File size: 1,410 Bytes
740f687 fe02692 d34a7d4 fe02692 56970e5 fe02692 56970e5 d34a7d4 740f687 ebce2f4 5b4bbaf ebce2f4 64cf906 ebce2f4 64cf906 8318b97 64cf906 ebce2f4 8318b97 ebce2f4 8318b97 cfb9a07 64cf906 8c688d8 8318b97 8c688d8 5b4bbaf 56970e5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
---
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:
Loss curve on evaluation set:
 |