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pipo-hayula-inference-paper
hayulalab/pipo-hayula-inference-paper
pipo-hayula-inference-paper is a machine learning model from hayulalab. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
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Updated Jul 8, 2026
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- License
- mit
- Access
- Public
- Created
- Jul 8, 2026
- Updated
- Jul 8, 2026
- SHA
- e6e0ad3e
Model card
From the Hugging Face model README
Hayula Research Paper — Hayula Research
Hayula AI Lab
Abstract
Large language model (LLM) inference on consumer-grade hardware remains bottlenecked by autoregressive decoding, where each token requires a separate forward pass through the model. Speculative decoding mitigates this by using a small draft model to propose multiple tokens per step, but introduces a verifier overhead that can negate gains on memory-bandwidth-constrained systems such as Apple Silicon. We present PIPO-Hayula, an implementation of the Pair-In-Pair-Out (PIPO) multi-token prediction
Files
| File | Description |
|---|---|
paper.md | Full paper (Markdown) |
README.md | This model card |
Citation
@techreport{hayulalab2026pipohayulainference,
title={Hayula Research Paper — Hayula Research},
author={Hayula AI Lab},
year={2026},
url={https://huggingface.co/hayulalab/pipo-hayula-inference-paper}
}
hayulalab — Open Source AI Research
- License
- mit
- Created
- Jul 8, 2026
- Updated
- Jul 8, 2026