diff --git a/.nojekyll b/.nojekyll
index a73629108..576a9293d 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-4f9512c6
\ No newline at end of file
+c00becba
\ No newline at end of file
diff --git a/docs/api/utils.callbacks.profiler.html b/docs/api/utils.callbacks.profiler.html
index dde9921bc..4cc5a4585 100644
--- a/docs/api/utils.callbacks.profiler.html
+++ b/docs/api/utils.callbacks.profiler.html
@@ -797,6 +797,7 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>    profiler_steps_start<span class="op">=</span><span class="dv">0</span>,</span>
 <span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
 <p>PyTorch Profiler callback to create snapshots of GPU memory usage at specified steps.</p>
+<p>Also runs torch.profiler to produce a Chrome trace for timing analysis.</p>
 
 
 </section>
diff --git a/docs/custom_integrations.html b/docs/custom_integrations.html
index e8c9dac3e..18d68b382 100644
--- a/docs/custom_integrations.html
+++ b/docs/custom_integrations.html
@@ -963,7 +963,7 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
 <ul>
 <li>If you are installing from pip</li>
 </ul>
-<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb2"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="ex">pip3</span> uninstall <span class="at">-y</span> cut-cross-entropy <span class="kw">&amp;&amp;</span> <span class="ex">pip3</span> install <span class="st">"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@fa9a7fe"</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
+<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb2"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="ex">pip3</span> uninstall <span class="at">-y</span> cut-cross-entropy <span class="kw">&amp;&amp;</span> <span class="ex">pip3</span> install <span class="st">"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@63b15e6"</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
 </section>
 <section id="usage" class="level3">
 <h3 class="anchored" data-anchor-id="usage">Usage</h3>
diff --git a/examples/colab-notebooks/colab-axolotl-example.html b/examples/colab-notebooks/colab-axolotl-example.html
index 1eacd9278..6fb778c94 100644
--- a/examples/colab-notebooks/colab-axolotl-example.html
+++ b/examples/colab-notebooks/colab-axolotl-example.html
@@ -810,7 +810,7 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
 <div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>capture</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="co"># This step can take ~5-10 minutes to install dependencies</span></span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install <span class="op">--</span>no<span class="op">-</span>build<span class="op">-</span>isolation axolotl[flash<span class="op">-</span>attn]<span class="op">&gt;=</span><span class="fl">0.9.1</span></span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install <span class="st">"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@fa9a7fe"</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install <span class="st">"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@63b15e6"</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
 </div>
 <section id="demo-talk-like-a-pirate" class="level2">
 <h2 class="anchored" data-anchor-id="demo-talk-like-a-pirate">Demo: Talk Like a Pirate</h2>
diff --git a/search.json b/search.json
index 19e3e11c0..a8f8106f0 100644
--- a/search.json
+++ b/search.json
@@ -2558,7 +2558,7 @@
