metadata
thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
license: llama3
datasets:
- mc4
- wikipedia
- EleutherAI/pile
- oscar-corpus/colossal-oscar-1.0
- cc100
language:
- ja
- en
tags:
- llama
- llama-3
- gptq
inference: false
Llama 3 Youko 70B GPTQ (rinna/llama-3-youko-70b-gptq)
Overview
rinna/llama-3-youko-70b-gptq is the quantized model for rinna/llama-3-youko-70b using AutoGPTQ. The quantized version is 4x smaller than the original model and thus requires less memory and provides faster inference.
| Size | Continual Pre-Training | Instruction-Tuning |
|---|---|---|
| 8B | Llama 3 Youko 8B [HF] [GPTQ] | Llama 3 Youko 8B Instruct [HF] [GPTQ] |
| 70B | Llama 3 Youko 70B [HF] [GPTQ] | Llama 3 Youko 70B Instruct [HF] [GPTQ] |
Training: Built with Meta Llama 3
See rinna/llama-3-youko-70b for details about model architecture and data.
Contributors
Benchmarking
Please refer to rinna's LM benchmark page.
How to use the model
import transformers
import torch
model_id = "rinna/llama-3-youko-70b-gptq"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
device_map="auto"
)
output = pipeline(
"西田幾多郎は、",
max_new_tokens=256,
do_sample=True
)
print(output)
Tokenization
The model uses the original meta-llama/Meta-Llama-3-70B tokenizer.
How to cite
@misc{rinna-llama-3-youko-70b-gptq,
title = {rinna/llama-3-youko-70b-gptq},
author = {Wakatsuki, Toshiaki and Mitsuda, Koh and Chen, Xinqi and Sawada, Kei},
url = {https://huggingface.co./rinna/llama-3-youko-70b-gptq}
}
@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}}
}
References
@article{llama3modelcard,
title = {Llama 3 Model Card},
author = {AI@Meta},
year = {2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
@article{frantar2022gptq,
title = {{GPTQ}: Accurate Post-training Compression for Generative Pretrained Transformers},
author = {Frantar, Elias and Ashkboos, Saleh and Hoefler, Torsten and Alistarh, Dan},
year = {2022},
url = {https://arxiv.org/abs/2210.17323}
}