i-route-ai/iroute-math-llm-v2-16bit
The i-route-ai/iroute-math-llm-v2-16bit is a LoRA adapter developed by i-route-ai, fine-tuned from unsloth/Meta-Llama-3.1-8B-bnb-4bit. This adapter specializes in generating accurate solutions and explanations for mathematical problems, leveraging the 8 billion parameter Llama 3.1 base model. It is specifically optimized for Korean mathematics curriculum data, providing detailed step-by-step problem-solving capabilities. The model is designed for integration into AI systems requiring precise mathematical reasoning and educational content generation.
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Overview
This model, iroute-math-llm-v2-16bit, is a LoRA adapter developed by i-route-ai, specifically fine-tuned for generating accurate solutions and explanations for mathematical problems in Korean. It is built upon the unsloth/Meta-Llama-3.1-8B-bnb-4bit base model, utilizing QLoRA (4-bit NF4) for efficient fine-tuning.
Key Capabilities
- Specialized Math Problem Solving: Generates precise step-by-step solutions and explanations for math questions.
- Korean Education Focus: Trained on the AI Hub 수학교과문제풀이데이터 (Korean national math curriculum dataset), including 12,640 training samples.
- Curriculum Alignment: Can incorporate achievement standards (2022/2015 curriculum) using a
[성취 기준: ...]tag in prompts. - Robust Training: Achieved a best evaluation loss of 0.7531 at step 1500 (epoch 2), with continuous decrease in evaluation loss indicating no overfitting.
- Efficient Deployment: Designed to be loaded as a PEFT adapter on a base model, allowing for concurrent service with other adapters on a single GPU.
Use Cases
- Educational AI: Ideal for applications requiring automated math problem solving and detailed explanations for students.
- Content Generation: Useful for creating educational materials, tutorials, or interactive learning platforms focused on mathematics.
- RAG Integration: Can be combined with RAG contexts in systems like the i-Route AI server to provide personalized math concept explanations and solutions based on student performance.