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import gradio as gr
import spaces
from transformers import pipeline
# Initialize model once at startup
model = pipeline(
"text-generation",
model="unsloth/DeepSeek-R1-Distill-Llama-8B",
torch_dtype="auto",
device_map="auto"
)
@spaces.GPU(duration=120)
def chat_response(message, history):
# Format conversation history for model input
messages = []
for human, assistant in history:
messages.extend([
{"role": "user", "content": human},
{"role": "assistant", "content": assistant}
])
messages.append({"role": "user", "content": message})
# Generate response
response = model(
messages,
max_new_tokens=256,
temperature=0.7,
do_sample=True
)
return response[0]['generated_text'][-1]["content"]
# Create chat interface
demo = gr.ChatInterface(
chat_response,
chatbot=gr.Chatbot(height=500),
textbox=gr.Textbox(placeholder="Ask me anything...", container=False, scale=7),
title="DeepSeek-Llama-8B Chat Demo",
examples=[["Explain quantum computing simply"], ["Write a Python function for Fibonacci sequence"]]
)
demo.launch()
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