Built site for gh-pages

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@@ -1 +1 @@
-866af8c6
+6fb18589

docs/api/cli.args.html

@@ -593,7 +593,8 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
 <span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>    quantize_embedding<span class="op">=</span><span class="va">None</span>,</span>
 <span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>    group_size<span class="op">=</span><span class="va">None</span>,</span>
 <span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a>    output_dir<span class="op">=</span><span class="va">None</span>,</span>
-<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a>    hub_model_id<span class="op">=</span><span class="va">None</span>,</span>
+<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <p>Dataclass with CLI arguments for <code>axolotl quantize</code> command.</p>
 </section>
 <section id="axolotl.cli.args.TrainerCliArgs" class="level3">

docs/api/utils.quantization.html

@@ -501,10 +501,10 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
   <ul class="collapse">
   <li><a href="#functions" id="toc-functions" class="nav-link" data-scroll-target="#functions">Functions</a>
   <ul class="collapse">
-  <li><a href="#axolotl.utils.quantization.convert_qat_model_for_ptq" id="toc-axolotl.utils.quantization.convert_qat_model_for_ptq" class="nav-link" data-scroll-target="#axolotl.utils.quantization.convert_qat_model_for_ptq">convert_qat_model_for_ptq</a></li>
+  <li><a href="#axolotl.utils.quantization.convert_qat_model" id="toc-axolotl.utils.quantization.convert_qat_model" class="nav-link" data-scroll-target="#axolotl.utils.quantization.convert_qat_model">convert_qat_model</a></li>
-  <li><a href="#axolotl.utils.quantization.get_ptq_config" id="toc-axolotl.utils.quantization.get_ptq_config" class="nav-link" data-scroll-target="#axolotl.utils.quantization.get_ptq_config">get_ptq_config</a></li>
+  <li><a href="#axolotl.utils.quantization.get_quantization_config" id="toc-axolotl.utils.quantization.get_quantization_config" class="nav-link" data-scroll-target="#axolotl.utils.quantization.get_quantization_config">get_quantization_config</a></li>
   <li><a href="#axolotl.utils.quantization.prepare_model_for_qat" id="toc-axolotl.utils.quantization.prepare_model_for_qat" class="nav-link" data-scroll-target="#axolotl.utils.quantization.prepare_model_for_qat">prepare_model_for_qat</a></li>
-  <li><a href="#axolotl.utils.quantization.quantize_model_for_ptq" id="toc-axolotl.utils.quantization.quantize_model_for_ptq" class="nav-link" data-scroll-target="#axolotl.utils.quantization.quantize_model_for_ptq">quantize_model_for_ptq</a></li>
+  <li><a href="#axolotl.utils.quantization.quantize_model" id="toc-axolotl.utils.quantization.quantize_model" class="nav-link" data-scroll-target="#axolotl.utils.quantization.quantize_model">quantize_model</a></li>
   </ul></li>
   </ul></li>
   </ul>
@@ -531,11 +531,11 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
 </thead>
 <tbody>
 <tr class="odd">
-<td><a href="#axolotl.utils.quantization.convert_qat_model_for_ptq">convert_qat_model_for_ptq</a></td>
+<td><a href="#axolotl.utils.quantization.convert_qat_model">convert_qat_model</a></td>
-<td>This function is used to convert a swap fake-quantized modules in a model</td>
+<td>This function converts a QAT model which has fake quantized layers back to the original model.</td>
 </tr>
 <tr class="even">
-<td><a href="#axolotl.utils.quantization.get_ptq_config">get_ptq_config</a></td>
+<td><a href="#axolotl.utils.quantization.get_quantization_config">get_quantization_config</a></td>
 <td>This function is used to build a post-training quantization config.</td>
 </tr>
 <tr class="odd">
@@ -543,65 +543,31 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
 <td>This function is used to prepare a model for QAT by swapping the model’s linear</td>
 </tr>
 <tr class="even">
-<td><a href="#axolotl.utils.quantization.quantize_model_for_ptq">quantize_model_for_ptq</a></td>
+<td><a href="#axolotl.utils.quantization.quantize_model">quantize_model</a></td>
-<td>This function is used to quantize a model for post-training quantization.</td>
+<td>This function is used to quantize a model.</td>
-</tr>
-</tbody>
-</table>
-<section id="axolotl.utils.quantization.convert_qat_model_for_ptq" class="level3">
-<h3 class="anchored" data-anchor-id="axolotl.utils.quantization.convert_qat_model_for_ptq">convert_qat_model_for_ptq</h3>
