cutelemonlili/Qwen2.5-Coder-1.5B-Instruct_MATH_training_Qwen_QwQ_32B_Preview
cutelemonlili/Qwen2.5-Coder-1.5B-Instruct_MATH_training_Qwen_QwQ_32B_Preview is a 1.5 billion parameter language model, fine-tuned from Qwen/Qwen2.5-Coder-1.5B-Instruct. This model is specifically optimized for mathematical tasks, leveraging a 32K context length. It is designed to enhance performance in mathematical reasoning and problem-solving, building upon the Qwen2.5-Coder architecture.
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Model Overview
This model, cutelemonlili/Qwen2.5-Coder-1.5B-Instruct_MATH_training_Qwen_QwQ_32B_Preview, is a specialized fine-tuned version of the Qwen/Qwen2.5-Coder-1.5B-Instruct base model. With 1.5 billion parameters and a 32,768 token context length, it is specifically adapted for mathematical applications.
Key Characteristics
- Base Model: Fine-tuned from Qwen/Qwen2.5-Coder-1.5B-Instruct, indicating a foundation in code generation and instruction following.
- Specialization: The model's name and training dataset (
MATH_training_Qwen_QwQ_32B_Preview) strongly suggest an optimization for mathematical reasoning and problem-solving tasks. - Training Details: Trained for 2 epochs with a learning rate of 1e-05, using a multi-GPU setup. The training loss reached 0.2983, with a validation loss of 0.3738.
Intended Use Cases
Given its fine-tuning on a math-specific dataset, this model is likely best suited for:
- Mathematical Problem Solving: Assisting with various mathematical challenges.
- Educational Tools: Potentially useful in applications requiring mathematical explanations or solutions.
- Code Generation for Math: Leveraging its coder base for generating code related to mathematical computations or algorithms.