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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load your custom model and tokenizer
MODEL_NAME = "Qwen/Qwen2.5-1.5B-Instruct" # Replace with your model's Hugging Face repo ID or local path
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16, device_map="auto")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Prepare the chat history
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})
# Format the input for the model
input_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
# Generate a response
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(
inputs.input_ids,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract only the assistant's response
assistant_response = response.split("assistant:")[-1].strip()
yield assistant_response
# Create the Gradio interface
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 (nucleus sampling)",
),
],
)
# Launch the app
if __name__ == "__main__":
demo.launch() |