-<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>utils.quantization.convert_qat_model_for_ptq(model, <span class="op">*</span>, quantize_embedding<span class="op">=</span><span class="va">None</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<p>This function is used to convert a swap fake-quantized modules in a model
-which has been trained with QAT back to the original modules, ready for PTQ.</p>
-<section id="parameters" class="level4 doc-section doc-section-parameters">
-<h4 class="doc-section doc-section-parameters anchored" data-anchor-id="parameters">Parameters</h4>
-<table class="caption-top table">
-<colgroup>
-<col style="width: 20%">
-<col style="width: 14%">
-<col style="width: 53%">
-<col style="width: 12%">
-</colgroup>
-<thead>
-<tr class="header">
-<th>Name</th>
-<th>Type</th>
-<th>Description</th>
-<th>Default</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td>model</td>
-<td></td>
-<td>The model to convert.</td>
-<td><em>required</em></td>
-</tr>
-<tr class="even">
-<td>quantize_embedding</td>
-<td>bool | None</td>
-<td>Whether to quantize the model’s embedding weights.</td>
-<td><code>None</code></td>
 </tr>
 </tbody>
 </table>
+<section id="axolotl.utils.quantization.convert_qat_model" class="level3">
+<h3 class="anchored" data-anchor-id="axolotl.utils.quantization.convert_qat_model">convert_qat_model</h3>
+<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>utils.quantization.convert_qat_model(model, quantize_embedding<span class="op">=</span><span class="va">False</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<p>This function converts a QAT model which has fake quantized layers back to the original model.</p>
 </section>
-</section>
+<section id="axolotl.utils.quantization.get_quantization_config" class="level3">
-<section id="axolotl.utils.quantization.get_ptq_config" class="level3">
+<h3 class="anchored" data-anchor-id="axolotl.utils.quantization.get_quantization_config">get_quantization_config</h3>
-<h3 class="anchored" data-anchor-id="axolotl.utils.quantization.get_ptq_config">get_ptq_config</h3>
+<div class="sourceCode" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>utils.quantization.get_quantization_config(</span>
-<div class="sourceCode" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>utils.quantization.get_ptq_config(</span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>    weight_dtype,</span>
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>    activation_dtype<span class="op">=</span><span class="va">None</span>,</span>
 <span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>    group_size<span class="op">=</span><span class="va">None</span>,</span>
 <span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <p>This function is used to build a post-training quantization config.</p>
-<section id="parameters-1" class="level4 doc-section doc-section-parameters">
+<section id="parameters" class="level4 doc-section doc-section-parameters">
-<h4 class="doc-section doc-section-parameters anchored" data-anchor-id="parameters-1">Parameters</h4>
+<h4 class="doc-section doc-section-parameters anchored" data-anchor-id="parameters">Parameters</h4>
 <table class="caption-top table">
 <colgroup>
 <col style="width: 17%">
-<col style="width: 22%">
+<col style="width: 25%">
-<col style="width: 47%">
+<col style="width: 45%">
 <col style="width: 11%">
 </colgroup>
 <thead>
@@ -615,13 +581,13 @@ which has been trained with QAT back to the original modules, ready for PTQ.</p>
 <tbody>
 <tr class="odd">
 <td>weight_dtype</td>
-<td>TorchIntDType</td>
+<td>TorchAOQuantDType</td>
 <td>The dtype to use for weight quantization.</td>
 <td><em>required</em></td>
 </tr>
 <tr class="even">
 <td>activation_dtype</td>
-<td>TorchIntDType | None</td>
+<td>TorchAOQuantDType | None</td>
 <td>The dtype to use for activation quantization.</td>
 <td><code>None</code></td>
 </tr>
@@ -683,21 +649,21 @@ which has been trained with QAT back to the original modules, ready for PTQ.</p>
 <div class="sourceCode" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>utils.quantization.prepare_model_for_qat(</span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>    model,</span>
 <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>    weight_dtype,</span>
-<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>    group_size,</span>
+<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>    group_size<span class="op">=</span><span class="va">None</span>,</span>
 <span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a>    activation_dtype<span class="op">=</span><span class="va">None</span>,</span>
