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--- |
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language: |
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- en |
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--- |
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# Input files for generating the Importance Matrix |
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## How to quantize with an imatrix in llama.cpp |
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1. Get one of the input files collected here, or eleswhere. |
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2. Convert or download the model you want to quantise, in fp16 GGUF format. |
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3. Generate an imatrix file specific to the model you want to quantise |
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``` |
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cd <llama.cpp directory> |
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./imatrix -m <model_path>/ggml-model-f16.gguf -f <matrix_training_path>/<plain_text_matrix_file> -o <output_binary_file.matrix> -t 12 -ngl 144 --chunks 100 -b 512 -c 512 |
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# -ngl : layers offloaded to gpu (recommended to use number of layers the model contains) |
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# -t 12 : number of threads (should probably match no of cpu) |
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# -c 512 : context size, testing seems to show 512 is recommended (default=512, 0=loaded from model) |
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# -b 200 : batch size (default=512) |
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# --chunks 100 (recommended) |
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# --mlock : keep model in ram (only use if you had sufficient RAM for the whole fp16) |
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``` |
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4. Use the generated binary matrix file to quantise the model |
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``` |
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./quantize <model_path>/ggml-model-f16.gguf -matrix <matrix_file> <output_model_path>/ggml-model-IQ4_XS.gguf IQ4_XS |
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``` |
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Note: normal quantisation also benefits from using a matrix file. It also seem that a larger input data is |
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better for higher quantisation. |
