Exllamav2 quant (exl2 / 2.5 bpw) made with ExLlamaV2 v0.0.21

Other EXL2 quants:

Quant	Model Size	lm_head
2.2	3250 MB	6
2.5	3479 MB	6
3.0	3893 MB	6
3.5	4311 MB	6
3.75	4519 MB	6
4.0	4726 MB	6
4.25	4935 MB	6
5.0	5558 MB	6
6.0	6495 MB	8
6.5	6912 MB	8
8.0	8106 MB	8

🦣 MAmmoTH2: Scaling Instructions from the Web

Project Page: https://tiger-ai-lab.github.io/MAmmoTH2/

Paper: https://arxiv.org/pdf/2405.03548

Code: https://github.com/TIGER-AI-Lab/MAmmoTH2

Introduction

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 34% on MATH and from 36% to 67% 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

Training Data

Please refer to https://huggingface.co/datasets/TIGER-Lab/WebInstructSub for more details.

Training Procedure

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.

Evaluation

The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:

Model	TheoremQA	MATH	GSM8K	GPQA	MMLU-ST	BBH	ARC-C	Avg
MAmmoTH2-7B (Updated)	29.0	36.7	68.4	32.4	62.4	58.6	81.7	52.7
MAmmoTH2-8B (Updated)	30.3	35.8	70.4	35.2	64.2	62.1	82.2	54.3
MAmmoTH2-8x7B	32.2	39.0	75.4	36.8	67.4	71.1	87.5	58.9
MAmmoTH2-7B-Plus (Updated)	31.2	46.0	84.6	33.8	63.8	63.3	84.4	58.1
MAmmoTH2-8B-Plus (Updated)	31.5	43.0	85.2	35.8	66.7	69.7	84.3	59.4
MAmmoTH2-8x7B-Plus	34.1	47.0	86.4	37.8	72.4	74.1	88.4	62.9

To reproduce our results, please refer to https://github.com/TIGER-AI-Lab/MAmmoTH2/tree/main/math_eval.

Usage

You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution. Check our Github repo for more advanced use: https://github.com/TIGER-AI-Lab/MAmmoTH2

Limitations

We've tried our best to build math generalist models. However, we acknowledge that the models' performance may vary based on the complexity and specifics of the math problem. Still not all mathematical fields can be covered comprehensively.

Citation

If you use the models, data, or code from this project, please cite the original paper:

@article{yue2024mammoth2,
  title={MAmmoTH2: Scaling Instructions from the Web},
  author={Yue, Xiang and Zheng, Tuney and Zhang, Ge and Chen, Wenhu},
  journal={arXiv preprint arXiv:2405.03548},
  year={2024}
}