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d04f182 b17e7db 5477154 b17e7db d04f182 b17e7db d04f182 b17e7db d04f182 b17e7db d04f182 b17e7db d04f182 b17e7db d04f182 b17e7db d04f182 b17e7db d04f182 b17e7db d04f182 b17e7db d04f182 | 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 | import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load Qwen3-0.6B locally with GPU/CPU optimization
model_name = "Qwen/Qwen3-0.6B"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="auto" if torch.cuda.is_available() else None
)
model.eval()
def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
# Build chat history
messages = [{"role": "system", "content": system_message}]
for user_msg, bot_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if bot_msg:
messages.append({"role": "assistant", "content": bot_msg})
messages.append({"role": "user", "content": message})
# Format messages into a single string for generation
prompt = ""
for m in messages:
prompt += f"{m['role'].capitalize()}: {m['content']}\n"
prompt += "Assistant:"
# Tokenize
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
# Generate
output_ids = model.generate(
input_ids,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True
)
# Decode
output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
response = output_text[len(prompt):].strip()
yield response
# Gradio UI
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, 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"),
],
)
if __name__ == "__main__":
demo.launch()
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