Overview

This model, difficulty_sorting_easy_seed_math, is a specialized fine-tune of the Qwen/Qwen2.5-7B-Instruct base model. It has been adapted by mlfoundations-dev using the mlfoundations-dev/difficulty_sorting_easy_seed_math dataset. The fine-tuning process aimed to enhance its capabilities in specific mathematical and difficulty sorting tasks.

Training Details

The model was trained with a learning rate of 1e-05 over 3.0 epochs, utilizing a distributed setup with 16 devices and a total training batch size of 96. The optimizer used was ADAMW_TORCH with standard betas and epsilon, and a cosine learning rate scheduler with a warmup ratio of 0.1 was applied. The training leveraged Transformers 4.46.1, Pytorch 2.3.0, Datasets 3.1.0, and Tokenizers 0.20.3.

Key Characteristics

• Base Model: Qwen2.5-7B-Instruct
• Fine-tuning Focus: Mathematical reasoning and difficulty sorting.

Intended Use Cases

This model is primarily intended for applications requiring specialized performance in mathematical problem-solving and tasks involving the sorting of items based on perceived difficulty, particularly within a mathematical context. Its fine-tuned nature suggests improved accuracy and relevance for these specific domains compared to a general-purpose LLM.