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-7b-instruction
base_model_relation: quantized
rinna/nekomata-7b-instruction-gguf
Overview
The model is the GGUF version of rinna/nekomata-7b-instruction. 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-7b-instruction 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-7b-instruction-gguf/nekomata-7b-instruction.Q4_K_M.gguf
MAX_N_TOKENS=512
PROMPT_INSTRUCTION="次の日本語を英語に翻訳してください。"
PROMPT_INPUT="大規模言語モデル（だいきぼげんごモデル、英: large language model、LLM）は、多数のパラメータ（数千万から数十億）を持つ人工ニューラルネットワークで構成されるコンピュータ言語モデルで、膨大なラベルなしテキストを使用して自己教師あり学習または半教師あり学習によって訓練が行われる。"
PROMPT="以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさい。\n\n### 指示:\n${PROMPT_INSTRUCTION}\n\n### 入力:\n${PROMPT_INPUT}\n\n### 応答:\n"
./main -m ${MODEL_PATH} -n ${MAX_N_TOKENS} -p "${PROMPT}"
Tokenization
Please refer to rinna/nekomata-7b for tokenization details.
How to cite
@misc{rinna-nekomata-7b-instruction-gguf,
title = {rinna/nekomata-7b-instruction-gguf},
author = {Wakatsuki, Toshiaki and Zhao, Tianyu and Sawada, Kei},
url = {https://huggingface.co/rinna/nekomata-7b-instruction-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}}
}