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llama.cpp/examples/imatrix
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Pierrick Hymbert 0c4d489e29
quantize: add imatrix and dataset metadata in GGUF (#6658) 
* imatrix: save the dataset file used in the output file

* llama: support kv overrides type string string

* common: factorize KV Overrides parsing between common and server

* quantize: add imatrix n entries and dataset KV metadata
quantize: factorize KV Overrides parsing between common
#6656

* llama: remove kv override str_value initialization as it does not compile on some toolchain

* quantize: add imatrix m_last_call as `quantize.imatrix.chunks_count`

* quantize: add imatrix filename in KV

* llama: add llama_model_kv_override_free

* common: add llama_model_kv_override_free
common: free kv override if used after model loading

* llama: finally move the string KV override value to the stack

* llama : minor

* no need to add a NUL to the std::vector, std::string can be initialized from a pair of iterators.

Co-authored-by: slaren <slarengh@gmail.com>

* kv override: ensure string termination

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
2024-04-26 20:06:33 +02:00
..
CMakeLists.txt Importance Matrix calculation (#4861) 2024-01-12 06:59:57 +01:00
imatrix.cpp quantize: add imatrix and dataset metadata in GGUF (#6658) 2024-04-26 20:06:33 +02:00
README.md cuda : rename build flag to LLAMA_CUDA (#6299) 2024-03-26 01:16:01 +01:00

README.md

llama.cpp/examples/imatrix

Compute an importance matrix for a model and given text dataset. Can be used during quantization to enchance the quality of the quantum models. More information is available here: https://github.com/ggerganov/llama.cpp/pull/4861

Usage

./imatrix -m <some_fp_model> -f <some_training_data> [-o <output_file>] [--verbosity <verbosity_level>]
        [-ofreq num_chunks] [-ow <0 or 1>] [other common params]

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 missing imatrix.dat is used.
  • --verbosity specifies the verbosity level. If set to 0, no output other than the perplexity of the processed chunks will be generated. If set to 1, each time the results are saved a message is written to stderr. If >=2, a message is output each time data is collected for any tensor. Default verbosity level is 1.
  • -ofreq (or --output-frequency) specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks)
  • -ow (or --output-weight) specifies if data will be collected for the output.weight tensor. My experience is that it is better to not utilize the importance matrix when quantizing output.weight, so this is set to false by default.

For faster computation, make sure to use GPU offloading via the -ngl argument

Example

LLAMA_CUDA=1 make -j

# generate importance matrix (imatrix.dat)
./imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99

# use the imatrix to perform a Q4_K_M quantization
./quantize --imatrix imatrix.dat ggml-model-f16.gguf ./ggml-model-q4_k_m.gguf q4_k_m