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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load your fine-tuned model
model_id = "ragunath-ravi/distilgpt2-lmsys-chat"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# Set padding token to be the same as EOS token if not set
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
# Move model to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Format the conversation history as expected by the model
prompt = system_message + "\n\n"
for user_msg, assistant_msg in history:
if user_msg:
prompt += f"User: {user_msg}\n"
if assistant_msg:
prompt += f"Assistant: {assistant_msg}\n"
# Add the latest user message
prompt += f"User: {message}\nAssistant:"
# Tokenize the prompt
inputs = tokenizer(prompt, return_tensors="pt").to(device)
# Generate response
with torch.no_grad():
output = model.generate(
inputs["input_ids"],
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
attention_mask=inputs["attention_mask"],
)
# Decode the generated response
full_response = tokenizer.decode(output[0], skip_special_tokens=True)
# Extract only the assistant's part from the full response
assistant_response = full_response[len(prompt):].strip()
# Sometimes the model might continue with "User:" - we need to cut that off
if "User:" in assistant_response:
assistant_response = assistant_response.split("User:")[0].strip()
# Stream the response token by token (simulated for this model)
response_so_far = ""
tokens = assistant_response.split()
for token in tokens:
response_so_far += token + " "
yield response_so_far.strip()
# Create the Gradio chat interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a helpful assistant.", label="System message"),
gr.Slider(minimum=1, maximum=512, value=128, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.9,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
title="DistilGPT-2 Chat Assistant",
description="A simple chatbot powered by a fine-tuned DistilGPT-2 model on the LMSYS Chat 1M dataset.",
)
if __name__ == "__main__":
demo.launch() |