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Model: minlik/chinese-alpaca-33b-merged
Source: Original Platform

README.md

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+---
+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         |