| import gradio as gr | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_name = "Qwen/Qwen2.5-Coder-1.5B-Instruct" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| device_map="auto", | |
| torch_dtype="auto" | |
| ) | |
| def chat_fn(message, history): | |
| inputs = tokenizer.apply_chat_template( | |
| history + [{"role": "user", "content": message}], | |
| return_tensors="pt" | |
| ).to(model.device) | |
| outputs = model.generate(inputs, max_new_tokens=350) | |
| reply = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return reply | |
| iface = gr.ChatInterface(fn=chat_fn, title="AI Coder Chatbot") | |
| iface.launch() | |