LLaMA2-SFT
LLaMA2-SFT, Llama-2-7B微调(transformers)/LORA(peft)/推理
Gtihub地址
https://github.com/yongzhuo/Llama2-SFT
prompt
text_1 = f"".join(["[INST] <<SYS>>\n ""You are a helpful, respectful and honest assistant. ""Always answer as helpfully as possible, while being safe."" Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, ""or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\n"" If a question does not make any sense, or is not factually coherent, ""explain why instead of answering something not correct. ""If you don't know the answer to a question, please don't share false information.\n""<</SYS>>\n\n{0} [/INST] "]).format(data_point.get('instruction', '').strip() +"\t"+ data_point.get('input', '').strip())我们缩短后为
text_1 = f"[INST] <<SYS>>\n You are a helpful, respectful and honest assistant.<</SYS>>" \f"\n\n{0} [/INST] ".format(data_point.get('instruction', '').strip() + "\t" + data_point.get('input', '').strip())踩过的坑(截至20230727)
1. LLaMA2的weights权重不能为fp16(即必须为fp32, 或者bf16), 否则会溢出;
微调样例
地址: llama2_sft/ft_llama2配置: llama2_sft/ft_llama2/config.py
训练: python train.py
推理: python predict.py
验证: python evaluation.py
接口: python post_api.py
实验日志
微调日志(ADVGEN)
推理样例(LoRA, R=8)
参考/感谢
- https://github.com/facebookresearch/llama
- https://github.com/huggingface/peft
- https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
- https://github.com/THUDM/ChatGLM-6B
- math23k
免责申明
本项目相关资源仅供学术研究之用，使用涉及第三方代码的部分时，请严格遵循相应的开源协议。模型生成的内容受模型计算、随机性和量化精度损失等因素影响，本项目不对其准确性作出保证。对于模型输出的任何内容，本项目不承担任何法律责任，亦不对因使用相关资源和输出结果而可能产生的任何损失承担责任。
- 大模型权重的详细协议见facebookresearch/llama
facebookresearch/llama](https://github.com/facebookresearch/llama)
