| --- |
| library_name: transformers |
| tags: [] |
| --- |
| |
| # Model Card for Model ID |
|
|
| <!-- Provide a quick summary of what the model is/does. --> |
| ## Model: MathTutor RL version (Lambda = 1.0) (Think) |
|
|
| ## Usage: |
|
|
| ``` |
| import torch |
| import json |
| from huggingface_hub import hf_hub_download |
| from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig |
| |
| # --- Configuration --- |
| model_weights_id = "Sandesh-Zenteiq/MathTutor-7B-Think_v0.1" |
| tokenizer_id = "Qwen/Qwen2.5-7B-Instruct" |
| device = "cuda" if torch.cuda.is_available() else "cpu" |
| |
| print(f"Loading weights from: {model_weights_id}") |
| print(f"Loading tokenizer from: {tokenizer_id}") |
| print(f"Using device: {device}") |
| |
| # --- Loading Logic --- |
| print("\nLoading model config...") |
| config = AutoConfig.from_pretrained(model_weights_id, trust_remote_code=True) |
| |
| print("\nLoading tokenizer...") |
| tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, trust_remote_code=True) |
| |
| print("Loading model weights...") |
| model = AutoModelForCausalLM.from_pretrained( |
| model_weights_id, |
| config=config, |
| torch_dtype=torch.bfloat16, |
| device_map="auto", |
| trust_remote_code=True |
| ) |
| print("Model loaded successfully!") |
| |
| # --- Interactive Socratic Chat Loop --- |
| conversation_history = [ |
| {"role": "system", "content": "You are a Socratic teacher. Guide the student to solve the problem by asking heuristic questions. Do not give direct answers or calculations. Ask one question at a time."}, |
| {"role": "user", "content": "YOUR QUESTION HERE"} |
| |
| ] |
| |
| print("\n--- Starting Interactive Socratic Session ---") |
| print("You are the student. The model is the teacher.") |
| print("Type 'quit' or 'exit' to end the conversation.\n") |
| |
| # Generate the very first response from the teacher |
| prompt_parts = [] |
| for message in conversation_history: |
| prompt_parts.append(f"<|im_start|>{message['role']}\n{message['content']}<|im_end|>") |
| # Signal to the model that it's its turn to generate |
| prompt_parts.append("<|im_start|>assistant") |
| manual_prompt = "\n".join(prompt_parts) |
| |
| inputs = tokenizer(manual_prompt, return_tensors="pt").to(model.device) |
| outputs = model.generate(**inputs, max_new_tokens=1000, temperature=0.7, do_sample=True) |
| initial_response = tokenizer.decode(outputs[0], skip_special_tokens=False) |
| # Extract only the assistant's part of the response |
| teacher_response_text = initial_response.split('<|im_start|>assistant')[1].replace('<|im_end|>', '').strip() |
| |
| print(f"Teacher: {teacher_response_text}") |
| conversation_history.append({"role": "assistant", "content": teacher_response_text}) |
| |
| |
| # Now start the interactive loop for back-and-forth |
| while True: |
| student_input = input("Student: ") |
| if student_input.lower() in ["quit", "exit"]: |
| print("--- Session Ended ---") |
| break |
| |
| # Add the user's new message to the history |
| conversation_history.append({"role": "user", "content": student_input}) |
| |
| # --- Manually build the prompt with the FULL history --- |
| prompt_parts = [] |
| for message in conversation_history: |
| prompt_parts.append(f"<|im_start|>{message['role']}\n{message['content']}<|im_end|>") |
| prompt_parts.append("<|im_start|>assistant") |
| manual_prompt = "\n".join(prompt_parts) |
| |
| # Generate the next response based on the full history |
| inputs = tokenizer(manual_prompt, return_tensors="pt").to(model.device) |
| outputs = model.generate(**inputs, max_new_tokens=1000, temperature=0.7, do_sample=True) |
| full_generation = tokenizer.decode(outputs[0], skip_special_tokens=False) |
| |
| # Cleanly extract only the *newest* assistant response |
| try: |
| new_response_part = full_generation.split(manual_prompt)[1] |
| teacher_response_text = new_response_part.replace('<|im_end|>', '').strip() |
| except IndexError: |
| # Fallback if splitting fails |
| teacher_response_text = "I'm sorry, I seem to have lost my train of thought. Could you please repeat your question?" |
| |
| |
| print(f"\nTeacher: {teacher_response_text}") |
| |
| # Add the model's new response to the history for the next turn |
| conversation_history.append({"role": "assistant", "content": teacher_response_text}) |
| ``` |
