metadata
thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
language:
- ja
- en
tags:
- qwen
inference: false
license: other
license_name: tongyi-qianwen-license-agreement
license_link: >-
https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT
pipeline_tag: text-generation
base_model: rinna/nekomata-14b
base_model_relation: quantized
rinna/nekomata-14b-gguf
Overview
The model is the GGUF version of rinna/nekomata-14b. It can be used with llama.cpp for lightweight inference.
Quantization of this model may cause stability issue in GPTQ, AWQ and GGUF q4_0. We recommend GGUF q4_K_M for 4-bit quantization.
See rinna/nekomata-14b for details about model architecture and data.
Contributors
How to use the model
See llama.cpp for more usage details.
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make
MODEL_PATH=/path/to/nekomata-14b-gguf/nekomata-14b.Q4_K_M.gguf
MAX_N_TOKENS=128
PROMPT="西田幾多郎は、"
./main -m ${MODEL_PATH} -n ${MAX_N_TOKENS} -p "${PROMPT}"
Tokenization
Please refer to rinna/nekomata-14b for tokenization details.
How to cite
@misc{rinna-nekomata-14b-gguf,
title = {rinna/nekomata-14b-gguf},
author = {Wakatsuki, Toshiaki and Zhao, Tianyu and Sawada, Kei},
url = {https://huggingface.co./rinna/nekomata-14b-gguf}
}
@inproceedings{sawada2024release,
title = {Release of Pre-Trained Models for the {J}apanese Language},
author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
month = {5},
year = {2024},
pages = {13898--13905},
url = {https://aclanthology.org/2024.lrec-main.1213},
note = {\url{https://arxiv.org/abs/2404.01657}}
}