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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from peft import PeftModel
# Load base + LoRA model
base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
lora_model = "Futuresony/future_12_10_2024"
tokenizer = AutoTokenizer.from_pretrained(base_model)
base = AutoModelForCausalLM.from_pretrained(base_model)
model = PeftModel.from_pretrained(base, lora_model)
model.eval()
# Create generation pipeline
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
# Define the chat function
def respond(message, history, system_message, max_tokens, temperature, top_p):
prompt = system_message + "\n"
for user, bot in history:
prompt += f"User: {user}\nAssistant: {bot}\n"
prompt += f"User: {message}\nAssistant:"
response = generator(
prompt,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
return_full_text=False,
)[0]["generated_text"]
yield response.strip()
# Set up 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 (nucleus sampling)"),
],
)
if __name__ == "__main__":
demo.launch()
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