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