Update main.py

main.py

@@ -1,52 +1,31 @@
+import transformers
+import torch
 from fastapi import FastAPI
 from pydantic import BaseModel
-from huggingface_hub import InferenceClient
 import uvicorn
 
 
 app = FastAPI()
 
-client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")
+
+
+model_id = "meta-llama/Meta-Llama-3-8B"
+
+pipeline = transformers.pipeline(
+    "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
+)
+
+
 
 class Item(BaseModel):
     prompt: str
-    history: list
-    system_prompt: str
-    temperature: float = 0.0
-    max_new_tokens: int = 1024
-    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
+    pipeline(item.prompt)
+   
 
 @app.post("/generate/")
 async def generate_text(item: Item):