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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.07816 |
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| _version_ | 1866909134826242048 |
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| author | Sukhbaatar, Sainbayar Golovneva, Olga Sharma, Vasu Xu, Hu Lin, Xi Victoria Rozière, Baptiste Kahn, Jacob Li, Daniel Yih, Wen-tau Weston, Jason Li, Xian |
| author_facet | Sukhbaatar, Sainbayar Golovneva, Olga Sharma, Vasu Xu, Hu Lin, Xi Victoria Rozière, Baptiste Kahn, Jacob Li, Daniel Yih, Wen-tau Weston, Jason Li, Xian |
| contents | We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowledge. Our method, named Branch-Train-MiX (BTX), starts from a seed model, which is branched to train experts in embarrassingly parallel fashion with high throughput and reduced communication cost. After individual experts are asynchronously trained, BTX brings together their feedforward parameters as experts in Mixture-of-Expert (MoE) layers and averages the remaining parameters, followed by an MoE-finetuning stage to learn token-level routing. BTX generalizes two special cases, the Branch-Train-Merge method, which does not have the MoE finetuning stage to learn routing, and sparse upcycling, which omits the stage of training experts asynchronously. Compared to alternative approaches, BTX achieves the best accuracy-efficiency tradeoff. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07816 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM Sukhbaatar, Sainbayar Golovneva, Olga Sharma, Vasu Xu, Hu Lin, Xi Victoria Rozière, Baptiste Kahn, Jacob Li, Daniel Yih, Wen-tau Weston, Jason Li, Xian Computation and Language Artificial Intelligence We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowledge. Our method, named Branch-Train-MiX (BTX), starts from a seed model, which is branched to train experts in embarrassingly parallel fashion with high throughput and reduced communication cost. After individual experts are asynchronously trained, BTX brings together their feedforward parameters as experts in Mixture-of-Expert (MoE) layers and averages the remaining parameters, followed by an MoE-finetuning stage to learn token-level routing. BTX generalizes two special cases, the Branch-Train-Merge method, which does not have the MoE finetuning stage to learn routing, and sparse upcycling, which omits the stage of training experts asynchronously. Compared to alternative approaches, BTX achieves the best accuracy-efficiency tradeoff. |
| title | Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2403.07816 |