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)
