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Computer Science > Machine Learning

arXiv:2505.17272 (cs)
[Submitted on 22 May 2025]

Title:Zebra-Llama: Towards Extremely Efficient Hybrid Models

Authors:Mingyu Yang, Mehdi Rezagholizadeh, Guihong Li, Vikram Appia,
Emad Barsoum
View a PDF of the paper titled Zebra-Llama: Towards Extremely
Efficient Hybrid Models, by Mingyu Yang and 4 other authors
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    Abstract:With the growing demand for deploying large language
    models (LLMs) across diverse applications, improving their
    inference efficiency is crucial for sustainable and democratized
    access. However, retraining LLMs to meet new user-specific
    requirements is prohibitively expensive and environmentally
    unsustainable. In this work, we propose a practical and scalable
    alternative: composing efficient hybrid language models from
    existing pre-trained models. Our approach, Zebra-Llama,
    introduces a family of 1B, 3B, and 8B hybrid models by combining
    State Space Models (SSMs) and Multi-head Latent Attention (MLA)
    layers, using a refined initialization and post-training pipeline
    to efficiently transfer knowledge from pre-trained Transformers.
    Zebra-Llama achieves Transformer-level accuracy with near-SSM
    efficiency using only 7-11B training tokens (compared to
    trillions of tokens required for pre-training) and an 8B teacher.
    Moreover, Zebra-Llama dramatically reduces KV cache size -down to
    3.9%, 2%, and 2.73% of the original for the 1B, 3B, and 8B
    variants, respectively-while preserving 100%, 100%, and >97% of
    average zero-shot performance on LM Harness tasks. Compared to
    models like MambaInLLaMA, X-EcoMLA, Minitron, and Llamba,
    Zebra-Llama consistently delivers competitive or superior
    accuracy while using significantly fewer tokens, smaller
    teachers, and vastly reduced KV cache memory. Notably,
    Zebra-Llama-8B surpasses Minitron-8B in few-shot accuracy by 7%
    while using 8x fewer training tokens, over 12x smaller KV cache,
    and a smaller teacher (8B vs. 15B). It also achieves 2.6x-3.8x
    higher throughput (tokens/s) than MambaInLlama up to a 32k
    context length. We will release code and model checkpoints upon
    acceptance.

Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL)
Cite as:  arXiv:2505.17272 [cs.LG]
          (or arXiv:2505.17272v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2505.17272
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Mehdi Rezagholizadeh [view email]
[v1] Thu, 22 May 2025 20:39:57 UTC (12,646 KB)
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