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Stack 2.9 - HuggingFace Space
Gradio 6.x compatible
"""
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
print("Loading model...")
MODEL_NAME = "Qwen/Qwen2.5-Coder-1.5B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
torch_dtype=torch.float32,
device_map="cpu",
trust_remote_code=True,
low_cpu_mem_usage=True
)
print("Model loaded!")
def generate(prompt, max_tokens, temperature):
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=int(max_tokens),
temperature=float(temperature),
do_sample=True,
pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
return response.strip()
demo = gr.Interface(
fn=generate,
inputs=[
gr.Textbox(label="Prompt", placeholder="Write a Python function to calculate fibonacci...", lines=4),
gr.Number(label="Max tokens", value=256, minimum=64, maximum=512),
gr.Number(label="Temperature", value=0.7, minimum=0.1, maximum=1.0),
],
outputs=gr.Textbox(label="Response", lines=10),
title="Stack 2.9 Code Assistant",
description="Powered by Qwen2.5-Coder-1.5B",
)
demo.launch(server_name="0.0.0.0", server_port=7860)
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