LLaMA-7B-EN-Chat-40k/README.md

frameworks, license, tasks, datasets, tags

frameworks	license	tasks	datasets	tags
Pytorch	Apache License 2.0	text-generation	train Data-Juicer/alpaca-cot-en-refined-by-data-juicer	data-juicer arxiv:2309.02033

News

Our first data-centric LLM competition begins! Please visit the competition's official websites, FT-Data Ranker (1B Track, 7B Track), for more information.

Introduction

This is a reference LLM from Data-Juicer.

The model architecture is LLaMA-7B and we built it upon the pre-trained checkpoint. The model is fine-trained on 40k English chat samples of Data-Juicer's refined alpaca-CoT data. It beats LLaMA-7B fine-tuned on 52k Alpaca samples in GPT-4 evaluation.

For more details, please refer to our paper.

使用

from modelscope import (
    AutoModelForCausalLM, AutoTokenizer, GenerationConfig, snapshot_download
)
model_dir = 'LLaMA-7B-EN-Chat-40k'

tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForCausalLM.from_pretrained(model_dir).eval()

inputs = tokenizer('How are you?', return_tensors='pt').to(model.device)
response = model.generate(inputs.input_ids, max_length=128)
print(tokenizer.decode(response.cpu()[0], skip_special_tokens=True))

参考

If you find our work useful for your research or development, please kindly cite the following paper.

@misc{chen2023datajuicer,
title={Data-Juicer: A One-Stop Data Processing System for Large Language Models},
author={Daoyuan Chen and Yilun Huang and Zhijian Ma and Hesen Chen and Xuchen Pan and Ce Ge and Dawei Gao and Yuexiang Xie and Zhaoyang Liu and Jinyang Gao and Yaliang Li and Bolin Ding and Jingren Zhou},
year={2023},
eprint={2309.02033},
archivePrefix={arXiv},
primaryClass={cs.LG}
}

Clone with HTTP

 git clone https://www.modelscope.cn/Data-Juicer/LLaMA-7B-EN-Chat-40k.git