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| from fastapi import FastAPI | |
| from pydantic import BaseModel | |
| from huggingface_hub import InferenceClient | |
| import uvicorn | |
| app = FastAPI() | |
| client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") | |
| class Item(BaseModel): | |
| prompt: str | |
| history: list | |
| system_prompt: str | |
| temperature: float = 0.0 | |
| max_new_tokens: int = 1048 | |
| top_p: float = 0.15 | |
| repetition_penalty: float = 1.0 | |
| def format_prompt(message, history): | |
| prompt = "<s>" | |
| for user_prompt, bot_response in history: | |
| prompt += f"[INST] {user_prompt} [/INST]" | |
| prompt += f" {bot_response}</s> " | |
| prompt += f"[INST] {message} [/INST]" | |
| return prompt | |
| def generate(item: Item): | |
| temperature = float(item.temperature) | |
| if temperature < 1e-2: | |
| temperature = 1e-2 | |
| top_p = float(item.top_p) | |
| generate_kwargs = dict( | |
| temperature=temperature, | |
| max_new_tokens=item.max_new_tokens, | |
| top_p=top_p, | |
| repetition_penalty=item.repetition_penalty, | |
| do_sample=True, | |
| seed=42, | |
| ) | |
| formatted_prompt = format_prompt(f"{item.system_prompt}, {item.prompt}", item.history) | |
| stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) | |
| output = "" | |
| for response in stream: | |
| output += response.token.text | |
| return output | |
| async def generate_text(item: Item): | |
| return {"response": generate(item)} | |