AbhilekhMeda/qwen2.5-1.5b-numinamath-sft
AbhilekhMeda/qwen2.5-1.5b-numinamath-sft is a 1.5 billion parameter language model, fine-tuned from Qwen/Qwen2.5-1.5B-Instruct using the TRL framework. This model is specifically optimized for mathematical and reasoning tasks, leveraging Supervised Fine-Tuning (SFT) to enhance its capabilities in these domains. With a context length of 32768 tokens, it is designed for applications requiring robust numerical and logical processing.
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Overview
AbhilekhMeda/qwen2.5-1.5b-numinamath-sft is a 1.5 billion parameter language model, fine-tuned from the base Qwen/Qwen2.5-1.5B-Instruct model. This model has undergone Supervised Fine-Tuning (SFT) using the TRL library, specifically targeting mathematical and reasoning capabilities. It maintains a substantial context length of 32768 tokens, making it suitable for complex problem-solving scenarios.
Key Capabilities
- Enhanced Mathematical Reasoning: Optimized through SFT for improved performance on numerical and logical tasks.
- Instruction Following: Builds upon the instruction-tuned base model, retaining strong general instruction adherence.
- Efficient Processing: At 1.5 billion parameters, it offers a balance between capability and computational efficiency.
Good for
- Mathematical Problem Solving: Ideal for applications requiring accurate numerical computations and logical deductions.
- Reasoning Tasks: Suitable for scenarios where the model needs to understand and apply logical principles.
- Educational Tools: Can be integrated into systems for generating explanations or solving math-related queries.
- Research and Development: A strong base for further fine-tuning on specific mathematical or scientific datasets.