_cache: size =   11.00 MiB (   512 cells,  22 layers,  1/1 seqs), K (f16):    5.50 MiB, V (f16):    5.50 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =    66.50 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 512
  llama_context: n_ctx_per_seq = 512
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (512) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 512 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    11.00 MiB
  llama_kv_cache: size =   11.00 MiB (   512 cells,  22 layers,  1/1 seqs), K (f16):    5.50 MiB, V (f16):    5.50 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =    66.50 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 512
  llama_context: n_ctx_per_seq = 512
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (512) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 512 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    11.00 MiB
  llama_kv_cache: size =   11.00 MiB (   512 cells,  22 layers,  1/1 seqs), K (f16):    5.50 MiB, V (f16):    5.50 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =    66.50 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 512
  llama_context: n_ctx_per_seq = 512
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (512) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 512 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    11.00 MiB
  llama_kv_cache: size =   11.00 MiB (   512 cells,  22 layers,  1/1 seqs), K (f16):    5.50 MiB, V (f16):    5.50 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =    66.50 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  decode: failed to find a memory slot for batch of size 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 512
  llama_context: n_ctx_per_seq = 512
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (512) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 512 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    11.00 MiB
  llama_kv_cache: size =   11.00 MiB (   512 cells,  22 layers,  1/1 seqs), K (f16):    5.50 MiB, V (f16):    5.50 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =    66.50 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 512
  llama_context: n_ctx_per_seq = 512
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (512) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 512 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    11.00 MiB
  llama_kv_cache: size =   11.00 MiB (   512 cells,  22 layers,  1/1 seqs), K (f16):    5.50 MiB, V (f16):    5.50 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =    66.50 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 512
  llama_context: n_ctx_per_seq = 512
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (512) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 512 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    11.00 MiB
  llama_kv_cache: size =   11.00 MiB (   512 cells,  22 layers,  1/1 seqs), K (f16):    5.50 MiB, V (f16):    5.50 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =    66.50 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 512
  llama_context: n_ctx_per_seq = 512
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (512) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 512 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    11.00 MiB
  llama_kv_cache: size =   11.00 MiB (   512 cells,  22 layers,  1/1 seqs), K (f16):    5.50 MiB, V (f16):    5.50 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =    66.50 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 512
  llama_context: n_ctx_per_seq = 512
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (512) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 512 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    11.00 MiB
  llama_kv_cache: size =   11.00 MiB (   512 cells,  22 layers,  1/1 seqs), K (f16):    5.50 MiB, V (f16):    5.50 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =    66.50 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 512
  llama_context: n_ctx_per_seq = 512
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (512) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 512 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    11.00 MiB
  llama_kv_cache: size =   11.00 MiB (   512 cells,  22 layers,  1/1 seqs), K (f16):    5.50 MiB, V (f16):    5.50 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =    66.50 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  decode: failed to find a memory slot for batch of size 3
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 512
  llama_context: n_ctx_per_seq = 512
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (512) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 512 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    11.00 MiB
  llama_kv_cache: size =   11.00 MiB (   512 cells,  22 layers,  1/1 seqs), K (f16):    5.50 MiB, V (f16):    5.50 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =    66.50 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 512
  llama_context: n_ctx_per_seq = 512
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (512) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 512 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    11.00 MiB
  llama_kv_cache: size =   11.00 MiB (   512 cells,  22 layers,  1/1 seqs), K (f16):    5.50 MiB, V (f16):    5.50 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =    66.50 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 256
  llama_context: n_ctx_per_seq = 256
  llama_context: n_batch       = 256
  llama_context: n_ubatch      = 256
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (256) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 256 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =     5.50 MiB
  llama_kv_cache: size =    5.50 MiB (   256 cells,  22 layers,  1/1 seqs), K (f16):    2.75 MiB, V (f16):    2.75 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 256, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  256, n_seqs =  1, n_outputs =  256
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  256, n_seqs =  1, n_outputs =  256
  llama_context:        CPU compute buffer size =    33.25 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from C:\Users\CRAN\Documents\.cache\edgemodelr\tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))
  llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  llama_model_loader: - kv   0:                       general.architecture str              = llama
  llama_model_loader: - kv   1:                               general.name str              = tinyllama_tinyllama-1.1b-chat-v1.0
  llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
  llama_model_loader: - kv   3:                     llama.embedding_length u32              = 2048
  llama_model_loader: - kv   4:                          llama.block_count u32              = 22
  llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 5632
  llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 64
  llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
  llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 4
  llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
  llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000
  llama_model_loader: - kv  11:                          general.file_type u32              = 15
  llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
  llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
  llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
  llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
  llama_model_loader: - kv  16:                      tokenizer.ggml.merges arr[str,61249]   = ["▁ t", "e r", "i n", "▁ a", "e n...
  llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
  llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
  llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 0
  llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 2
  llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}\n{% if m...
