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docs/custom_integrations.html

@@ -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@bb8d9f8"</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@0ee9ee8"</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>

examples/colab-notebooks/colab-axolotl-example.html

@@ -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@bb8d9f8"</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@0ee9ee8"</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>

index.html

@@ -537,17 +537,30 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
 <section id="latest-updates" class="level2">
 <h2 class="anchored" data-anchor-id="latest-updates">🎉 Latest Updates</h2>
 <ul>
-<li>2025/07: Voxtral with mistral-common tokenizer support has been integrated in Axolotl. Read the <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/voxtral">docs</a>!</li>
-<li>2025/07: TiledMLP support for single-GPU to multi-GPU training with DDP, DeepSpeed and FSDP support has been added to support Arctic Long Sequence Training. (ALST). See <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/alst">examples</a> for using ALST with Axolotl!</li>
-<li>2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral">examples</a> to start training your own Magistral models with Axolotl!</li>
+<li>2025/07:
+<ul>
+<li>ND Parallelism support has been added into Axolotl. Compose Context Parallelism (CP), Tensor Parallelism (TP), and Fully Sharded Data Parallelism (FSDP) within a single node and across multiple nodes. Check out the <a href="https://huggingface.co/blog/accelerate-nd-parallel">blog post</a> for more info.</li>
+<li>Axolotl adds more models: <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/gpt-oss">GPT-OSS</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/gemma3n">Gemma 3n</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/lfm2">Liquid Foundation Model 2 (LFM2)</a>, and <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/afm">Arcee Foundation Models (AFM)</a>.</li>
+<li>FP8 finetuning with fp8 gather op is now possible in Axolotl via <code>torchao</code>. Get started <a href="https://docs.axolotl.ai/docs/mixed_precision.html#sec-fp8">here</a>!</li>
+<li><a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/voxtral">Voxtral</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral">Magistral 1.1</a>, and <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/devstral">Devstral</a> with mistral-common tokenizer support has been integrated in Axolotl!</li>
+<li>TiledMLP support for single-GPU to multi-GPU training with DDP, DeepSpeed and FSDP support has been added to support Arctic Long Sequence Training. (ALST). See <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/alst">examples</a> for using ALST with Axolotl!</li>
+</ul></li>
 <li>2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the <a href="https://docs.axolotl.ai/docs/qat.html">docs</a> to learn more!</li>
-<li>2025/04: Llama 4 support has been added in Axolotl. See <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/llama-4">examples</a> to start training your own Llama 4 models with Axolotl’s linearized version!</li>
 <li>2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the <a href="https://huggingface.co/blog/axolotl-ai-co/long-context-with-sequence-parallelism-in-axolotl">blog</a> and <a href="https://docs.axolotl.ai/docs/sequence_parallelism.html">docs</a> to learn how to scale your context length when fine-tuning.</li>
+</ul>
+<details>
+<summary>
+Expand older updates
+</summary>
+<ul>
+<li>2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral">examples</a> to start training your own Magistral models with Axolotl!</li>
+<li>2025/04: Llama 4 support has been added in Axolotl. See <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/llama-4">examples</a> to start training your own Llama 4 models with Axolotl’s linearized version!</li>
 <li>2025/03: (Beta) Fine-tuning Multimodal models is now supported in Axolotl. Check out the <a href="https://docs.axolotl.ai/docs/multimodal.html">docs</a> to fine-tune your own!</li>
 <li>2025/02: Axolotl has added LoRA optimizations to reduce memory usage and improve training speed for LoRA and QLoRA in single GPU and multi-GPU training (DDP and DeepSpeed). Jump into the <a href="https://docs.axolotl.ai/docs/lora_optims.html">docs</a> to give it a try.</li>
 <li>2025/02: Axolotl has added GRPO support. Dive into our <a href="https://huggingface.co/blog/axolotl-ai-co/training-llms-w-interpreter-feedback-wasm">blog</a> and <a href="https://github.com/axolotl-ai-cloud/grpo_code">GRPO example</a> and have some fun!</li>
 <li>2025/01: Axolotl has added Reward Modelling / Process Reward Modelling fine-tuning support. See <a href="https://docs.axolotl.ai/docs/reward_modelling.html">docs</a>.</li>
 </ul>
+</details>
 </section>
 <section id="overview" class="level2">
 <h2 class="anchored" data-anchor-id="overview">✨ Overview</h2>

search.json

@@ -18,7 +18,7 @@
     "href": "index.html#latest-updates",
     "title": "Axolotl",
     "section": "🎉 Latest Updates",
-    "text": "🎉 Latest Updates\n\n2025/07: Voxtral with mistral-common tokenizer support has been integrated in Axolotl. Read the docs!\n2025/07: TiledMLP support for single-GPU to multi-GPU training with DDP, DeepSpeed and FSDP support has been added to support Arctic Long Sequence Training. (ALST). See examples for using ALST with Axolotl!\n2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See examples to start training your own Magistral models with Axolotl!\n2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the docs to learn more!\n2025/04: Llama 4 support has been added in Axolotl. See examples to start training your own Llama 4 models with Axolotl’s linearized version!\n2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the blog and docs to learn how to scale your context length when fine-tuning.\n2025/03: (Beta) Fine-tuning Multimodal models is now supported in Axolotl. Check out the docs to fine-tune your own!\n2025/02: Axolotl has added LoRA optimizations to reduce memory usage and improve training speed for LoRA and QLoRA in single GPU and multi-GPU training (DDP and DeepSpeed). Jump into the docs to give it a try.\n2025/02: Axolotl has added GRPO support. Dive into our blog and GRPO example and have some fun!\n2025/01: Axolotl has added Reward Modelling / Process Reward Modelling fine-tuning support. See docs.",
+    "text": "🎉 Latest Updates\n\n2025/07:\n\nND Parallelism support has been added into Axolotl. Compose Context Parallelism (CP), Tensor Parallelism (TP), and Fully Sharded Data Parallelism (FSDP) within a single node and across multiple nodes. Check out the blog post for more info.\nAxolotl adds more models: GPT-OSS, Gemma 3n, Liquid Foundation Model 2 (LFM2), and Arcee Foundation Models (AFM).\nFP8 finetuning with fp8 gather op is now possible in Axolotl via torchao. Get started here!\nVoxtral, Magistral 1.1, and Devstral with mistral-common tokenizer support has been integrated in Axolotl!\nTiledMLP support for single-GPU to multi-GPU training with DDP, DeepSpeed and FSDP support has been added to support Arctic Long Sequence Training. (ALST). See examples for using ALST with Axolotl!\n\n2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the docs to learn more!\n2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the blog and docs to learn how to scale your context length when fine-tuning.\n\n\n\nExpand older updates\n\n\n2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See examples to start training your own Magistral models with Axolotl!\n2025/04: Llama 4 support has been added in Axolotl. See examples to start training your own Llama 4 models with Axolotl’s linearized version!\n2025/03: (Beta) Fine-tuning Multimodal models is now supported in Axolotl. Check out the docs to fine-tune your own!\n2025/02: Axolotl has added LoRA optimizations to reduce memory usage and improve training speed for LoRA and QLoRA in single GPU and multi-GPU training (DDP and DeepSpeed). Jump into the docs to give it a try.\n2025/02: Axolotl has added GRPO support. Dive into our blog and GRPO example and have some fun!\n2025/01: Axolotl has added Reward Modelling / Process Reward Modelling fine-tuning support. See docs.",
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@@ -2929,7 +2929,7 @@
     "href": "docs/custom_integrations.html#cut-cross-entropy",
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     "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@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",
+    "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@0ee9ee8\"\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",
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