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--- |
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license: apache-2.0 |
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base_model: Qwen/Qwen2.5-Math-7B-Instruct |
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tags: |
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- math |
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- ib-mathematics |
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- qwen2 |
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- fine-tuned |
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- education |
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- ontology |
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- chain-of-thought |
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language: |
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- en |
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pipeline_tag: text-generation |
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--- |
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# IB-Math-Ontology-7B |
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Fine-tuned Qwen2.5-Math-7B-Instruct for IB Mathematics AA with ontology-based Chain-of-Thought reasoning. |
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## Features |
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- 🎯 **IB Math AA Specialized**: Trained on 1,332 ontology-based examples |
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- 💭 **Chain-of-Thought**: Uses `<think>` tags for step-by-step reasoning |
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- 📚 **Curriculum-Aligned**: Covers all 5 IB Math AA topics |
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- ⚠️ **Pitfall Awareness**: Warns about common student mistakes |
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## Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("ongilLabs/IB-Math-Ontology-7B", torch_dtype="auto", device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained("ongilLabs/IB-Math-Ontology-7B") |
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prompt = "Find the derivative of f(x) = x³ - 2x² + 5x [6 marks]" |
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messages = [ |
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{"role": "system", "content": "You are an expert IB Mathematics AA tutor. Think step-by-step and explain concepts clearly."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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inputs = tokenizer(text, return_tensors="pt").to(model.device) |
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outputs = model.generate(**inputs, max_new_tokens=512) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## Training Details |
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- **Base Model**: Qwen2.5-Math-7B-Instruct |
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- **Method**: LoRA (r=64, alpha=128) |
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- **Dataset**: 1,332 IB Math Ontology examples with CoT |
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- **Hardware**: NVIDIA A100 (80GB) |
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- **Epochs**: 3 |
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- **Precision**: BF16 |
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