thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
license: llama3
language:
- ja
- en
tags:
- llama
- llama-3
- gptq
inference: false
base_model: rinna/llama-3-youko-70b-instruct
base_model_relation: quantized
Llama 3 Youko 70B Instruct GPTQ (rinna/llama-3-youko-70b-instruct-gptq)
Overview
rinna/llama-3-youko-70b-instruct-gptq is the quantized model for rinna/llama-3-youko-70b-instruct 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-instruct for details about model architecture and data.
Contributors
Release date
July 25, 2024
Benchmarking
Please refer to rinna's LM benchmark page (Sheet 20240725).
How to use the model
We found this instruction-tuned model tends to generate repeated text more often than its base counterpart, and thus we set repetition_penalty=1.1 for better generation performance. The same repetition penalty was applied to the instruction-tuned model in the aforementioned evaluation experiments.
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "rinna/llama-3-youko-70b-instruct-gptq"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
)
messages = [
{"role": "system", "content": "あなたは誠実で優秀なアシスタントです。どうか、簡潔かつ正直に答えてください。"},
{"role": "user", "content": "西田幾多郎とはどんな人物ですか？"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.convert_tokens_to_ids("<|end_of_text|>"),
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=512,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
repetition_penalty=1.1,
)
response = outputs[0][input_ids.shape[-1]:]
response = tokenizer.decode(response, skip_special_tokens=True)
print(response)
Tokenization
The model uses the original meta-llama/Meta-Llama-3-70B-Instruct tokenizer.
How to cite
@misc{rinna-llama-3-youko-70b-instruct-gptq,
title = {rinna/llama-3-youko-70b-instruct-gptq},
author = {Wakatsuki, Toshiaki and Mitsuda, Koh and Chen, Xinqi and Sawada, Kei},
url = {https://huggingface.co/rinna/llama-3-youko-70b-instruct-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}
}