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33 lines
1.8 KiB
Markdown
33 lines
1.8 KiB
Markdown
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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.
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More information is available here: https://github.com/ggerganov/llama.cpp/pull/4861
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## Usage
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```
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./imatrix -m <some_fp_model> -f <some_training_data> [-o <output_file>] [--verbosity <verbosity_level>]
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[-ofreq num_chunks] [-ow <0 or 1>] [other common params]
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```
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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.
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The parameters in square brackets are optional and have the following meaning:
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* `-o` (or `--output-file`) specifies the name of the file where the computed data will be stored. If missing `imatrix.dat` is used.
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* `--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`.
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* `-ofreq` (or `--output-frequency`) specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks)
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* `-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
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LLAMA_CUBLAS=1 make -j
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# generate importance matrix (imatrix.dat)
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./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
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./quantize --imatrix imatrix.dat ggml-model-f16.gguf ./ggml-model-q4_k_m.gguf q4_k_m
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```
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