 <span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a>    quantize_embedding<span class="op">=</span><span class="va">False</span>,</span>
 <span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <p>This function is used to prepare a model for QAT by swapping the model’s linear
 layers with fake quantized linear layers, and optionally the embedding weights with
 fake quantized embedding weights.</p>
-<section id="parameters-2" class="level4 doc-section doc-section-parameters">
+<section id="parameters-1" class="level4 doc-section doc-section-parameters">
-<h4 class="doc-section doc-section-parameters anchored" data-anchor-id="parameters-2">Parameters</h4>
+<h4 class="doc-section doc-section-parameters anchored" data-anchor-id="parameters-1">Parameters</h4>
 <table class="caption-top table">
 <colgroup>
 <col style="width: 18%">
-<col style="width: 21%">
+<col style="width: 24%">
-<col style="width: 48%">
+<col style="width: 46%">
-<col style="width: 11%">
+<col style="width: 10%">
 </colgroup>
 <thead>
 <tr class="header">
@@ -716,19 +682,19 @@ fake quantized embedding weights.</p>
 </tr>
 <tr class="even">
 <td>weight_dtype</td>
-<td>TorchIntDType</td>
+<td>TorchAOQuantDType</td>
 <td>The dtype to use for weight quantization.</td>
 <td><em>required</em></td>
 </tr>
 <tr class="odd">
 <td>group_size</td>
-<td>int</td>
+<td>int | None</td>
 <td>The group size to use for weight quantization.</td>
-<td><em>required</em></td>
+<td><code>None</code></td>
 </tr>
 <tr class="even">
 <td>activation_dtype</td>
-<td>TorchIntDType | None</td>
+<td>TorchAOQuantDType | None</td>
 <td>The dtype to use for activation quantization.</td>
 <td><code>None</code></td>
 </tr>
@@ -766,26 +732,24 @@ fake quantized embedding weights.</p>
 </table>
 </section>
 </section>
-<section id="axolotl.utils.quantization.quantize_model_for_ptq" class="level3">
+<section id="axolotl.utils.quantization.quantize_model" class="level3">
-<h3 class="anchored" data-anchor-id="axolotl.utils.quantization.quantize_model_for_ptq">quantize_model_for_ptq</h3>
+<h3 class="anchored" data-anchor-id="axolotl.utils.quantization.quantize_model">quantize_model</h3>
-<div class="sourceCode" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>utils.quantization.quantize_model_for_ptq(</span>
+<div class="sourceCode" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>utils.quantization.quantize_model(</span>
 <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>    model,</span>
 <span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>    weight_dtype,</span>
 <span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>    group_size<span class="op">=</span><span class="va">None</span>,</span>
 <span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>    activation_dtype<span class="op">=</span><span class="va">None</span>,</span>
 <span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>    quantize_embedding<span class="op">=</span><span class="va">None</span>,</span>
 <span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<p>This function is used to quantize a model for post-training quantization.
+<p>This function is used to quantize a model.</p>
-It swaps the model’s linear layers with fake quantized linear layers.
+<section id="parameters-2" class="level4 doc-section doc-section-parameters">
-If <code>quantize_embedding</code> is True, it will also swap the model’s embedding weights with fake quantized embedding weights.</p>
+<h4 class="doc-section doc-section-parameters anchored" data-anchor-id="parameters-2">Parameters</h4>
-<section id="parameters-3" class="level4 doc-section doc-section-parameters">
-<h4 class="doc-section doc-section-parameters anchored" data-anchor-id="parameters-3">Parameters</h4>
 <table class="caption-top table">
 <colgroup>
 <col style="width: 18%">
-<col style="width: 21%">
+<col style="width: 24%">
-<col style="width: 48%">
+<col style="width: 46%">
-<col style="width: 11%">
+<col style="width: 10%">
 </colgroup>
 <thead>
 <tr class="header">
@@ -804,7 +768,7 @@ If <code>quantize_embedding</code> is True, it will also swap the model’s embe
 </tr>
 <tr class="even">
 <td>weight_dtype</td>
-<td>TorchIntDType</td>
+<td>TorchAOQuantDType</td>
 <td>The dtype to use for weight quantization.</td>
 <td><em>required</em></td>
 </tr>
@@ -816,7 +780,7 @@ If <code>quantize_embedding</code> is True, it will also swap the model’s embe
 </tr>
 <tr class="even">
 <td>activation_dtype</td>
-<td>TorchIntDType | None</td>
+<td>TorchAOQuantDType | None</td>
 <td>The dtype to use for activation quantization.</td>
 <td><code>None</code></td>
 </tr>

