xw1234gan/Merging_Prob_Qwen2.5-7B-Instruct_MATH_lr1e-05_mb2_ga128_n2048_seed42
The xw1234gan/Merging_Prob_Qwen2.5-7B-Instruct_MATH_lr1e-05_mb2_ga128_n2048_seed42 is a 7.6 billion parameter instruction-tuned model based on the Qwen2.5 architecture. This model is specifically fine-tuned for mathematical problem-solving, leveraging a learning rate of 1e-05, a micro-batch size of 2, and a gradient accumulation of 128. It is designed to excel in complex quantitative reasoning tasks, making it suitable for applications requiring high accuracy in mathematical contexts.
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Overview
This model, xw1234gan/Merging_Prob_Qwen2.5-7B-Instruct_MATH_lr1e-05_mb2_ga128_n2048_seed42, is a 7.6 billion parameter instruction-tuned variant of the Qwen2.5 architecture. While specific training data and detailed performance metrics are not provided in the model card, its naming convention strongly suggests a specialization in mathematical problem-solving.
Key Characteristics
- Base Model: Qwen2.5-7B-Instruct
- Parameter Count: 7.6 billion parameters
- Context Length: 32768 tokens
- Fine-tuning Focus: Implied specialization in mathematical reasoning, indicated by "MATH" in the model name.
- Training Hyperparameters: Fine-tuned with a learning rate of 1e-05, a micro-batch size of 2, and a gradient accumulation of 128, suggesting a focused optimization strategy.
Potential Use Cases
Given its apparent specialization, this model is likely optimized for:
- Solving complex mathematical equations and word problems.
- Assisting in quantitative analysis and data interpretation.
- Educational tools for mathematics.
- Applications requiring precise numerical reasoning.