  llama_model_loader: - kv  22:               general.quantization_version u32              = 2
  llama_model_loader: - type  f32:   45 tensors
  llama_model_loader: - type q4_K:  135 tensors
  llama_model_loader: - type q6_K:   21 tensors
  print_info: file format = GGUF V3 (latest)
  print_info: file type   = Q4_K - Medium
  print_info: file size   = 636.18 MiB (4.85 BPW) 
  init_tokenizer: initializing tokenizer for type 1
  load: control token:      2 '</s>' is not marked as EOG
  load: control token:      1 '<s>' is not marked as EOG
  load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  load: printing all EOG tokens:
  load:   - 2 ('</s>')
  load: special tokens cache size = 3
  load: token to piece cache size = 0.1684 MB
  print_info: arch             = llama
  print_info: vocab_only       = 0
  print_info: n_ctx_train      = 2048
  print_info: n_embd           = 2048
  print_info: n_layer          = 22
  print_info: n_head           = 32
  print_info: n_head_kv        = 4
  print_info: n_rot            = 64
  print_info: n_swa            = 0
  print_info: is_swa_any       = 0
  print_info: n_embd_head_k    = 64
  print_info: n_embd_head_v    = 64
  print_info: n_gqa            = 8
  print_info: n_embd_k_gqa     = 256
  print_info: n_embd_v_gqa     = 256
  print_info: f_norm_eps       = 0.0e+00
  print_info: f_norm_rms_eps   = 1.0e-05
  print_info: f_clamp_kqv      = 0.0e+00
  print_info: f_max_alibi_bias = 0.0e+00
  print_info: f_logit_scale    = 0.0e+00
  print_info: f_attn_scale     = 0.0e+00
  print_info: n_ff             = 5632
  print_info: n_expert         = 0
  print_info: n_expert_used    = 0
  print_info: causal attn      = 1
  print_info: pooling type     = 0
  print_info: rope type        = 0
  print_info: rope scaling     = linear
  print_info: freq_base_train  = 10000.0
  print_info: freq_scale_train = 1
  print_info: n_ctx_orig_yarn  = 2048
  print_info: rope_finetuned   = unknown
  print_info: model type       = 1B
  print_info: model params     = 1.10 B
  print_info: general.name     = tinyllama_tinyllama-1.1b-chat-v1.0
  print_info: vocab type       = SPM
  print_info: n_vocab          = 32000
  print_info: n_merges         = 0
  print_info: BOS token        = 1 '<s>'
  print_info: EOS token        = 2 '</s>'
  print_info: UNK token        = 0 '<unk>'
  print_info: PAD token        = 2 '</s>'
  print_info: LF token         = 13 '<0x0A>'
  print_info: EOG token        = 2 '</s>'
  print_info: max token length = 48
  load_tensors: loading model tensors, this can take a while... (mmap = true)
  load_tensors: layer   0 assigned to device CPU, is_swa = 0
  load_tensors: layer   1 assigned to device CPU, is_swa = 0
  load_tensors: layer   2 assigned to device CPU, is_swa = 0
  load_tensors: layer   3 assigned to device CPU, is_swa = 0
  load_tensors: layer   4 assigned to device CPU, is_swa = 0
  load_tensors: layer   5 assigned to device CPU, is_swa = 0
  load_tensors: layer   6 assigned to device CPU, is_swa = 0
  load_tensors: layer   7 assigned to device CPU, is_swa = 0
  load_tensors: layer   8 assigned to device CPU, is_swa = 0
  load_tensors: layer   9 assigned to device CPU, is_swa = 0
  load_tensors: layer  10 assigned to device CPU, is_swa = 0
  load_tensors: layer  11 assigned to device CPU, is_swa = 0
  load_tensors: layer  12 assigned to device CPU, is_swa = 0
  load_tensors: layer  13 assigned to device CPU, is_swa = 0
  load_tensors: layer  14 assigned to device CPU, is_swa = 0
  load_tensors: layer  15 assigned to device CPU, is_swa = 0
  load_tensors: layer  16 assigned to device CPU, is_swa = 0
  load_tensors: layer  17 assigned to device CPU, is_swa = 0
  load_tensors: layer  18 assigned to device CPU, is_swa = 0
  load_tensors: layer  19 assigned to device CPU, is_swa = 0
  load_tensors: layer  20 assigned to device CPU, is_swa = 0
  load_tensors: layer  21 assigned to device CPU, is_swa = 0
  load_tensors: layer  22 assigned to device CPU, is_swa = 0
  skipping PrefetchVirtualMemory because _WIN32_WINNT < 0x602
  load_tensors:   CPU_Mapped model buffer size =   636.18 MiB
  ....................................................................................
