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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch
model_id = "PowerInfer/SmallThinker-21BA3B-Instruct"
model = None
tokenizer = None
generator = None
def chat(prompt, max_new_tokens=256, temperature=0.7):
global model, tokenizer, generator
if generator is None:
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto", # Let Accelerate handle the devices
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
trust_remote_code=True
)
# ❌ Remove the `device` argument to avoid ValueError
generator = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer
)
output = generator(
prompt,
max_new_tokens=max_new_tokens,
temperature=temperature,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
return output[0]["generated_text"]
demo = gr.Interface(
fn=chat,
inputs=[
gr.Textbox(label="Prompt", lines=4),
gr.Slider(32, 512, value=256, step=16, label="Max New Tokens"),
gr.Slider(0.1, 1.5, value=0.7, step=0.1, label="Temperature")
],
outputs=gr.Textbox(label="Response"),
title="💬 SmallThinker-21B",
)
if __name__ == "__main__":
demo.launch()
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