From bc807ddaeea751a93222773780638ed392cc3e84 Mon Sep 17 00:00:00 2001 From: Quarto GHA Workflow Runner Date: Fri, 18 Apr 2025 16:59:56 +0000 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- docs/custom_integrations.html | 2 +- search.json | 2 +- sitemap.xml | 340 +++++++++++++++++----------------- 4 files changed, 173 insertions(+), 173 deletions(-) diff --git a/.nojekyll b/.nojekyll index 2a144add9..9779cb8f1 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -3eb90e3e \ No newline at end of file +8d85df4a \ No newline at end of file diff --git a/docs/custom_integrations.html b/docs/custom_integrations.html index f67c90d90..dae266eed 100644 --- a/docs/custom_integrations.html +++ b/docs/custom_integrations.html @@ -640,7 +640,7 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin
  • deepseek_v2
  • gemma
  • gemma2
    • -
  • gemma3 (partial support, no support for FLCE yet)
    • +
  • gemma3
  • granite
  • jamba
  • llama
    • diff --git a/search.json b/search.json index 3621bd8a5..315cdecf5 100644 --- a/search.json +++ b/search.json @@ -3066,7 +3066,7 @@ "href": "docs/custom_integrations.html#liger-kernels", "title": "Custom Integrations", "section": "Liger Kernels", - "text": "Liger Kernels\nLiger Kernel provides efficient Triton kernels for LLM training, offering:\n\n20% increase in multi-GPU training throughput\n60% reduction in memory usage\nCompatibility with both FSDP and DeepSpeed\n\nSee https://github.com/linkedin/Liger-Kernel\n\nUsage\nplugins:\n - axolotl.integrations.liger.LigerPlugin\nliger_rope: true\nliger_rms_norm: true\nliger_glu_activation: true\nliger_layer_norm: true\nliger_fused_linear_cross_entropy: true\n\n\nSupported Models\n\ndeepseek_v2\ngemma\ngemma2\ngemma3 (partial support, no support for FLCE yet)\ngranite\njamba\nllama\nmistral\nmixtral\nmllama\nmllama_text_model\nolmo2\npaligemma\nphi3\nqwen2\nqwen2_5_vl\nqwen2_vl\n\n\n\nCitation\n@article{hsu2024ligerkernelefficienttriton,\n title={Liger Kernel: Efficient Triton Kernels for LLM Training},\n 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},\n year={2024},\n eprint={2410.10989},\n archivePrefix={arXiv},\n primaryClass={cs.LG},\n url={https://arxiv.org/abs/2410.10989},\n journal={arXiv preprint arXiv:2410.10989},\n}\nPlease see reference here", + "text": "Liger Kernels\nLiger Kernel provides efficient Triton kernels for LLM training, offering:\n\n20% increase in multi-GPU training throughput\n60% reduction in memory usage\nCompatibility with both FSDP and DeepSpeed\n\nSee https://github.com/linkedin/Liger-Kernel\n\nUsage\nplugins:\n - axolotl.integrations.liger.LigerPlugin\nliger_rope: true\nliger_rms_norm: true\nliger_glu_activation: true\nliger_layer_norm: true\nliger_fused_linear_cross_entropy: true\n\n\nSupported Models\n\ndeepseek_v2\ngemma\ngemma2\ngemma3\ngranite\njamba\nllama\nmistral\nmixtral\nmllama\nmllama_text_model\nolmo2\npaligemma\nphi3\nqwen2\nqwen2_5_vl\nqwen2_vl\n\n\n\nCitation\n@article{hsu2024ligerkernelefficienttriton,\n title={Liger Kernel: Efficient Triton Kernels for LLM Training},\n 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},\n year={2024},\n eprint={2410.10989},\n archivePrefix={arXiv},\n primaryClass={cs.LG},\n url={https://arxiv.org/abs/2410.10989},\n journal={arXiv preprint arXiv:2410.10989},\n}\nPlease see reference here", "crumbs": [ "Advanced Features", "Custom Integrations" diff --git a/sitemap.xml b/sitemap.xml index cb75db15b..e7aac27ce 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,682 +2,682 @@ https://docs.axolotl.ai/examples/colab-notebooks/colab-axolotl-example.html - 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