import gradio as gr import torch from transformers import AutoModelForCausalLM, AutoTokenizer # Model ID model_id = "apu20/Llama-3.2-3B-Instruct_Tele" # Load quantized model (switch to 8-bit if needed) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, # Use float16 for reduced memory footprint device_map="cpu" # Force model to run on CPU ) # Load tokenizer tokenizer = AutoTokenizer.from_pretrained(model_id) def respond(message, history, system_message, max_tokens, temperature, top_p): messages = [{"role": "system", "content": system_message}] for val in history: if val[0]: messages.append({"role": "user", "content": val[0]}) if val[1]: messages.append({"role": "assistant", "content": val[1]}) messages.append({"role": "user", "content": message}) # Tokenize input inputs = tokenizer(message, return_tensors="pt").to("cpu") # Ensure inputs are on CPU # Generate response with torch.no_grad(): outputs = model.generate( **inputs, max_length=max_tokens, temperature=temperature, top_p=top_p ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response # Gradio Chat 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()