diff --git a/.nojekyll b/.nojekyll
index 421b935aa..9626b1ed2 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-7e7ab175
\ No newline at end of file
+ebdd0027
\ No newline at end of file
diff --git a/docs/custom_integrations.html b/docs/custom_integrations.html
index a0abd7274..83b368bf8 100644
--- a/docs/custom_integrations.html
+++ b/docs/custom_integrations.html
@@ -452,6 +452,7 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin
   <li><a href="#liger-kernels" id="toc-liger-kernels" class="nav-link" data-scroll-target="#liger-kernels">Liger Kernels</a>
   <ul class="collapse">
   <li><a href="#usage-3" id="toc-usage-3" class="nav-link" data-scroll-target="#usage-3">Usage</a></li>
+  <li><a href="#supported-models-1" id="toc-supported-models-1" class="nav-link" data-scroll-target="#supported-models-1">Supported Models</a></li>
   <li><a href="#citation-2" id="toc-citation-2" class="nav-link" data-scroll-target="#citation-2">Citation</a></li>
   </ul></li>
   <li><a href="#language-model-evaluation-harness-lm-eval" id="toc-language-model-evaluation-harness-lm-eval" class="nav-link" data-scroll-target="#language-model-evaluation-harness-lm-eval">Language Model Evaluation Harness (LM Eval)</a>
@@ -613,6 +614,28 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin
 <span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a><span class="fu">liger_layer_norm</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
 <span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a><span class="fu">liger_fused_linear_cross_entropy</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </section>
+<section id="supported-models-1" class="level3">
+<h3 class="anchored" data-anchor-id="supported-models-1">Supported Models</h3>
+<ul>
+<li>deepseek_v2</li>
+<li>gemma</li>
+<li>gemma2</li>
+<li>gemma3 (partial support, no support for FLCE yet)</li>
+<li>granite</li>
+<li>jamba</li>
+<li>llama</li>
+<li>mistral</li>
+<li>mixtral</li>
+<li>mllama</li>
+<li>mllama_text_model</li>
+<li>olmo2</li>
+<li>paligemma</li>
+<li>phi3</li>
+<li>qwen2</li>
+<li>qwen2_5_vl</li>
+<li>qwen2_vl</li>
+</ul>
+</section>
 <section id="citation-2" class="level3">
 <h3 class="anchored" data-anchor-id="citation-2">Citation</h3>
 <div class="sourceCode" id="cb8"><pre class="sourceCode bib code-with-copy"><code class="sourceCode bibtex"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="va">@article</span>{<span class="ot">hsu2024ligerkernelefficienttriton</span>,</span>
diff --git a/search.json b/search.json
index 34ef09bfb..e6e3e9a31 100644
--- a/search.json
+++ b/search.json
@@ -2954,7 +2954,7 @@
     "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\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 (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",
     "crumbs": [
       "Advanced Features",
       "Custom Integrations"
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