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95385241a9
* Fix import of llama2.c models that don't share weights between embedding layers * llama2c: reinstate ggmlv3 conversion output + update readme w/ gguf conv * llama2.c: comment out legacy "load from ggml model" logic * llama2.c: convert special-cased "<0xXX>" single byte tokens from tokenizer.bin
31 lines
1.5 KiB
Markdown
31 lines
1.5 KiB
Markdown
## Convert llama2.c model to ggml
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This example reads weights from project [llama2.c](https://github.com/karpathy/llama2.c) and saves them in ggml compatible format. The vocab that is available in `models/ggml-vocab.bin` is used by default.
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To convert the model first download the models from the [llma2.c](https://github.com/karpathy/llama2.c) repository:
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`$ make -j`
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After successful compilation, following usage options are available:
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```
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usage: ./convert-llama2c-to-ggml [options]
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options:
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-h, --help show this help message and exit
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--copy-vocab-from-model FNAME model path from which to copy vocab (default 'tokenizer.bin')
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--llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model
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--llama2c-output-model FNAME model path to save the converted llama2.c model (default ak_llama_model.bin')
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```
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An example command using a model from [karpathy/tinyllamas](https://huggingface.co/karpathy/tinyllamas) is as follows:
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`$ ./convert-llama2c-to-ggml --copy-vocab-from-model ../llama2.c/tokenizer.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.ggmlv3.bin`
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For now the generated model is in the legacy GGJTv3 format, so you need to convert it to gguf manually:
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`$ python ./convert-llama-ggmlv3-to-gguf.py --eps 1e-5 --input stories42M.ggmlv3.bin --output stories42M.gguf.bin`
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Now you can use the model with a command like:
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`$ ./main -m stories42M.gguf.bin -p "One day, Lily met a Shoggoth" -n 500 -c 256`
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