Foundation model: open_llama_7b_v2, a commercially available language model.
Finetuning data: ShareGPT，ShareGPT-ZH，Langchain-MRKL-finetune
Training code: based on 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. 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")

instruction = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {} ASSISTANT:"
prompt = instruction.format('用flask写一个简单的http服务器。')
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")

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）：

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")
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").