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rinna/nekomata-14b-gguf
rinna/nekomata-14b. It can be used with llama.cpp for lightweight inference.rinna/nekomata-14b for details about model architecture and data.1git clone https://github.com/ggerganov/llama.cpp
2cd llama.cpp
3make
4
5MODEL_PATH=/path/to/nekomata-14b-gguf/nekomata-14b.Q4_K_M.gguf
6MAX_N_TOKENS=128
7PROMPT="西田幾多郎は、"
8
9./main -m ${MODEL_PATH} -n ${MAX_N_TOKENS} -p "${PROMPT}"rinna/nekomata-14b for tokenization details.1@misc{rinna-nekomata-14b-gguf,
2 title = {rinna/nekomata-14b-gguf},
3 author = {Wakatsuki, Toshiaki and Zhao, Tianyu and Sawada, Kei},
4 url = {https://huggingface.co/rinna/nekomata-14b-gguf}
5}
6
7@inproceedings{sawada2024release,
8 title = {Release of Pre-Trained Models for the {J}apanese Language},
9 author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
10 booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
11 month = {5},
12 year = {2024},
13 pages = {13898--13905},
14 url = {https://aclanthology.org/2024.lrec-main.1213},
15 note = {\url{https://arxiv.org/abs/2404.01657}}
16}