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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# Load the model and tokenizer
def load_model():
base_model_name = "unsloth/llama-3.2-1b-instruct-bnb-4bit" # Replace with your base model name
lora_model_name = "sreyanghosh/lora_model" # Replace with your LoRA model path
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
model = AutoModelForCausalLM.from_pretrained(
base_model_name,
device_map="auto" if torch.cuda.is_available() else None,
load_in_8bit=not torch.cuda.is_available(),
)
model = PeftModel.from_pretrained(model, lora_model_name)
model.eval()
return tokenizer, model
tokenizer, model = load_model()
# Define the respond function
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Prepare the conversation history
messages = [{"role": "system", "content": system_message}]
for user_input, bot_response in history:
if user_input:
messages.append({"role": "user", "content": user_input})
if bot_response:
messages.append({"role": "assistant", "content": bot_response})
messages.append({"role": "user", "content": message})
# Format the input for the model
conversation_text = "\n".join(
f"{msg['role']}: {msg['content']}" for msg in messages
)
inputs = tokenizer(conversation_text, return_tensors="pt", truncation=True)
# Generate the model's response
outputs = model.generate(
inputs.input_ids,
max_length=len(inputs.input_ids[0]) + max_tokens,
temperature=temperature,
top_p=top_p,
pad_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract the new response
new_response = response[len(conversation_text):].strip()
yield new_response
# Gradio app configuration
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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