rishiraj/zephyr-math
rishiraj/zephyr-math is a 7 billion parameter language model developed by Rishiraj Acharya, fine-tuned from HuggingFaceH4/zephyr-7b-alpha. This model is specifically optimized for mathematical reasoning and problem-solving, having been trained on the MetaMathQA dataset. It aims to achieve state-of-the-art results on benchmarks like GSM8k Pass@1, making it suitable for applications requiring strong mathematical capabilities.
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Zephyr Math 7B: Specialized for Mathematical Reasoning
Zephyr Math 7B, developed by Rishiraj Acharya, is a 7 billion parameter language model fine-tuned from the powerful HuggingFaceH4/zephyr-7b-alpha architecture. Its primary distinction lies in its specialized training on the MetaMathQA dataset, making it highly proficient in mathematical reasoning and problem-solving tasks.
Key Capabilities & Features
- Mathematical Proficiency: Optimized for complex mathematical queries and achieving high scores on benchmarks like GSM8k Pass@1.
- Fine-tuned for Accuracy: Utilizes a carefully preprocessed dataset derived from MetaMathQA, formatted for optimal training with AutoTrain Advanced.
- Performance Focus: Aims for state-of-the-art results in mathematical benchmarks, as evidenced by its comparative performance against other LLMs on GSM8k and MATH Pass@1.
- Efficient Training: Leveraged an A100 GPU and specific hyperparameters (e.g.,
learning_rate = 2e-5,num_epochs = 3,use_peft = True,use_int4 = True) for effective fine-tuning.
Good For
- Applications requiring strong mathematical problem-solving abilities.
- Tasks involving quantitative reasoning and logical deduction.
- Use cases where accuracy on math benchmarks like GSM8k is critical.