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* llama : refactor llama_context, llama_kv_cache, llm_build_context ggml-ci * graph : don't mutate the KV cache during defrag ggml-ci * context : reduce virtuals + remove test function ggml-ci * context : move interface implementation to source file + factory ggml-ci * graph : move KV cache build functions to llama_context impl ggml-ci * graph : remove model reference from build_pooling ggml-ci * graph : remove llama_model reference ggml-ci * kv_cache : provide rope factors ggml-ci * graph : rework inputs to use only unique_ptr, remove attn input abstraction ggml-ci * context : remove llama_context_i abstraction ggml-ci * context : clean-up ggml-ci * graph : clean-up ggml-ci * llama : remove redundant keywords (struct, enum) ggml-ci * model : adapt gemma3 ggml-ci * graph : restore same attention ops as on master ggml-ci * llama : remove TODO + fix indent ggml-ci
llama.cpp/examples/imatrix
Compute an importance matrix for a model and given text dataset. Can be used during quantization to enhance the quality of the quantized models. More information is available here: https://github.com/ggml-org/llama.cpp/pull/4861
Usage
./llama-imatrix \
-m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \
[--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \
[--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]
Here -m with a model name and -f with a file containing training data (such as e.g. wiki.train.raw) are mandatory.
The parameters in square brackets are optional and have the following meaning:
-o(or--output-file) specifies the name of the file where the computed data will be stored. If missingimatrix.datis used.--verbosityspecifies the verbosity level. If set to0, no output other than the perplexity of the processed chunks will be generated. If set to1, each time the results are saved a message is written tostderr. If>=2, a message is output each time data is collected for any tensor. Default verbosity level is1.--output-frequencyspecifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks)--save-frequencyspecifies how often to save a copy of the imatrix in a separate file. Default is 0 (i.e., never)--process-outputspecifies if data will be collected for theoutput.weighttensor. My experience is that it is better to not utilize the importance matrix when quantizingoutput.weight, so this is set tofalseby default.
For faster computation, make sure to use GPU offloading via the -ngl argument
Example
# generate importance matrix (imatrix.dat)
./llama-imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99
# use the imatrix to perform a Q4_K_M quantization
./llama-quantize --imatrix imatrix.dat ggml-model-f16.gguf ./ggml-model-q4_k_m.gguf q4_k_m