From 109cae8122038125fc4e4dd23f091b51d98efb93 Mon Sep 17 00:00:00 2001
From: Quarto GHA Workflow Runner <quarto-github-actions-publish@example.com>
Date: Wed, 19 Feb 2025 14:24:39 +0000
Subject: [PATCH] Built site for gh-pages

---
 .nojekyll             |  2 +-
 docs/lora_optims.html |  9 ++++-
 search.json           |  2 +-
 sitemap.xml           | 78 +++++++++++++++++++++----------------------
 4 files changed, 49 insertions(+), 42 deletions(-)

diff --git a/.nojekyll b/.nojekyll
index c29793c81..5d86df9ad 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-6e8747fa
\ No newline at end of file
+2adccae7
\ No newline at end of file
diff --git a/docs/lora_optims.html b/docs/lora_optims.html
index 4dd6e9b00..27df91675 100644
--- a/docs/lora_optims.html
+++ b/docs/lora_optims.html
@@ -369,7 +369,14 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin
 
 
 <p>Inspired by <a href="https://github.com/unslothai/unsloth">Unsloth</a>, we’ve implemented two optimizations for LoRA and QLoRA fine-tuning, supporting both single GPU and multi-GPU (in the DDP and DeepSpeed settings) training. These include (1) SwiGLU and GEGLU activation function Triton kernels, and (2) LoRA MLP and attention custom autograd functions. Our goal was to leverage operator fusion and tensor re-use in order to improve speed and reduce memory usage during the forward and backward passes of these calculations.</p>
-<p>We currently support several common model architectures, including (but not limited to): - <code>llama</code> - <code>mistral</code> - <code>qwen2</code> - <code>gemma</code> - <code>gemma2</code></p>
+<p>We currently support several common model architectures, including (but not limited to):</p>
+<ul>
+<li><code>llama</code></li>
+<li><code>mistral</code></li>
+<li><code>qwen2</code></li>
+<li><code>gemma</code></li>
+<li><code>gemma2</code></li>
+</ul>
 <details>
 <p>The set of models we support is currently limited by our attention patching strategy, which assumes (and replaces) specific code blocks for query / key / value and output projections:</p>
 <div class="sourceCode" 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>ORIGINAL_QKV_CODE <span class="op">=</span> <span class="st">"""</span></span>
diff --git a/search.json b/search.json
index f8c595602..a81d3a1fe 100644
--- a/search.json
+++ b/search.json
@@ -1321,7 +1321,7 @@
     "href": "docs/lora_optims.html",
     "title": "LoRA Optimizations",
     "section": "",
-    "text": "Inspired by Unsloth, we’ve implemented two optimizations for LoRA and QLoRA fine-tuning, supporting both single GPU and multi-GPU (in the DDP and DeepSpeed settings) training. These include (1) SwiGLU and GEGLU activation function Triton kernels, and (2) LoRA MLP and attention custom autograd functions. Our goal was to leverage operator fusion and tensor re-use in order to improve speed and reduce memory usage during the forward and backward passes of these calculations.\nWe currently support several common model architectures, including (but not limited to): - llama - mistral - qwen2 - gemma - gemma2"
+    "text": "Inspired by Unsloth, we’ve implemented two optimizations for LoRA and QLoRA fine-tuning, supporting both single GPU and multi-GPU (in the DDP and DeepSpeed settings) training. These include (1) SwiGLU and GEGLU activation function Triton kernels, and (2) LoRA MLP and attention custom autograd functions. Our goal was to leverage operator fusion and tensor re-use in order to improve speed and reduce memory usage during the forward and backward passes of these calculations.\nWe currently support several common model architectures, including (but not limited to):"
   },
   {
     "objectID": "docs/lora_optims.html#usage",
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index 36b28fe37..e5618dffe 100644
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@@ -2,158 +2,158 @@
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