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2025-04-28 16:21:02 +00:00
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docs/custom_integrations.html
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@@ -539,8 +539,8 @@ 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>llama4_text</li>
<li>mllama</li>
<li>phi3</li>
<li>gemma</li>
@@ -550,6 +550,11 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin
<li>mistral</li>
<li>mistral3</li>
<li>qwen2</li>
<li>qwen2_moe</li>
<li>qwen2_vl</li>
<li>qwen2_5_vl</li>
<li>qwen3</li>
<li>qwen3_moe</li>
<li>cohere</li>
<li>cohere2</li>
<li>glm</li>

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search.json
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@@ -1561,7 +1561,7 @@
"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/apple/ml-cross-entropy.git@bad6f7b49c75fdec69471abb71b4cddd0f0c6438\"\n\n\nUsage\nplugins:\n - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin\n\n\nSupported Models\n\nllama\nllama4_text\nllama4\nmllama\nphi3\ngemma\ngemma2\ngemma3\ngemma3_text\nmistral\nmistral3\nqwen2\ncohere\ncohere2\nglm\nglm4\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/apple/ml-cross-entropy.git@bad6f7b49c75fdec69471abb71b4cddd0f0c6438\"\n\n\nUsage\nplugins:\n - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin\n\n\nSupported Models\n\nllama\nllama4\nllama4_text\nmllama\nphi3\ngemma\ngemma2\ngemma3\ngemma3_text\nmistral\nmistral3\nqwen2\nqwen2_moe\nqwen2_vl\nqwen2_5_vl\nqwen3\nqwen3_moe\ncohere\ncohere2\nglm\nglm4\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",
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