README.md

---
title: chinese-alpaca-33b-merged
emoji: 📚
colorFrom: gray
colorTo: red
sdk: gradio
sdk_version: 3.23.0
app_file: app.py
pinned: false
---

加入中文词表并继续预训练中文Embedding，并在此基础上继续使用指令数据集finetuning，得到的中文Alpaca-33B模型。

模型转换用到的相关base及lora模型如下：
- base-model: elinas/llama-30b-hf-transformers-4.29
- lora-model: ziqingyang/chinese-alpaca-lora-33b

详情可参考：https://github.com/ymcui/Chinese-LLaMA-Alpaca/releases/tag/v4.0


### 使用方法参考
1. 安装模块包
```bash
pip install sentencepiece
pip install transformers>=4.28.0
```

2. 生成文本
```python
import torch
import transformers
from transformers import LlamaTokenizer, LlamaForCausalLM

def generate_prompt(text):
    return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{text}

### Response:"""


tokenizer = LlamaTokenizer.from_pretrained('minlik/chinese-alpaca-33b-merged')
model = LlamaForCausalLM.from_pretrained('minlik/chinese-alpaca-33b-merged').half().to('cuda')
model.eval()

text = '第一个登上月球的人是谁？'
prompt = generate_prompt(text)
input_ids = tokenizer.encode(prompt, return_tensors='pt').to('cuda')


with torch.no_grad():
    output_ids = model.generate(
        input_ids=input_ids,
        max_new_tokens=128,
        temperature=1,
        top_k=40,
        top_p=0.9,
        repetition_penalty=1.15
    ).cuda()
output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(output.replace(prompt, '').strip())
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_minlik__chinese-alpaca-33b-merged)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 53.09   |
| ARC (25-shot)         | 59.3          |
| HellaSwag (10-shot)   | 78.43    |
| MMLU (5-shot)         | 57.69         |
| TruthfulQA (0-shot)   | 52.45   |
| Winogrande (5-shot)   | 76.09   |
| GSM8K (5-shot)        | 8.04        |
| DROP (3-shot)         | 39.67         |