     "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.\n\nIf you are in dev environment\n\npython scripts/cutcrossentropy_install.py | sh\n\nIf you are installing from pip\n\npip3 uninstall -y cut-cross-entropy && pip3 install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@fa9a7fe\"\n\n\nUsage\nplugins:\n  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin\n\n\nSupported Models\n\nafmoe\napertus\narcee\ncohere\ncohere2\ndeepseek_v3\nexaone4\ngemma\ngemma2\ngemma3\ngemma3_text\ngemma3n\ngemma3n_text\nglm\nglm4\nglm4_moe\nglm4_moe_lite\nglm46v\nglm4v\nglm4v_moe\nglm_image\nglm_moe_dsa\ngpt_oss\ngranite\ngranitemoe\ngranitemoehybrid\ngranitemoeshared\nhunyuan_v1_dense\nhunyuan_v1_moe\ninternvl\nkimi_linear\nlfm2\nlfm2_moe\nlfm2_vl\nllama\nllama4\nllama4_text\nllava\nministral\nministral3\nmistral\nmistral3\nmistral4\nmixtral\nmllama\nnemotron_h\nolmo\nolmo2\nolmo3\nolmoe\nphi\nphi3\nphi4_multimodal\nqwen2\nqwen2_5_vl\nqwen2_moe\nqwen2_vl\nqwen3\nqwen3_5\nqwen3_5_text\nqwen3_5_moe\nqwen3_5_moe_text\nqwen3_moe\nqwen3_next\nqwen3_vl\nqwen3_vl_moe\nseed_oss\nsmollm3\nstep3p5\nvoxtral\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.\n\nIf you are in dev environment\n\npython scripts/cutcrossentropy_install.py | sh\n\nIf you are installing from pip\n\npip3 uninstall -y cut-cross-entropy && pip3 install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@63b15e6\"\n\n\nUsage\nplugins:\n  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin\n\n\nSupported Models\n\nafmoe\napertus\narcee\ncohere\ncohere2\ndeepseek_v3\nexaone4\ngemma\ngemma2\ngemma3\ngemma3_text\ngemma3n\ngemma3n_text\nglm\nglm4\nglm4_moe\nglm4_moe_lite\nglm46v\nglm4v\nglm4v_moe\nglm_image\nglm_moe_dsa\ngpt_oss\ngranite\ngranitemoe\ngranitemoehybrid\ngranitemoeshared\nhunyuan_v1_dense\nhunyuan_v1_moe\ninternvl\nkimi_linear\nlfm2\nlfm2_moe\nlfm2_vl\nllama\nllama4\nllama4_text\nllava\nministral\nministral3\nmistral\nmistral3\nmistral4\nmixtral\nmllama\nnemotron_h\nolmo\nolmo2\nolmo3\nolmoe\nphi\nphi3\nphi4_multimodal\nqwen2\nqwen2_5_vl\nqwen2_moe\nqwen2_vl\nqwen3\nqwen3_5\nqwen3_5_text\nqwen3_5_moe\nqwen3_5_moe_text\nqwen3_moe\nqwen3_next\nqwen3_vl\nqwen3_vl_moe\nseed_oss\nsmollm3\nstep3p5\nvoxtral\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"
@@ -4075,14 +4075,14 @@
     "href": "docs/api/utils.callbacks.profiler.html",
     "title": "utils.callbacks.profiler",
     "section": "",
-    "text": "utils.callbacks.profiler\nHF Trainer callback for creating pytorch profiling snapshots\n\n\n\n\n\nName\nDescription\n\n\n\n\nPytorchProfilerCallback\nPyTorch Profiler callback to create snapshots of GPU memory usage at specified steps.\n\n\n\n\n\nutils.callbacks.profiler.PytorchProfilerCallback(\n    steps_to_profile=5,\n    profiler_steps_start=0,\n)\nPyTorch Profiler callback to create snapshots of GPU memory usage at specified steps."
+    "text": "utils.callbacks.profiler\nHF Trainer callback for creating pytorch profiling snapshots\n\n\n\n\n\nName\nDescription\n\n\n\n\nPytorchProfilerCallback\nPyTorch Profiler callback to create snapshots of GPU memory usage at specified steps.\n\n\n\n\n\nutils.callbacks.profiler.PytorchProfilerCallback(\n    steps_to_profile=5,\n    profiler_steps_start=0,\n)\nPyTorch Profiler callback to create snapshots of GPU memory usage at specified steps.\nAlso runs torch.profiler to produce a Chrome trace for timing analysis."
   },
   {
     "objectID": "docs/api/utils.callbacks.profiler.html#classes",
     "href": "docs/api/utils.callbacks.profiler.html#classes",
     "title": "utils.callbacks.profiler",
     "section": "",
-    "text": "Name\nDescription\n\n\n\n\nPytorchProfilerCallback\nPyTorch Profiler callback to create snapshots of GPU memory usage at specified steps.\n\n\n\n\n\nutils.callbacks.profiler.PytorchProfilerCallback(\n    steps_to_profile=5,\n    profiler_steps_start=0,\n)\nPyTorch Profiler callback to create snapshots of GPU memory usage at specified steps."
+    "text": "Name\nDescription\n\n\n\n\nPytorchProfilerCallback\nPyTorch Profiler callback to create snapshots of GPU memory usage at specified steps.\n\n\n\n\n\nutils.callbacks.profiler.PytorchProfilerCallback(\n    steps_to_profile=5,\n    profiler_steps_start=0,\n)\nPyTorch Profiler callback to create snapshots of GPU memory usage at specified steps.\nAlso runs torch.profiler to produce a Chrome trace for timing analysis."
