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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from huggingface_hub import login
import os
import torch
import uvicorn
login(os.getenv("HF_TOKEN"))
app = FastAPI(
title="VexaAI Model-Platform: NVIDIA Nemotron-Nano-9B-V2",
description="Self-hosted AI-Model NVIDIA Nemotron-Nano-9B-V2, powered by VexaAI.",
version="0.9"
)
model_name = "nvidia/NVIDIA-Nemotron-Nano-9B-v2"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16
)
model.eval()
class GenerateRequest(BaseModel):
prompt: str
max_new_tokens: int = 512
temperature: float = 0.7
@app.post("/generate")
async def generate_text(request: GenerateRequest):
try:
inputs = tokenizer(request.prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=request.max_new_tokens,
temperature=request.temperature,
do_sample=True,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id
)
full_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
generated_text = full_text[len(tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True)):].strip()
return {"generated_text": generated_text}
except Exception as e:
raise HTTPException(status_code=500, detail=f"VexaAI Model_Platform: HTTP/S error: {str(e)}")
@app.get("/")
async def root():
return {"message": "To start generating text, use /generate."}
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860) |