🎓 Math Misconception Classifier (Llama-3 8B)
This model is fine-tuned to identify specific mathematical misconceptions in student explanations. It was developed as part of a Final Year Computer Science Major Project.
🚀 Model Details
- Developed by: Priyanshu Bhusan
- Model Type: Large Language Model (Fine-tuned for Classification)
- Base Model: Meta Llama-3 8B (4-bit Quantized)
- Task: Mapping student math misunderstandings to specific categories.
- Language: English
- Fine-tuning Technique: LoRA (Low-Rank Adaptation) via Unsloth.
📊 Performance Metrics
Based on the validation set (MAP Kaggle dataset):
- Top-1 Accuracy: 88.00%
- MAP@3 Score: 0.9090
- Weighted F1-Score: 0.7448
🛠️ Training Setup
- Hardware: NVIDIA L4/T4 GPU (Lightning AI Studio)
- Optimization: Unsloth 4-bit kernels for memory efficiency.
- Training Steps: 60 steps (Initial Trial)
- Learning Rate: 2e-4 with Linear Scheduler.
🧠 How to Use
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "vijigishu/math-misconception-llama-3",
max_seq_length = 512,
load_in_4bit = True,
)
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Model tree for vijigishu/math-misconception-llama-3
Base model
meta-llama/Meta-Llama-3-8B Quantized
unsloth/llama-3-8b-bnb-4bit