Sandesh-Zenteiq/MathTutor-7B_v0.1.75

Model Card for Model ID

Model: MathTutor RL version (Lambda = 1.75) (no 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_v0.1.75" 
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})