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omurberaisik
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Model Details
Model Provider
omurberaisik
Model Tree
mistralai/Mistral-7B-Instruct-v0.3
Input Modalities
Output Modalities
Supported Functionality
Unlike standard fine-tuning approaches that mix all data randomly, MateMistral-7B-Base uses a strict curriculum order — inspired by how humans learn mathematics: Stage 1: Logic & Reasoning → 6,000 samples (20%)
Stage 2: Code + Mathematics → 4,500 samples (15%)
Stage 3: Hard Olympiad Math → 19,500 samples (65%)
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Total → 30,000 samples
This ordering is not random. The model first builds a strong reasoning foundation, then learns mathematical code patterns, and finally tackles olympiad-level problems — at which point the loss drops sharply, indicating genuine mathematical understanding rather than pattern memorization.
📉 The loss curve showed a dramatic drop at step ~385, exactly when NuminaMath olympiad problems began — confirming the curriculum strategy worked.
| Parameter | Value |
|---|---|
| Base Model | Mistral-7B-Instruct-v0.3 (4-bit) |
| Method | LoRA + Curriculum SFT |
| LoRA Rank | 16 |
| LoRA Alpha | 16 |
| Max Sequence Length | 4096 |
| Effective Batch Size | 16 |
| Training Steps | 600 |
| Learning Rate | 2e-4 |
| Optimizer | AdamW 8-bit |
| Hardware | Kaggle T4 GPU |
| Training Time | ~6 hours |
| Dataset | Samples | Purpose |
|---|---|---|
| Open-Orca/SlimOrca-Dedup | 6,000 | Logic & reasoning foundation |
| MathLLMs/MathCodeInstruct | 4,500 | Mathematical code understanding |
| AI-MO/NuminaMath-CoT | 19,500 | Olympiad-level mathematics |
python
from unsloth import FastLanguageModelmodel, tokenizer = FastLanguageModel.from_pretrained(model_name = "omurberaisik/MateMistral-7B-base",max_seq_length = 4096,load_in_4bit = True,)FastLanguageModel.for_inference(model)messages = [{"role": "user", "content": "Find all integer solutions to x² + y² = z²."}]inputs = tokenizer.apply_chat_template(messages,tokenize=True,add_generation_prompt=True,return_tensors="pt").to("cuda")outputs = model.generate(input_ids = inputs,max_new_tokens = 512,temperature = 0.7,do_sample = True,)print(tokenizer.decode(outputs[0], skip_special_tokens=True))
omurberaisik/MateMistral-7Bomurberaisik/MateMistral-7B)Trained by @omurberaisik using curriculum learning on a single Kaggle T4 GPU.
"The right order of learning matters more than the amount of data."