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

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