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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "nvidia/OpenReasoning-Nemotron-1.5B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
def chat_api(prompt, max_new_tokens=200, temperature=0.7):
inputs = tokenizer(prompt, return_tensors="pt").to(device)
outputs = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
temperature=temperature,
do_sample=True
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
demo = gr.Interface(
fn=chat_api,
inputs=[
gr.Textbox(label="Prompt", placeholder="Ask me anything..."),
gr.Slider(50, 512, value=200, step=10, label="Max Tokens"),
gr.Slider(0.1, 1.5, value=0.7, step=0.1, label="Temperature")
],
outputs="text",
title="OpenReasoning Nemotron-1.5B API",
description="Public Hugging Face Space that runs NVIDIA's Nemotron-1.5B model."
)
demo.launch() |