Spaces:
Sleeping
Sleeping
File size: 2,019 Bytes
0218c20 03991d8 0218c20 03991d8 0218c20 03991d8 0218c20 03991d8 0218c20 03991d8 0218c20 03991d8 0218c20 03991d8 0218c20 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
from fastapi import FastAPI
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load Qwen model and tokenizer (once)
model_name = "Qwen/Qwen2.5-0.5B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
# Set device
device = torch.device("cpu") # Or "cuda" if using GPU
model.to(device)
# FastAPI app
app = FastAPI()
# CORS settings
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Request body model
class Question(BaseModel):
question: str
# System prompt (your custom instructions)
SYSTEM_PROMPT = "You are Orion, an intelligent AI assistant created by Abdullah Ali, a 13-year-old from Lahore. Respond kindly and wisely."
# Chat response generator
async def generate_response_chunks(prompt: str):
# Build prompt using Qwen's expected format
qwen_prompt = (
f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
f"<|im_start|>user\n{prompt}<|im_end|>\n"
f"<|im_start|>assistant\n"
)
# Tokenize input
inputs = tokenizer(qwen_prompt, return_tensors="pt").to(device)
# Generate response
outputs = model.generate(
**inputs,
max_new_tokens=256,
do_sample=True,
temperature=0.7,
top_p=0.9,
pad_token_id=tokenizer.eos_token_id
)
# Decode and yield line by line
full_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
reply = full_output.split("<|im_start|>assistant\n")[-1].strip()
for chunk in reply.split():
yield chunk + " "
@app.post("/ask")
async def ask(question: Question):
return StreamingResponse(
generate_response_chunks(question.question),
media_type="text/plain"
)
|