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JetMoE: Reaching Llama2 Performance with 0.1M Dollars

Paper · 2024 · Yikang Shen, Zhen Guo, Tianle Cai et al.

Summary

Training a competitive large language model is usually thought to need budgets that only large companies have. Strong MoE models also rarely publish their recipes and often depend on private data. JetMoE-8B shows that a capable model can be built for under $100k, using about 30,000 H100 GPU hours and 1.25T tokens. All of that data comes from public corpora, including RefinedWeb, StarCoder data, Dolma and The Pile, plus math, code and synthetic instruction sets, arranged in a two-phase pretraining schedule. The architecture builds on ModuleFormer and uses top-k routing to sparsely activate both the attention layer and the feed-forward layer. The attention layer is a Mixture of Attention heads variant with shared key/value projections and RoPE. Only about 2B of the 8B parameters are used per token, which cuts inference compute by roughly 70% compared with Llama2-7B. The base model beats Llama2-7B on the OpenLLM Leaderboard average, and the aligned chat model scores above Llama-2-13b-chat on MT-Bench. Because the data mixtures, hyperparameters and code are all disclosed, the work gives academic groups a reproducible template for efficient open foundation models.

Key points

  • Scale and parameters: 8B total parameters with about 2.2B active per token (per Table 3). Trained on 1.25T tokens, versus 2T for Llama2-7B, DeepseekMoE and Gemma.
  • Router training: combines the standard LM loss with the Fedus et al. frequency-based load-balancing loss and the router z-loss from Zoph et al.
  • OpenLLM Leaderboard: JetMoE-8B averages 53.0, ahead of Llama2-7B (51.0), DeepseekMoE (51.1) and Gemma (46.4).
    • It leads on most tasks, e.g. GSM8k 27.8 vs 14.5 for Llama2-7B and MMLU 49.2.
    • It trails on ARC-challenge and WinoGrande.
    • It has the best MBPP score (34.2) but is weaker on HumanEval (14.6).
  • Alignment: distilled SFT on UltraChat 200k, Airoboros-3.2, Code-Feedback, Orca-math, SystemChat and Capybara, followed by dDPO on UltraFeedback. The whole process takes 60 H100 GPU hours.
    • The resulting JetMoE-8B-Chat scores 6.681 on MT-Bench, versus 6.650 for Llama-2-13b-chat.
    • It remains weak on coding and extraction compared with GPT-3.5-turbo.
  • Limitation: the budget allowed no architecture ablations, so hyperparameters and data mixtures were chosen based on prior work. Models and code are released at github.com/myshell-ai/JetMoE.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Massachusetts Institute of TechnologyCambridge · United States211 works · 69,000 citations🏢IBMArmonk · United States139 works · 70,691 citations🎓Princeton UniversityPrinceton · United States90 works · 39,109 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works

Cite

Cite as Yikang Shen, Zhen Guo, Tianle Cai and Zengyi Qin. 2024. JetMoE: Reaching Llama2 Performance with 0.1M Dollars. arXiv:2404.07413.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2404.07413 · arXiv 2404.07413

@misc{shen2024jetmoe,
  title = {{JetMoE: Reaching Llama2 Performance with 0.1M Dollars}},
  author = {Yikang Shen, Zhen Guo, Tianle Cai and Zengyi Qin},
  year = {2024},
  doi = {10.48550/arxiv.2404.07413},
  eprint = {2404.07413},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2404.07413}
}

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