`rinna/youri-7b-gptq`

Overview

rinna/youri-7b-gptq is the quantized model for rinna/youri-7b using AutoGPTQ. The quantized version is 4x smaller than the original model and thus requires less memory and provides faster inference.

Library

Refer to the original model for library details.
Model architecture

Refer to the original model for architecture details.
Continual pre-training

Refer to the original model for pre-training details.
Contributors
Release date

October 31, 2023

Benchmarking

Please refer to rinna's LM benchmark page (Sheet 20231031).

How to use the model

import torch
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rinna/youri-7b-gptq")
model = AutoGPTQForCausalLM.from_quantized("rinna/youri-7b-gptq", use_safetensors=True)

text = "西田幾多郎は、"
token_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt")

with torch.no_grad():
    output_ids = model.generate(
        input_ids=token_ids.to(model.device),
        max_new_tokens=200,
        min_new_tokens=200,
        do_sample=True,
        temperature=1.0,
        top_p=0.95,
        pad_token_id=tokenizer.pad_token_id,
        bos_token_id=tokenizer.bos_token_id,
        eos_token_id=tokenizer.eos_token_id
    )

output = tokenizer.decode(output_ids.tolist()[0])
print(output)

Tokenization

The model uses the original llama-2 tokenizer.

How to cite

@misc{rinna-youri-7b-gptq,
    title = {rinna/youri-7b-gptq},
    author = {Wakatsuki, Toshiaki and Zhao, Tianyu and Sawada, Kei},
    url = {https://huggingface.co/rinna/youri-7b-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}}
}