UWNSL/Qwen2.5-1.5B-Instruct_Short_CoT
UWNSL/Qwen2.5-1.5B-Instruct_Short_CoT is a 1.5 billion parameter instruction-tuned language model, fine-tuned from Qwen/Qwen2.5-1.5B-Instruct. This model is specifically trained on the MATH_training_Qwen2.5-32B-Instruct dataset, indicating an optimization for mathematical reasoning tasks. It features a substantial 32768-token context length, making it suitable for processing longer mathematical problems or complex instructions. The fine-tuning process focused on improving performance in specific mathematical domains, as evidenced by its low training loss.
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Model Overview
UWNSL/Qwen2.5-1.5B-Instruct_Short_CoT is a specialized 1.5 billion parameter instruction-following language model. It is a fine-tuned variant of the base Qwen/Qwen2.5-1.5B-Instruct model, specifically adapted through training on the MATH_training_Qwen2.5-32B-Instruct dataset. This targeted fine-tuning suggests an emphasis on enhancing the model's capabilities in mathematical problem-solving and reasoning.
Key Characteristics
- Base Model: Qwen2.5-1.5B-Instruct architecture.
- Parameter Count: 1.5 billion parameters.
- Context Length: Supports a substantial 32768 tokens, allowing for detailed inputs and outputs.
- Specialized Training: Fine-tuned on a mathematical dataset, indicating a focus on numerical and logical tasks.
- Performance: Achieved a low validation loss of 0.1457 during training, suggesting effective learning on its specialized dataset.
Intended Use Cases
This model is likely best suited for applications requiring:
- Mathematical Reasoning: Solving or assisting with mathematical problems, especially those similar to the training data.
- Instruction Following: Executing complex instructions within a mathematical context.
- Long Context Processing: Handling detailed problem descriptions or multi-step mathematical derivations due to its large context window.