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metadata 
license: apache-2.0
datasets:
  - Mathoctopus/GSM8KInstruct_Parallel
language:
  - en
  - es
  - zh
  - de
  - ru
  - th
  - sw
  - ja
  - fr
  - bn

Introduction

We introduce 🐙 MathOctopus, a series of open-source large language models (LLMs) specifically tailored for multilingual math problem-solving. The MathOctopus models are trained on 🤗 MGSM8KInstruct Dataset, encompassing ten distinct languages. MathOctopus notably outperforms conventional open-source LLMs and exhibits superiority over ChatGPT in few-shot scenarios.

Datasets

MGSM8KInstruct

Training Dataset En Sw Zh Bn De Es Fr Ja Ru Th Overall
MGSM8KInstruct 7473 7472 7466 6539 7466 7470 7469 7471 7361 7473 73.6K

MSVAMP

Test Dataset En Sw Zh Bn De Es Fr Ja Ru Th Overall
MSVAMP 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 10K

Usage

Our dataset and models are all available at Huggingface.

🤗 MGSM8KInstruct_Parallel Dataset

🤗 MSVAMP Dataset

Models

Base Model: LLama Parallel-Training Cross-Training
7B-LLaMA 2 🐙 MathOctopus-Parallel-7B 🐙 MathOctopus-Cross-7B
🐙MathOctopus-Parallel-xRFT-7B 🐙MathOctopus-Cross-xRFT-7B
13B-LLaMA 2 🐙 [MathOctopus-Parallel-13B] 🐙 [MathOctopus-Cross-13B]
🐙MathOctopus-Parallel-xRFT-13B 🐙[MathOctopus-Cross-xRFT-13B]
33B-LLaMA 1 🐙 [MathOctopus-Parallel-33B] 🐙 [MathOctopus-Cross-33B]
70B-LLaMA 2 Coming soon! Coming Soon!

*-Parallel refers to our model trained with the parallel-training strategy.

*-Cross refers to our model trained with cross-training strategy.

*-xRFT means we train the model with multilingual rejection sampling.

Overall Results on MGSM

7B Model En Sw Zh Bn De Es Fr Ja Ru Th Overall
MathOctopusC 52.0 23.6 31.6 18.8 38.0 39.2 36.4 27.2 33.6 21.6 32.2
xRFT-MathOctopusC 51.2 24.0 33.2 18.8 36.0 41.2 37.6 29.6 36.4 25.2 33.3
MathOctopusP-LoRA 30.4 15.2 23.6 10.4 22.8 24.8 26.4 18.0 22.0 14.8 20.8
MathOctopusP 52.4 39.2 38.4 28.8 44.8 42.4 43.6 36.0 39.6 34.4 40.0
xRFT-MathOctopusP 54.8 38.4 45.2 33.2 43.6 45.2 38.0 35.6 48.4 36.4 41.9

13B Model En Sw Zh Bn De Es Fr Ja Ru Th Overall
MathOctopusC 56.4 27.2 39.2 24.0 47.6 49.6 47.6 40.4 42.0 24.8 39.9
xRFT-MathOctopusC 53.6 28.0 45.2 21.2 48.0 46.4 46.0 35.2 45.6 28.8 39.8
MathOctopusP 53.2 42.8 48.8 35.2 44.4 48.0 48.4 43.2 47.6 46.8 45.8
xRFT-MathOctopusP 51.6 46.0 51.2 42.0 49.2 53.2 49.6 39.6 47.6 46.0 47.6

30-34B Model En Sw Zh Bn De Es Fr Ja Ru Th Overall
MathOctopusC 55.6 24.4 36.0 19.2 40.4 51.2 44.4 27.2 37.2 21.6 35.7
xRFT-MathOctopusC 53.6 27.6 34.4 19.2 47.2 47.6 44.8 30.8 38.8 22.8 36.7
MathOctopusP 56.4 46.8 52.0 35.2 47.2 53.2 48.0 39.2 45.6 41.2 46.5
xRFT-MathOctopusP 51.6 47.2 52.4 37.6 51.2 52.8 44.4 41.6 50.0 47.6 47.6

Overall Results on MSVAMP

7B Model En Sw Zh Bn De Es Fr Ja Ru Th Overall
MathOctopusC 49.2 36.6 43.6 30.2 48.6 46.8 46.4 42.5 46.7 34.0 42.5
xRFT-MathOctopusC 49.9 37.7 43.3 32.9 46.5 47.6 47.3 42.7 46.6 36.2 43.1
MathOctopusP-LoRA 30.4 15.2 23.6 10.4 22.8 24.8 26.4 18.0 22.0 14.8 20.8
MathOctopusP 46.5 40.1 42.5 29.1 43.5 45.4 46.0 42.5 45.4 35.7 41.7
xRFT-MathOctopusP 46.8 42.3 43.2 32.8 43.1 44.5 45.3 43.2 42.1 40.5 42.4

13B Model En Sw Zh Bn De Es Fr Ja Ru Th Overall
MathOctopusC 56.6 40.4 49.0 30.3 50.9 54.2 54.7 46.3 52.4 35.7 47.1
xRFT-MathOctopusC 52.9 41.9 49.2 34.1 50.5 52.8 51.5 45.8 50.2 35.7 46.5
MathOctopusP 50.7 43.4 42.6 31.8 48.4 49.4 50.6 41.1 46.9 39.3 44.4
xRFT-MathOctopusP 44.6 43.4 46.4 34.2 47.7 48.2 49.9 43.1 48.2 39.5 44.5

30-34B Model En Sw Zh Bn De Es Fr Ja Ru Th Overall
MathOctopusC 51.5 42.1 46.2 23.2 50.5 52.1 52.9 42.2 50.5 33.4 44.5
xRFT-MathOctopusC 48.1 42.8 43.6 23.3 48.7 50.0 48.9 43.4 44.6 35.5 42.9
MathOctopusP 56.4 46.8 52.0 35.2 47.2 53.2 48.0 39.2 45.6 41.2 46.5
xRFT-MathOctopusP 48.0 42.3 46.1 36.2 47.5 48.5 48.3 45.8 47.2 41.2 45.1

MathOctopus in English

Models GSM8K SVAMP
LLaMA 2-7B 42.4 38.3
MathOctopusP-7B 49.3 46.8
MathOctopusC-7B 50.8 49.3
LLaMA 2-13B 51.0 50.9
MathOctopusP-13B 55.5 52.1
MathOctopusC-13B 56.6 56.6
LLaMA 1-33B 50.0 49.0
MathOctopusP-33B 56.0 52.5
MathOctopusC-33B 53.7 51.5

Intended Uses

These models are trained for research purposes. They are designed to solve multilingual math problems. They can be used in educational software, tutoring systems, or any application where a solution to a math problem is needed.