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* feat(doc): organize docs, add to menu bar, fix broken formatting * feat: add link to custom integrations * feat: update readme for integrations to include citations and repo link * chore: update lm_eval info * chore: use fullname * Update docs/cli.qmd per suggestion Co-authored-by: Dan Saunders <danjsaund@gmail.com> * feat: add sweep doc * feat: add kd doc * fix: remove toc * fix: update deprecation * feat: add more info about chat_template issues * fix: heading level * fix: shell->bash code block * fix: ray link * fix(doc): heading level, header links, formatting * feat: add grpo docs * feat: add style changes * fix: wrong cli arg for lm-eval * fix: remove old run method * feat: load custom integration doc dynamically * fix: remove old cli way * fix: toc * fix: minor formatting --------- Co-authored-by: Dan Saunders <danjsaund@gmail.com>
994 B
994 B
Liger Kernel Integration
Liger Kernel provides efficient Triton kernels for LLM training, offering:
- 20% increase in multi-GPU training throughput
- 60% reduction in memory usage
- Compatibility with both FSDP and DeepSpeed
See https://github.com/linkedin/Liger-Kernel
Usage
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Citation
@article{hsu2024ligerkernelefficienttriton,
title={Liger Kernel: Efficient Triton Kernels for LLM Training},
author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen},
year={2024},
eprint={2410.10989},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2410.10989},
journal={arXiv preprint arXiv:2410.10989},
}