---
license: apache-2.0
---
### This model is trained from Mistral-7B-Instruct-V0.2 with 90% chinese dataset and 10% english dataset
github [Web-UI](https://github.com/moseshu/llama2-chat/tree/main/webui)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/62f4c7172f63f904a0c61ba3/JIeyxhTm9_PNzXyU7wQVd.png)

```
from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer,AutoTokenizer,AutoModelForCausalLM,MistralForCausalLM
import torch

model_id=Mistral-7B-Instruct-v0.4

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype=torch.bfloat16,device_map="auto",)

chat_template="{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"

def chat_format(conversation:list):
    system_prompt = "You are a helpful, respectful and honest assistant.Help humman as much as you can."
    
    id = tokenizer.apply_chat_template(conversation,chat_template=chat_template,tokenize=False)
    
    return id

user_chat=[{"role":"user","content":"你好，最近在干嘛呢"}]
text = chat_format(user_chat).rstrip("</s>")
def predict(content_prompt):
    inputs = tokenizer(content_prompt,return_tensors="pt",add_special_tokens=True)
    input_ids = inputs["input_ids"].to("cuda:0")
    # print(f"input length:{len(input_ids[0])}")
    with torch.no_grad():
        generation_output = model.generate(
                    input_ids=input_ids,
                    #generation_config=generation_config,
                    return_dict_in_generate=True,
                    output_scores=True,
                    max_new_tokens=2048,
                    top_p=0.9,
                    num_beams=1,
                    do_sample=True,
                    repetition_penalty=1.0,
                    eos_token_id=tokenizer.eos_token_id,
                    pad_token_id=tokenizer.pad_token_id,
                )
        s = generation_output.sequences[0]
        output = tokenizer.decode(s,skip_special_tokens=True)
        output1 = output.split("[/INST]")[-1].strip()
        # print(output1)
    return output1

predict(text)
output：你好！作为一个大型语言模型，我一直在学习和提高自己的能力。最近，我一直在努力学习新知识、改进算法，以便更好地回答用户的问题并提供帮助。同时，我也会定期接受人工智能专家的指导和评估，以确保我的表现不断提升。希望这些信息对你有所帮助！
```