From 17a84c24d3d95dea621dfe1dfc18943e2aeacd40 Mon Sep 17 00:00:00 2001
From: Quarto GHA Workflow Runner <quarto-github-actions-publish@example.com>
Date: Fri, 6 Mar 2026 14:26:20 +0000
Subject: [PATCH] Built site for gh-pages

---
 .nojekyll                     |   2 +-
 docs/expert_quantization.html |   1 +
 index.html                    |   2 +-
 search.json                   |   2 +-
 sitemap.xml                   | 474 +++++++++++++++++-----------------
 5 files changed, 241 insertions(+), 240 deletions(-)

diff --git a/.nojekyll b/.nojekyll
index 0901dfda8..d1a291e66 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-24230d64
\ No newline at end of file
+b1700d42
\ No newline at end of file
diff --git a/docs/expert_quantization.html b/docs/expert_quantization.html
index db1df48d7..22408a858 100644
--- a/docs/expert_quantization.html
+++ b/docs/expert_quantization.html
@@ -837,6 +837,7 @@ Note
 <section id="limitations" class="level2">
 <h2 class="anchored" data-anchor-id="limitations">Limitations</h2>
 <ul>
+<li><code>lora_target_linear</code> is not compatible with <code>quantize_moe_experts</code>. See <a href="#expert-lora-targeting">Expert LoRA targeting</a> instead.</li>
 <li><code>cpu_ram_efficient_loading</code> hangs / takes long time with FSDP2 + QLoRA.</li>
 <li>Total model parameter count may display incorrectly (trainable param count is correct).</li>
 <li>FSDP LoRA (8-bit) may have a large initial VRAM spike at the first 1-2 steps, which then drops. QLoRA does not exhibit this.</li>
diff --git a/index.html b/index.html
index daa07e547..4f3ad096c 100644
--- a/index.html
+++ b/index.html
@@ -809,7 +809,7 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
 <ul>
 <li>2026/03:
 <ul>
-<li>New model support has been added in Axolotl for Qwen3.5, Qwen3.5 MoE, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm47-flash">GLM-4.7-Flash</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm46v">GLM-4.6V</a>, and <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm45">GLM-4.5-Air</a>.</li>
+<li>New model support has been added in Axolotl for <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen3.5">Qwen3.5, Qwen3.5 MoE</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm47-flash">GLM-4.7-Flash</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm46v">GLM-4.6V</a>, and <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm45">GLM-4.5-Air</a>.</li>
 <li><a href="https://docs.axolotl.ai/docs/expert_quantization.html">MoE expert quantization</a> support (via <code>quantize_moe_experts: true</code>) greatly reduces VRAM when training MoE models (FSDP2 compat).</li>
 </ul></li>
 <li>2026/02:
diff --git a/search.json b/search.json
index 7f88f3e6f..cd25e44a6 100644
--- a/search.json
+++ b/search.json
@@ -124,7 +124,7 @@
     "href": "docs/expert_quantization.html#limitations",
     "title": "MoE Expert Quantization",
     "section": "Limitations",
-    "text": "Limitations\n\ncpu_ram_efficient_loading hangs / takes long time with FSDP2 + QLoRA.\nTotal model parameter count may display incorrectly (trainable param count is correct).\nFSDP LoRA (8-bit) may have a large initial VRAM spike at the first 1-2 steps, which then drops. QLoRA does not exhibit this.\nFSDP2 may use more VRAM per GPU than single GPU training due to not all layers being properly sharded across ranks.\nModel loading takes longer due to on-demand quantization, even on consecutive runs.\nDeepSpeed has not been tested.",
+    "text": "Limitations\n\nlora_target_linear is not compatible with quantize_moe_experts. See Expert LoRA targeting instead.\ncpu_ram_efficient_loading hangs / takes long time with FSDP2 + QLoRA.\nTotal model parameter count may display incorrectly (trainable param count is correct).\nFSDP LoRA (8-bit) may have a large initial VRAM spike at the first 1-2 steps, which then drops. QLoRA does not exhibit this.\nFSDP2 may use more VRAM per GPU than single GPU training due to not all layers being properly sharded across ranks.\nModel loading takes longer due to on-demand quantization, even on consecutive runs.\nDeepSpeed has not been tested.",
     "crumbs": [
       "Advanced Features",
       "MoE Expert Quantization"
diff --git a/sitemap.xml b/sitemap.xml
index b0c9b29db..5460d357c 100644
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