diff --git a/.nojekyll b/.nojekyll
index 7daede940..44659a03a 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-293e0ad4
\ No newline at end of file
+c64b1d3f
\ No newline at end of file
diff --git a/docs/custom_integrations.html b/docs/custom_integrations.html
index 4633d43f8..31d3a135d 100644
--- a/docs/custom_integrations.html
+++ b/docs/custom_integrations.html
@@ -534,6 +534,9 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin
 <h3 class="anchored" data-anchor-id="supported-models">Supported Models</h3>
 <ul>
 <li>llama</li>
+<li>llama4_text</li>
+<li>llama4</li>
+<li>mllama</li>
 <li>phi3</li>
 <li>gemma</li>
 <li>gemma2</li>
diff --git a/search.json b/search.json
index 1640867b8..4a2d91753 100644
--- a/search.json
+++ b/search.json
@@ -3026,7 +3026,7 @@
     "href": "docs/custom_integrations.html#cut-cross-entropy",
     "title": "Custom Integrations",
     "section": "Cut Cross Entropy",
-    "text": "Cut Cross Entropy\nCut Cross Entropy (CCE) reduces VRAM usage through optimization on the cross-entropy operation during loss calculation.\nSee https://github.com/apple/ml-cross-entropy\n\nRequirements\n\nPyTorch 2.4.0 or higher\n\n\n\nInstallation\nRun the following command to install cut_cross_entropy[transformers] if you don’t have it already.\npython scripts/cutcrossentropy_install.py | sh\n\npip3 uninstall -y cut-cross-entropy && pip3 install \"cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@24fbe4b5dab9a6c250a014573613c1890190536c\"\n\n\nUsage\nplugins:\n  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin\n\ncut_cross_entropy: true\n\n\nSupported Models\n\nllama\nphi3\ngemma\ngemma2\ngemma3\ngemma3_text\nmistral\nmistral3\nqwen2\ncohere\ncohere2\n\n\n\nCitation\n@article{wijmans2024cut,\n  author       = {Erik Wijmans and\n                  Brody Huval and\n                  Alexander Hertzberg and\n                  Vladlen Koltun and\n                  Philipp Kr\\\"ahenb\\\"uhl},\n  title        = {Cut Your Losses in Large-Vocabulary Language Models},\n  journal      = {arXiv},\n  year         = {2024},\n  url          = {https://arxiv.org/abs/2411.09009},\n}\nPlease see reference here",
+    "text": "Cut Cross Entropy\nCut Cross Entropy (CCE) reduces VRAM usage through optimization on the cross-entropy operation during loss calculation.\nSee https://github.com/apple/ml-cross-entropy\n\nRequirements\n\nPyTorch 2.4.0 or higher\n\n\n\nInstallation\nRun the following command to install cut_cross_entropy[transformers] if you don’t have it already.\npython scripts/cutcrossentropy_install.py | sh\n\npip3 uninstall -y cut-cross-entropy && pip3 install \"cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@24fbe4b5dab9a6c250a014573613c1890190536c\"\n\n\nUsage\nplugins:\n  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin\n\ncut_cross_entropy: true\n\n\nSupported Models\n\nllama\nllama4_text\nllama4\nmllama\nphi3\ngemma\ngemma2\ngemma3\ngemma3_text\nmistral\nmistral3\nqwen2\ncohere\ncohere2\n\n\n\nCitation\n@article{wijmans2024cut,\n  author       = {Erik Wijmans and\n                  Brody Huval and\n                  Alexander Hertzberg and\n                  Vladlen Koltun and\n                  Philipp Kr\\\"ahenb\\\"uhl},\n  title        = {Cut Your Losses in Large-Vocabulary Language Models},\n  journal      = {arXiv},\n  year         = {2024},\n  url          = {https://arxiv.org/abs/2411.09009},\n}\nPlease see reference here",
     "crumbs": [
       "Advanced Features",
       "Custom Integrations"
diff --git a/sitemap.xml b/sitemap.xml
index f863e6a88..508f3d178 100644
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