   },
   {
     "objectID": "docs/api/kernels.lora.html",
diff --git a/sitemap.xml b/sitemap.xml
index defeba787..946fa595b 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -2,954 +2,954 @@
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
   <url>
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-    <lastmod>2026-03-19T12:29:57.767Z</lastmod>
+    <lastmod>2026-03-20T03:08:46.048Z</lastmod>
   </url>
   <url>
     <loc>https://docs.axolotl.ai/src/axolotl/core/trainers/grpo/async_trainer.html</loc>
-    <lastmod>2026-03-19T12:29:57.788Z</lastmod>
+    <lastmod>2026-03-20T03:08:46.072Z</lastmod>
   </url>
   <url>
     <loc>https://docs.axolotl.ai/src/axolotl/integrations/LICENSE.html</loc>
-    <lastmod>2026-03-19T12:29:57.789Z</lastmod>
+    <lastmod>2026-03-20T03:08:46.073Z</lastmod>
   </url>
   <url>
     <loc>https://docs.axolotl.ai/docs/faq.html</loc>
-    <lastmod>2026-03-19T12:29:57.758Z</lastmod>
+    <lastmod>2026-03-20T03:08:46.036Z</lastmod>
   </url>
   <url>
     <loc>https://docs.axolotl.ai/docs/debugging.html</loc>
-    <lastmod>2026-03-19T12:29:57.758Z</lastmod>
+    <lastmod>2026-03-20T03:08:46.036Z</lastmod>
   </url>
   <url>
     <loc>https://docs.axolotl.ai/docs/models/ministral3/vision.html</loc>
-    <lastmod>2026-03-19T12:33:45.401Z</lastmod>
+    <lastmod>2026-03-20T03:12:41.324Z</lastmod>
   </url>
   <url>
     <loc>https://docs.axolotl.ai/docs/models/ministral.html</loc>
-    <lastmod>2026-03-19T12:33:45.403Z</lastmod>
+    <lastmod>2026-03-20T03:12:41.327Z</lastmod>
   </url>
   <url>
     <loc>https://docs.axolotl.ai/docs/models/qwen3.html</loc>
-    <lastmod>2026-03-19T12:33:45.406Z</lastmod>
+    <lastmod>2026-03-20T03:12:41.331Z</lastmod>
   </url>
   <url>
     <loc>https://docs.axolotl.ai/docs/models/magistral.html</loc>
-    <lastmod>2026-03-19T12:33:45.402Z</lastmod>
+    <lastmod>2026-03-20T03:12:41.326Z</lastmod>
   </url>
   <url>
     <loc>https://docs.axolotl.ai/docs/models/voxtral.html</loc>
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+    <lastmod>2026-03-20T03:12:41.328Z</lastmod>
   </url>
   <url>
     <loc>https://docs.axolotl.ai/docs/models/llama-4.html</loc>
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+    <lastmod>2026-03-20T03:12:41.330Z</lastmod>
   </url>
   <url>
     <loc>https://docs.axolotl.ai/docs/models/magistral/think.html</loc>
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   </url>
   <url>
     <loc>https://docs.axolotl.ai/docs/models/ministral3.html</loc>
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   </url>
   <url>
     <loc>https://docs.axolotl.ai/docs/models/arcee.html</loc>
-    <lastmod>2026-03-19T12:33:45.399Z</lastmod>
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   </url>
   <url>
     <loc>https://docs.axolotl.ai/docs/models/gemma3n.html</loc>
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   <url>
     <loc>https://docs.axolotl.ai/docs/models/plano.html</loc>
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   </url>
   <url>
     <loc>https://docs.axolotl.ai/docs/models/apertus.html</loc>
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   <url>
     <loc>https://docs.axolotl.ai/docs/models/hunyuan.html</loc>
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   <url>
     <loc>https://docs.axolotl.ai/docs/models/trinity.html</loc>
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   <url>
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   <url>
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     <loc>https://docs.axolotl.ai/docs/reward_modelling.html</loc>
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   <url>
     <loc>https://docs.axolotl.ai/docs/torchao.html</loc>
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   <url>
     <loc>https://docs.axolotl.ai/docs/api/utils.chat_templates.html</loc>
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   <url>
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     <loc>https://docs.axolotl.ai/docs/api/utils.schemas.utils.html</loc>
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   </url>
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