---
license: apache-2.0
datasets:
- tatsu-lab/alpaca
- sahil2801/CodeAlpaca-20k
language:
- zh
- en
library_name: transformers
tags:
- baichuan
- lora
---

An instruction-tuned LoRA model of https://huggingface.co/baichuan-inc/baichuan-7B

This checkpoint is trained with: https://github.com/hiyouga/LLaMA-Efficient-Tuning

Usage:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer


tokenizer = AutoTokenizer.from_pretrained("hiyouga/baichuan-7b-sft", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("hiyouga/baichuan-7b-sft", trust_remote_code=True).cuda()
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

query = "晚上睡不着怎么办"
template = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\nHuman: {}\nAssistant: "

inputs = tokenizer([template.format(query)], return_tensors="pt")
inputs = inputs.to("cuda")
generate_ids = model.generate(**inputs, max_new_tokens=256, streamer=streamer)
```

You could also alternatively launch a CLI demo by using the script in https://github.com/hiyouga/LLaMA-Efficient-Tuning
```bash
python src/cli_demo.py --model_name_or_path hiyouga/baichuan-7b-sft
```

Loss curve on training set:
![train](training_loss.svg)

Loss curve on evaluation set:
![eval](eval_loss.svg)