MAmmoTH2-8B-Plus

Introducing 🦣 MAmmoTH2, a game-changer in improving the reasoning abilities of large language models (LLMs) through innovative instruction tuning. By efficiently harvesting 10 million instruction-response pairs from the pre-training web corpus, we've developed MAmmoTH2 models that significantly boost performance on reasoning benchmarks. For instance, MAmmoTH2-7B (Mistral) sees its performance soar from 11% to 36.7% on MATH and from 36% to 68.4% on GSM8K, all without training on any domain-specific data. Further training on public instruction tuning datasets yields MAmmoTH2-Plus, setting new standards in reasoning and chatbot benchmarks. Our work presents a cost-effective approach to acquiring large-scale, high-quality instruction data, offering a fresh perspective on enhancing LLM reasoning abilities. | | **Base Model** | **MAmmoTH2** | **MAmmoTH2-Plus** | |:-----|:---------------------|:-------------------------------------------------------------------|:------------------------------------------------------------------| | 7B | Mistral | 🦣 MAmmoTH2-7B | 🦣 MAmmoTH2-7B-Plus | | 8B | Llama-3 | 🦣 MAmmoTH2-8B | 🦣 MAmmoTH2-8B-Plus | | 8x7B | Mixtral | 🦣 MAmmoTH2-8x7B | 🦣 MAmmoTH2-8x7B-Plus | Please refer to https://huggingface.co/datasets/TIGER-Lab/WebInstructSub for more details. The models are fine-tuned with the WEBINSTRUCT dataset using the original Llama-3, Mistral and Mistal models as base models. The training procedure varies for different models based on their sizes. Check out our paper for more details. The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:

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