Mathoctopus/Parallel_xRFT_13B
Mathoctopus/Parallel_xRFT_13B is a 13 billion parameter LLaMA 2-based large language model developed by Mathoctopus, specifically fine-tuned for multilingual mathematical reasoning. This model is trained using a parallel-training strategy combined with multilingual rejection sampling (xRFT) on the MGSM8KInstruct Dataset, which covers ten distinct languages. It is designed to solve math problems across multiple languages, demonstrating superior performance over conventional open-source LLMs and ChatGPT in few-shot scenarios for such tasks.
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Mathoctopus/Parallel_xRFT_13B: Multilingual Math Reasoning
Mathoctopus/Parallel_xRFT_13B is a 13 billion parameter model from the MathOctopus series, built upon the LLaMA 2 architecture. Developed by Mathoctopus, this model is specifically engineered to excel in multilingual mathematical problem-solving. It leverages a unique parallel-training strategy enhanced with multilingual rejection sampling (xRFT).
Key Capabilities
- Multilingual Math Problem Solving: Trained on the extensive MGSM8KInstruct Dataset, which includes 73.6K problems across ten languages (English, Swahili, Chinese, Bengali, German, Spanish, French, Japanese, Russian, Thai).
- Enhanced Performance: Demonstrates superior performance in few-shot scenarios for multilingual math reasoning compared to conventional open-source LLMs and ChatGPT.
- Rejection Sampling: Incorporates multilingual rejection sampling (xRFT) during training to further refine its mathematical reasoning abilities.
Good For
- Educational Software: Ideal for applications requiring accurate solutions to math problems in various languages.
- Tutoring Systems: Can be integrated into AI-powered tutoring platforms to assist users with multilingual mathematical queries.
- Research in Multilingual LLMs: Useful for researchers exploring the frontiers of language models in complex reasoning tasks across diverse linguistic contexts. The model achieved an overall score of 47.6% on MGSM8K and 44.5% on MSVAMP for the 13B xRFT-Parallel variant.