From e9f1cda1fc36083c4eacc2469f8b5d619ccc4b75 Mon Sep 17 00:00:00 2001
From: Quarto GHA Workflow Runner <quarto-github-actions-publish@example.com>
Date: Tue, 18 Nov 2025 07:51:37 +0000
Subject: [PATCH] Built site for gh-pages

---
 .nojekyll   |   2 +-
 index.html  |   5 +-
 search.json |   2 +-
 sitemap.xml | 400 ++++++++++++++++++++++++++--------------------------
 4 files changed, 206 insertions(+), 203 deletions(-)

diff --git a/.nojekyll b/.nojekyll
index 390835073..53344d47b 100644
--- a/.nojekyll
+++ b/.nojekyll
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diff --git a/index.html b/index.html
index 141c55b76..72886705f 100644
--- a/index.html
+++ b/index.html
@@ -564,6 +564,9 @@ 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>2025/10: New model support has been added in Axolotl for: <a href="https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/qwen3-next">Qwen3 Next</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen2_5-vl">Qwen2.5-vl, Qwen3-vl</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen3">Qwen3, Qwen3MoE</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/granite4">Granite 4</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/hunyuan">HunYuan</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral#vision">Magistral 2509</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/apertus">Apertus</a>, and <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/seed-oss">Seed-OSS</a>.</li>
+<li>2025/09: Axolotl now has text diffusion training. Read more <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/diffusion">here</a>.</li>
+<li>2025/08: QAT has been updated to include NVFP4 support. See <a href="https://github.com/axolotl-ai-cloud/axolotl/pull/3107">PR</a>.</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>
@@ -573,13 +576,13 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
 <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>2025/03: 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>2025/03: 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>
 <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>
diff --git a/search.json b/search.json
index c49eb8021..596794572 100644
--- a/search.json
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@@ -2030,7 +2030,7 @@
     "href": "index.html#latest-updates",
     "title": "Axolotl",
     "section": "🎉 Latest Updates",
-    "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.",
+    "text": "🎉 Latest Updates\n\n2025/10: New model support has been added in Axolotl for: Qwen3 Next, Qwen2.5-vl, Qwen3-vl, Qwen3, Qwen3MoE, Granite 4, HunYuan, Magistral 2509, Apertus, and Seed-OSS.\n2025/09: Axolotl now has text diffusion training. Read more here.\n2025/08: QAT has been updated to include NVFP4 support. See PR.\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!\n\n\n\nExpand older updates\n\n\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.\n2025/06: 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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