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.nojekyll
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docs/custom_integrations.html
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@@ -583,7 +583,7 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
<ul>
<li>If you are installing from pip</li>
</ul>
<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@48b5169"</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<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@bb8d9f8"</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</section>
<section id="usage" class="level3">
<h3 class="anchored" data-anchor-id="usage">Usage</h3>
@@ -593,6 +593,7 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
<section id="supported-models" class="level3">
<h3 class="anchored" data-anchor-id="supported-models">Supported Models</h3>
<ul>
<li>arcee</li>
<li>cohere</li>
<li>cohere2</li>
<li>gemma</li>
@@ -603,13 +604,17 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
<li>gemma3n_text</li>
<li>glm</li>
<li>glm4</li>
<li>gpt_oss</li>
<li>granite</li>
<li>granitemoe</li>
<li>hunyuan_v1_dense</li>
<li>hunyuan_v1_moe</li>
<li>llama</li>
<li>llama4</li>
<li>llama4_text</li>
<li>mistral</li>
<li>mistral3</li>
<li>mixtral</li>
<li>mllama</li>
<li>phi</li>
<li>phi3</li>

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examples/colab-notebooks/colab-axolotl-example.html
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@@ -547,7 +547,7 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
<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@48b5169"</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></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@bb8d9f8"</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></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>

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search.json
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@@ -2929,7 +2929,7 @@
"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@48b5169\"\n\n\nUsage\nplugins:\n - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin\n\n\nSupported Models\n\ncohere\ncohere2\ngemma\ngemma2\ngemma3\ngemma3_text\ngemma3n\ngemma3n_text\nglm\nglm4\ngranite\ngranitemoe\nllama\nllama4\nllama4_text\nmistral\nmistral3\nmllama\nphi\nphi3\nphi4_multimodal\nqwen2\nqwen2_vl\nqwen2_moe\nqwen2_5_vl\nqwen3\nqwen3_moe\nsmollm3\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@bb8d9f8\"\n\n\nUsage\nplugins:\n - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin\n\n\nSupported Models\n\narcee\ncohere\ncohere2\ngemma\ngemma2\ngemma3\ngemma3_text\ngemma3n\ngemma3n_text\nglm\nglm4\ngpt_oss\ngranite\ngranitemoe\nhunyuan_v1_dense\nhunyuan_v1_moe\nllama\nllama4\nllama4_text\nmistral\nmistral3\nmixtral\nmllama\nphi\nphi3\nphi4_multimodal\nqwen2\nqwen2_vl\nqwen2_moe\nqwen2_5_vl\nqwen3\nqwen3_moe\nsmollm3\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"

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