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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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## Which file to use for generating the importance matrix |
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Not all importance matrices are equal. The best results are obtained when using a source file similar to the |
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training data. Size also matters: the bigger the model (eg: 70b vs 13b) and the higher the quant (eg: q6k_ vs iq3_xs), |
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the bigger the source file needs to be to make an impact. Multiple input files can be combined if needed; |
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for example: |
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``` |
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cat technical.txt multilingual.txt wiki.txt >custom.matrix |
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``` |
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You will find below descriptions for the various input files provided, to help you choose the correct one. |
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## Community provided files |
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**8k_random_data**\ |
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**20k_random_data**\ |
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**groups_merged**\ |
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**group_10_merged**\ |
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**ptb.train**\ |
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Penn Treebank (PTB) is a widely used preprocessed large dataset designed for language training. Casing, |
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punctuation and numbers have been removed from the training data. Recently it has kind of been superseeded |
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by WikiText which does not have these removals, features a larger vocabulary and full articles (better |
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suited for models that can take advantage of long term dependencies). However, for importantce matrix training, |
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PTB is still a valid dataset, which has the advantage of being manually curated, and similar to WikiText, |
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without being WikiText; this can help against bias. |
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**WikiText**\ |
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The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of |
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verified Good and Featured articles on Wikipedia. Compared to PTB, WikiText-2 is over 2 times larger and |
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WikiText-103 is over 110 times larger. As it is composed of full articles, the dataset is well suited for models |
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that can take advantage of long term dependencies.\ |
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https://huggingface.co/datasets/wikitext |
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**WikiText_FR**\ |
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70 million tokens extracted from the set of french Wikipedia articles that are classified as "quality articles" |
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or "good articles".\ |
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https://huggingface.co/datasets/asi/wikitext_fr |
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## exllamav2 calibration data |
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https://github.com/turboderp/exllamav2/tree/master/conversion/standard_cal_data |
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**c4**\ |
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**code**\ |
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Programming |
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**multilingual**\ |
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English, Arabic, Chinese, French, German, Japanese, Polish, Russian, Spanish, Swedish, Turkish, Hebrew, |
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Macedonian, Norwegian, Lithuanian, Greek, Italian, Afrikaans, Dutch, Danish. |
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**technical**\ |
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Technical writing. |
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**tiny**\ |
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Very short stories. |
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**wiki**\ |
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Wikipedia dump. |
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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. |
