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844ffb0 545ff31 844ffb0 545ff31 844ffb0 545ff31 844ffb0 545ff31 844ffb0 545ff31 844ffb0 545ff31 844ffb0 545ff31 844ffb0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_ID = "Qwen/Qwen2.5-Coder-7B-Instruct"
# Load tokenizer at startup
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
# Global model - loaded lazily on first GPU call
model = None
def load_model():
global model
if model is None:
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.float16,
device_map="auto",
)
return model
@spaces.GPU(duration=120)
def generate_response(message, history, system_message, max_tokens, temperature, top_p):
loaded_model = load_model()
messages = [{"role": "system", "content": system_message}]
for user_msg, assistant_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if assistant_msg:
messages.append({"role": "assistant", "content": assistant_msg})
messages.append({"role": "user", "content": message})
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = tokenizer([text], return_tensors="pt").to(loaded_model.device)
with torch.no_grad():
outputs = loaded_model.generate(
**inputs,
max_new_tokens=int(max_tokens),
temperature=float(temperature),
top_p=float(top_p),
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(
outputs[0][inputs['input_ids'].shape[1]:],
skip_special_tokens=True
)
return response
demo = gr.ChatInterface(
generate_response,
title="Qwen2.5 Coder 7B",
description="A coding assistant powered by Qwen2.5-Coder-7B-Instruct on ZeroGPU",
additional_inputs=[
gr.Textbox(
value="You are Qwen, a helpful coding assistant. You excel at writing clean, efficient code and explaining programming concepts clearly.",
label="System message",
lines=2,
),
gr.Slider(minimum=64, maximum=4096, value=1024, step=64, label="Max tokens"),
gr.Slider(minimum=0.1, maximum=1.5, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
],
examples=[
["Hello! What programming languages are you best at?"],
["Write a Python function to check if a string is a palindrome"],
["Explain the difference between async/await and promises in JavaScript"],
["Help me optimize this SQL query: SELECT * FROM users WHERE name LIKE '%john%'"],
],
)
if __name__ == "__main__":
demo.launch()
|