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 |
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.. | ||
CMakeLists.txt | ||
convert-llama2c-to-ggml.cpp | ||
README.md |
Convert llama2.c model to ggml
This example reads weights from project llama2.c and saves them in ggml compatible format. The vocab that is available in models/ggml-vocab.bin
is used by default.
To convert the model first download the models from the llma2.c repository:
$ make -j
After successful compilation, following usage options are available:
usage: ./convert-llama2c-to-ggml [options]
options:
-h, --help show this help message and exit
--copy-vocab-from-model FNAME model path from which to copy vocab (default 'tokenizer.bin')
--llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model
--llama2c-output-model FNAME model path to save the converted llama2.c model (default ak_llama_model.bin')
An example command using a model from karpathy/tinyllamas is as follows:
$ ./convert-llama2c-to-ggml --copy-vocab-from-model ../llama2.c/tokenizer.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.ggmlv3.bin
For now the generated model is in the legacy GGJTv3 format, so you need to convert it to gguf manually:
$ python ./convert-llama-ggmlv3-to-gguf.py --eps 1e-5 --input stories42M.ggmlv3.bin --output stories42M.gguf.bin
Now you can use the model with a command like:
$ ./main -m stories42M.gguf.bin -p "One day, Lily met a Shoggoth" -n 500 -c 256