  llama_context: constructing llama_context
  llama_context: n_seq_max     = 1
  llama_context: n_ctx         = 512
  llama_context: n_ctx_per_seq = 512
  llama_context: n_batch       = 512
  llama_context: n_ubatch      = 512
  llama_context: causal_attn   = 1
  llama_context: flash_attn    = 0
  llama_context: kv_unified    = false
  llama_context: freq_base     = 10000.0
  llama_context: freq_scale    = 1
  llama_context: n_ctx_per_seq (512) < n_ctx_train (2048) -- the full capacity of the model will not be utilized
  set_abort_callback: call
  llama_context:        CPU  output buffer size =     0.12 MiB
  create_memory: n_ctx = 512 (padded)
  llama_kv_cache: layer   0: dev = CPU
  llama_kv_cache: layer   1: dev = CPU
  llama_kv_cache: layer   2: dev = CPU
  llama_kv_cache: layer   3: dev = CPU
  llama_kv_cache: layer   4: dev = CPU
  llama_kv_cache: layer   5: dev = CPU
  llama_kv_cache: layer   6: dev = CPU
  llama_kv_cache: layer   7: dev = CPU
  llama_kv_cache: layer   8: dev = CPU
  llama_kv_cache: layer   9: dev = CPU
  llama_kv_cache: layer  10: dev = CPU
  llama_kv_cache: layer  11: dev = CPU
  llama_kv_cache: layer  12: dev = CPU
  llama_kv_cache: layer  13: dev = CPU
  llama_kv_cache: layer  14: dev = CPU
  llama_kv_cache: layer  15: dev = CPU
  llama_kv_cache: layer  16: dev = CPU
  llama_kv_cache: layer  17: dev = CPU
  llama_kv_cache: layer  18: dev = CPU
  llama_kv_cache: layer  19: dev = CPU
  llama_kv_cache: layer  20: dev = CPU
  llama_kv_cache: layer  21: dev = CPU
  llama_kv_cache:        CPU KV buffer size =    11.00 MiB
  llama_kv_cache: size =   11.00 MiB (   512 cells,  22 layers,  1/1 seqs), K (f16):    5.50 MiB, V (f16):    5.50 MiB
  llama_context: enumerating backends
  llama_context: backend_ptrs.size() = 1
  llama_context: max_nodes = 1608
  llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
  graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
  llama_context:        CPU compute buffer size =    66.50 MiB
  llama_context: graph nodes  = 776
  llama_context: graph splits = 1
  [ FAIL 4 | WARN 8 | SKIP 20 | PASS 456 ]
  
  ══ Skipped tests (20) ══════════════════════════════════════════════════════════
  • No test model available for concurrency tests (1):
    'test-error-handling.R:230:7'
  • No test model available for concurrent operations tests (1):
    'test-integration.R:381:7'
  • No test model available for edge case tests (1):
    'test-error-handling.R:200:7'
  • No test model available for end-to-end scenario tests (1):
    'test-integration.R:305:7'
  • No test model available for exception safety tests (1):
    'test-error-handling.R:398:7'
  • No test model available for integration tests (1): 'test-integration.R:51:7'
  • No test model available for memory constraint tests (1):
    'test-error-handling.R:130:7'
  • No test model available for memory management tests (1):
    'test-model-management.R:127:7'
  • No test model available for performance tests (1): 'test-integration.R:209:7'
  • No test model available for real model loading tests (1):
    'test-model-loading.R:224:5'
  • No test model available for streaming tests (1): 'test-streaming.R:321:5'
  • No test model available for stress tests (1): 'test-integration.R:160:7'
  • empty test (8): 'test-error-handling.R:1:1', 'test-integration.R:1:1',
    'test-model-management.R:1:1', 'test-model-management.R:283:1',
    'test-model-management.R:336:1', 'test-streaming.R:1:1',
    'test-streaming.R:326:1', 'test-text-completion.R:1:1'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Failure ('test-text-completion.R:121:7'): edge_completion parameter validation ──
  `edge_completion(ctx, "Hello", n_predict = "invalid")` did not throw the expected error.
  ── Failure ('test-text-completion.R:129:7'): edge_completion parameter validation ──
  `edge_completion(ctx, "Hello", temperature = "invalid")` did not throw the expected error.
  ── Failure ('test-text-completion.R:141:7'): edge_completion parameter validation ──
  `edge_completion(ctx, "Hello", top_p = "invalid")` did not throw the expected error.
  ── Error ('test-text-completion.R:284:9'): edge_completion stress tests ────────
  <Rcpp::exception/C++Error/error/condition>
  Error: Error during completion: Failed to process prompt
  Backtrace:
      ▆
   1. └─edgemodelr::edge_completion(ctx, "Quick test", n_predict = 3) at test-text-completion.R:284:9
  
  [ FAIL 4 | WARN 8 | SKIP 20 | PASS 456 ]
  Error: Test failures
  Execution halted
* checking PDF version of manual ... [20s] OK
* checking HTML version of manual ... OK
* DONE
Status: 1 ERROR, 1 WARNING, 1 NOTE