Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- anon8231489123/ShareGPT_Vicuna_unfiltered
+- PengQu/langchain-MRKL-finetune
+language:
+- zh
+- en
+---
+# open_llama_7b_v2_vicuna_Chinese
+
+open_llama_7b_v2_vicuna_Chinese是在中英双语sharegpt数据上全参数微调的对话模型。
+
+- 基座模型：[open_llama_7b_v2](https://huggingface.co/openlm-research/open_llama_7b_v2), 允许商业使用。
+- 微调数据：ShareGPT，ShareGPT-ZH，Langchain-MRKL-finetune
+- 训练代码：基于[FastChat](https://github.com/lm-sys/FastChat)
+
+open_llama_7b_v2_vicuna_Chinese is a chat model supervised finetuned on vicuna sharegpt data in both **English** and **Chinese**.
+
+- Foundation model: [open_llama_7b_v2](https://huggingface.co/openlm-research/open_llama_7b_v2), a **commercially available** language model.
+- Finetuning data: ShareGPT，ShareGPT-ZH，Langchain-MRKL-finetune
+- Training code: based on [FastChat](https://github.com/lm-sys/FastChat)
+
+## Loading the Weights with Hugging Face Transformers
+**Please note that it is advised to avoid using the Hugging Face fast tokenizer for now, as we’ve observed that** [**the auto-converted fast tokenizer sometimes gives incorrect tokenizations**](https://github.com/huggingface/transformers/issues/24233)**.** This can be achieved by directly using the `LlamaTokenizer` class, or passing in the `use_fast=False` option for the `AutoTokenizer` class. See the following example for usage.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("PengQu/open_llama_7b_v2_vicuna_Chinese",use_fast=False)
+model = AutoModelForCausalLM.from_pretrained("PengQu/open_llama_7b_v2_vicuna_Chinese").to("cuda:6")
+
+prompt = '用flask写一个简单的http服务器。'
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda:6")
+
+generation_output = model.generate(input_ids=input_ids, max_new_tokens=512)
+print(tokenizer.decode(generation_output[0],skip_special_tokens=True))
+```
+
+输出如下（output as follows）：<br>
+```
+用flask写一个简单的http服务器。
+
+from flask import Flask
+app = Flask(__name__)
+@app.route('/')
+def hello():
+    return 'Hello, World!'
+if __name__ == '__main__':
+    app.run()
+
+这段代码定义了一个Flask应用程序，并为根路径（'/'）定义了一个路由。当用户在其Web浏览器中导航到该路径时，将调用`hello()`函数，并返回字符串“Hello, World!”。
+要运行此代码，您需要在计算机上安装Flask。您可以使用以下命令使用pip安装它：
+
+pip install Flask
+
+安装Flask后，您可以使用以下命令运行代码：
+
+python app.py
+
+这将启动一个本地开发服务器，您可以使用Web浏览器访问它，方法是导航到`http://localhost:5000/`。
+您还可以通过添加其他路由和功能来进一步自定义代码。例如，您可以为不同的端点定义不同的路由，并使用请求数据执行某些操作。您还可以向应用程序添加错误处理和用户身份验证。
+```
+## Major Improvement
+- 基于open_llama_7b_v2训练，完全允许商业使用
+- 英语效果与vicuna-7b持平，中文效果好于vicuna-7b
+- 编程能力好于vicuna-7b，应该是open_llama_7b_v2用了StarCoder数据集
+- 支持langchain-MRKL格式(agent= "zero-shot-react-description")
+<br>
+- Finetuned on openllama, allowing for commercial purposes.
+- Achieves the same level of English performance as vicuna-7b and outperforms vicuna-7b in Chinese performance
+- Has better programming ability than vicuna-7b, likely due to the use of the StarCoder dataset in open_llama_7b_v2
+- Supports langchain-MRKL format(agent= "zero-shot-react-description").
+