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| import gradio as gr | |
| import spaces | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| # --- Model Loading --- | |
| model_name = "gitglubber/Qwen3-IWM" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| torch_dtype="auto", | |
| device_map="auto" | |
| ) | |
| # --- System Message --- | |
| # Define the persona or instructions for the model | |
| system_message = """"You are an expert at the terminal. If asked to perform a task - decorate the command with @command. Explaining why you would perform that task to complete the function. Then if there are follow up commands use @command2, etc. Be helpful and willing to correction.""" | |
| # --- Generation Function --- | |
| def generate_response(chat_history): | |
| # Prepare the model input from the chat history | |
| # The system message is the first entry | |
| messages = [{"role": "system", "content": system_message}] | |
| # Add previous user/assistant messages | |
| for user_msg, assistant_msg in chat_history: | |
| messages.append({"role": "user", "content": user_msg}) | |
| messages.append({"role": "assistant", "content": assistant_msg}) | |
| # Apply the chat template | |
| text = tokenizer.apply_chat_template( | |
| messages, | |
| tokenize=False, | |
| add_generation_prompt=True, | |
| ) | |
| model_inputs = tokenizer([text], return_tensors="pt").to(model.device) | |
| # Generate text | |
| generated_ids = model.generate( | |
| **model_inputs, | |
| max_new_tokens=8192 | |
| ) | |
| output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() | |
| content = tokenizer.decode(output_ids, skip_special_tokens=True) | |
| return content | |
| # --- Gradio Interface --- | |
| with gr.Blocks(fill_height=True) as demo: | |
| gr.Markdown("# IWM Chat Bot") | |
| # We use a state object to store the system message, though it's constant here | |
| chatbot = gr.Chatbot(scale=1) | |
| msg = gr.Textbox(label="Input", scale=0) | |
| clear = gr.Button("Clear") | |
| def respond(message, chat_history): | |
| if not message.strip(): # Check for empty or whitespace-only messages | |
| return "", chat_history | |
| # Append the new user message to the history | |
| chat_history.append((message, None)) | |
| # Prepare history for the model (without the last empty spot) | |
| model_input_history = chat_history[:-1] | |
| model_input_history.append((message, None)) # Add current message for context | |
| # Flatten the history for the model function | |
| flat_history = [] | |
| for user, assistant in chat_history: | |
| if user: flat_history.append((user, assistant)) | |
| bot_response = generate_response(flat_history) | |
| # Update the last entry in chat_history with the bot's response | |
| chat_history[-1] = (message, bot_response) | |
| return "", chat_history | |
| msg.submit(respond, [msg, chatbot], [msg, chatbot]) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| # Launch the app | |
| demo.launch() |