Abhiram1009/qwen2.5-0.5B-math-v2
Abhiram1009/qwen2.5-0.5B-math-v2 is a 0.5 billion parameter language model, fine-tuned from Abhiram1009/qwen2.5-0.5B-math-tuned using the TRL framework. This model is specifically optimized for mathematical reasoning and related tasks, building upon its math-tuned base. It offers a compact solution for applications requiring numerical and logical processing within a 32768 token context length.
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Model Overview
Abhiram1009/qwen2.5-0.5B-math-v2 is a 0.5 billion parameter language model that has been fine-tuned from the existing Abhiram1009/qwen2.5-0.5B-math-tuned base. This iteration focuses on enhancing its capabilities through further training using the TRL (Transformer Reinforcement Learning) framework.
Key Capabilities
- Mathematical Reasoning: Built upon a math-tuned predecessor, this model is designed to excel in tasks requiring numerical understanding and logical problem-solving.
- Compact Size: With 0.5 billion parameters, it offers a lightweight solution suitable for deployment in environments with resource constraints.
- Fine-tuned Performance: The model leverages the TRL framework for its training, indicating a focus on refining its responses and performance for specific applications.
Training Details
The model underwent training using the Supervised Fine-Tuning (SFT) method. The development utilized several key framework versions:
- TRL: 0.19.0
- Transformers: 4.53.0
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.2
Good For
- Applications requiring efficient mathematical problem-solving.
- Integration into systems where a smaller model footprint is advantageous.
- Tasks benefiting from a model specifically refined for numerical and logical operations.