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datasets: |
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- TIGER-Lab/MathInstruct |
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base_model: |
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- meta-llama/Llama-3.2-3B-Instruct |
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tags: |
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- lora |
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- adapters |
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- llama |
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--- |
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# **LLAMA-3.2-3B-MathInstruct_LORA_SFT** |
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This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) dataset. The fine-tuning process was designed to enhance the model's performance for mathematical instruction-following tasks, ensuring improved accuracy and precision when solving math-related problems. |
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It achieves the following results on the evaluation set: |
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- **Loss**: 0.6895 |
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## **Model Description** |
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This model is specifically fine-tuned for mathematical reasoning, problem-solving, and instruction-following tasks. Leveraging the LLaMA-3.2-3B-Instruct base model, it has been optimized to handle mathematical queries and tasks with improved efficiency and context understanding. |
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## **Training and Evaluation Data** |
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The model was fine-tuned on the [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) dataset. |
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- **Dataset Source**: TIGER-Lab. |
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- **Dataset Focus**: Mathematical instruction-following and reasoning tasks. |
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- **Scope**: A wide range of math topics, including arithmetic, algebra, calculus, and problem-solving. |
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The dataset was carefully curated to align with instructional objectives for solving mathematical problems and understanding step-by-step reasoning. |
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## **Training Procedure** |
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### **Hyperparameters** |
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- **Learning rate**: 0.0001 |
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- **Train batch size**: 1 |
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- **Eval batch size**: 1 |
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- **Gradient accumulation steps**: 8 |
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- **Total effective batch size**: 8 |
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- **Optimizer**: AdamW (torch) |
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- **Betas**: (0.9, 0.999) |
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- **Epsilon**: 1e-08 |
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- **Learning rate scheduler**: Cosine schedule with 10% warmup. |
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- **Number of epochs**: 3.0 |
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### **Framework Versions** |
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- **PEFT**: 0.12.0 |
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- **Transformers**: 4.46.1 |
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- **PyTorch**: 2.5.1+cu124 |
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- **Datasets**: 3.1.0 |
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- **Tokenizers**: 0.20.3 |
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## **Training Results** |
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- **Loss**: 0.6895 |
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- Evaluation indicates strong performance on math instruction-following tasks. Further testing on specific use cases is recommended to assess the model’s generalizability. |
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## **Additional Information** |
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- **Author**: Sri Santh M |
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- **Purpose**: Fine-tuned for educational and development purposes, particularly for math-related tasks. |
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- **Dataset Link**: [MathInstruct Dataset](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) |
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This model represents a focused effort to adapt the LLaMA-3.2-3B-Instruct model for specialized mathematical use cases. It can be further fine-tuned or extended for more specific mathematical domains or applications. |
