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# llama.cpp/examples/imatrix | ||
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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 | ||
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## Usage | ||
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``` | ||
./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] | ||
``` | ||
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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. | ||
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For faster computation, make sure to use GPU offloading via the `-ngl` argument | ||
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## Example | ||
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```bash | ||
LLAMA_CUBLAS=1 make -j | ||
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# generate importance matrix (imatrix.dat) | ||
./imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99 | ||
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# 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 | ||
``` |