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from fastapi import FastAPI, Request | |
from transformers import AutoTokenizer, AutoModelForCausalLM | |
import torch | |
app = FastAPI() | |
# Load the Falcon 7B model and tokenizer | |
MODEL_NAME = "SpiceyToad/demo-falc" # Replace with your Hub repo name | |
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) | |
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.bfloat16, device_map="auto") | |
async def generate_text(request: Request): | |
# Parse input JSON | |
data = await request.json() | |
prompt = data.get("prompt", "") | |
max_length = data.get("max_length", 50) | |
# Tokenize input and generate text | |
inputs = tokenizer(prompt, return_tensors="pt").to("cuda") | |
outputs = model.generate(inputs["input_ids"], max_length=max_length) | |
response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
return {"generated_text": response} | |