main.py

from fastapi import FastAPI
from pydantic import BaseModel
from huggingface_hub import InferenceClient
import uvicorn
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig


app = FastAPI()

model_id = "mistralai/Mistral-7B-v0.1"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)

#client = InferenceClient("HFHAB/FinetunedMistralModel")

class Item(BaseModel):
    prompt: str
    history: list
    system_prompt: str
    temperature: float = 0.3
    max_new_tokens: int = 5000
    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} "
    prompt += f"</s>[INST] {message} [/INST]"
    return prompt

def formatting_func(example):
    text = f"### Question: {example['input']}\n ### Answer: {example['output']}"
    return text

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)
    inputs = tokenizer(formatted_prompt, return_tensors="pt").to(0)
    out = model.generate(**inputs, max_new_tokens=250, temperature = 0.6, top_p=0.95, tok_k=40)
    output = tokenizer.decode(out[0], skip_special_tokens=True)
    #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

@app.post("/generate/")
async def generate_text(item: Item):
    return {"response": generate(item)}