---
license: apache-2.0
base_model: Qwen/Qwen2.5-Math-7B-Instruct
tags:
  - math
  - ib-mathematics
  - qwen2
  - fine-tuned
  - education
  - ontology
  - chain-of-thought
language:
  - en
pipeline_tag: text-generation
---

# IB-Math-Ontology-7B

Fine-tuned Qwen2.5-Math-7B-Instruct for IB Mathematics AA with ontology-based Chain-of-Thought reasoning.

## Features
- 🎯 **IB Math AA Specialized**: Trained on 1,332 ontology-based examples
- 💭 **Chain-of-Thought**: Uses `<think>` tags for step-by-step reasoning
- 📚 **Curriculum-Aligned**: Covers all 5 IB Math AA topics
- ⚠️ **Pitfall Awareness**: Warns about common student mistakes

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ongilLabs/IB-Math-Ontology-7B", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("ongilLabs/IB-Math-Ontology-7B")

prompt = "Find the derivative of f(x) = x³ - 2x² + 5x [6 marks]"
messages = [
    {"role": "system", "content": "You are an expert IB Mathematics AA tutor. Think step-by-step and explain concepts clearly."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training Details
- **Base Model**: Qwen2.5-Math-7B-Instruct
- **Method**: LoRA (r=64, alpha=128)
- **Dataset**: 1,332 IB Math Ontology examples with CoT
- **Hardware**: NVIDIA A100 (80GB)
- **Epochs**: 3
- **Precision**: BF16