File size: 1,297 Bytes
740f687 fe02692 740f687 ebce2f4 5b4bbaf ebce2f4 1b98b3a cfb9a07 1b98b3a ebce2f4 8c688d8 5b4bbaf |
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
language:
- zh
library_name: transformers
---
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
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/baichuan-7B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("baichuan-inc/baichuan-7B", device_map="auto", trust_remote_code=True)
model = PeftModel.from_pretrained(model, "hiyouga/baichuan-7b-sft")
query = "晚上睡不着怎么办"
inputs = tokenizer(["<human>:{}\n<bot>:".format(query)], return_tensors="pt")
inputs = inputs.to("cuda")
generate_ids = model.generate(**inputs)
output = tokenizer.batch_decode(generate_ids)[0]
print(output)
```
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 baichuan-inc/baichuan-7B \
--checkpoint_dir hiyouga/baichuan-7b-sft \
--prompt_template ziya
```
Loss curve on training set:
Loss curve on evaluation set:
|