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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
# Load model and tokenizer
model_name = "martinbravo/llama_finetuned_test"
base_model_name = "unsloth/llama-3.2-1b-instruct-bnb-4bit"
# Load tokenizer and model locally
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto", # Automatically maps model to GPU/CPU
trust_remote_code=True, # If model uses custom implementations
)
# Create a text-generation pipeline
generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Build input prompt
prompt = system_message + "\n"
for user_input, assistant_response in history:
prompt += f"User: {user_input}\nAssistant: {assistant_response}\n"
prompt += f"User: {message}\nAssistant:"
# Generate response
response = generator(
prompt,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True, # Sampling for diverse responses
)[0]["generated_text"]
# Extract the assistant's response
assistant_response = response[len(prompt) :].strip()
yield assistant_response
# 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)"
),
],
)
if __name__ == "__main__":
demo.launch() |