docs/api/utils.schemas.enums.html

@@ -505,7 +505,6 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
   <li><a href="#axolotl.utils.schemas.enums.CustomSupportedOptimizers" id="toc-axolotl.utils.schemas.enums.CustomSupportedOptimizers" class="nav-link" data-scroll-target="#axolotl.utils.schemas.enums.CustomSupportedOptimizers">CustomSupportedOptimizers</a></li>
   <li><a href="#axolotl.utils.schemas.enums.RLType" id="toc-axolotl.utils.schemas.enums.RLType" class="nav-link" data-scroll-target="#axolotl.utils.schemas.enums.RLType">RLType</a></li>
   <li><a href="#axolotl.utils.schemas.enums.RingAttnFunc" id="toc-axolotl.utils.schemas.enums.RingAttnFunc" class="nav-link" data-scroll-target="#axolotl.utils.schemas.enums.RingAttnFunc">RingAttnFunc</a></li>
-  <li><a href="#axolotl.utils.schemas.enums.TorchIntDType" id="toc-axolotl.utils.schemas.enums.TorchIntDType" class="nav-link" data-scroll-target="#axolotl.utils.schemas.enums.TorchIntDType">TorchIntDType</a></li>
   </ul></li>
   </ul></li>
   </ul>
@@ -547,10 +546,6 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
 <td><a href="#axolotl.utils.schemas.enums.RingAttnFunc">RingAttnFunc</a></td>
 <td>Enum class for supported <code>ring-flash-attn</code> implementations</td>
 </tr>
-<tr class="odd">
-<td><a href="#axolotl.utils.schemas.enums.TorchIntDType">TorchIntDType</a></td>
-<td>Torch integer data types - <code>getattr</code> guards against torch &lt; 2.6 which does not support int4</td>
-</tr>
 </tbody>
 </table>
 <section id="axolotl.utils.schemas.enums.ChatTemplate" class="level3">
@@ -572,11 +567,6 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
 <h3 class="anchored" data-anchor-id="axolotl.utils.schemas.enums.RingAttnFunc">RingAttnFunc</h3>
 <div class="sourceCode" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>utils.schemas.enums.RingAttnFunc()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <p>Enum class for supported <code>ring-flash-attn</code> implementations</p>
-</section>
-<section id="axolotl.utils.schemas.enums.TorchIntDType" class="level3">
-<h3 class="anchored" data-anchor-id="axolotl.utils.schemas.enums.TorchIntDType">TorchIntDType</h3>
-<div class="sourceCode" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>utils.schemas.enums.TorchIntDType()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<p>Torch integer data types - <code>getattr</code> guards against torch &lt; 2.6 which does not support int4</p>
 
 
 </section>

docs/quantize.html

@@ -561,6 +561,20 @@ you used to train the model:</p>
 <span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a><span class="fu">output_dir</span><span class="kw">:</span><span class="co"> # The path to the output directory used during training where the final checkpoint has been saved.</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="sourceCode" id="cb3"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="ex">axolotl</span> quantize qat.yml</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <p>This ensures that an identical quantization configuration is used to quantize the model as was used to train it.</p>
+<div class="callout callout-style-default callout-note callout-titled">
+<div class="callout-header d-flex align-content-center">
+<div class="callout-icon-container">
+<i class="callout-icon"></i>
+</div>
+<div class="callout-title-container flex-fill">
+Note
+</div>
+</div>
+<div class="callout-body-container callout-body">
+<p>If you have configured pushing to hub with <code>hub_model_id</code>, your model hub name will have the quantization schema appended to it,
+e.g.&nbsp;<code>axolotl-ai-cloud/qat-nvfp4-llama3B</code> will become <code>axolotl-ai-cloud/qat-nvfp4-llama3B-nvfp4w</code></p>
+</div>
+</div>
 
 
 </section>

index.html

@@ -499,7 +499,7 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
   <ul>
   <li><a href="#latest-updates" id="toc-latest-updates" class="nav-link active" data-scroll-target="#latest-updates">🎉 Latest Updates</a></li>
   <li><a href="#overview" id="toc-overview" class="nav-link" data-scroll-target="#overview">✨ Overview</a></li>
-  <li><a href="#quick-start" id="toc-quick-start" class="nav-link" data-scroll-target="#quick-start">🚀 Quick Start</a>
+  <li><a href="#quick-start---llm-fine-tuning-in-minutes" id="toc-quick-start---llm-fine-tuning-in-minutes" class="nav-link" data-scroll-target="#quick-start---llm-fine-tuning-in-minutes">🚀 Quick Start - LLM Fine-tuning in Minutes</a>
   <ul class="collapse">
   <li><a href="#google-colab" id="toc-google-colab" class="nav-link" data-scroll-target="#google-colab">Google Colab</a></li>
   <li><a href="#installation" id="toc-installation" class="nav-link" data-scroll-target="#installation">Installation</a></li>
@@ -528,6 +528,9 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
 </picture>
 </p>
 <p align="center">
+<strong>A Free and Open Source LLM Fine-tuning Framework</strong><br>
+</p>
+<p align="center">
 <img src="https://img.shields.io/github/license/axolotl-ai-cloud/axolotl.svg?color=blue" alt="GitHub License">
 <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests.yml/badge.svg" alt="tests">
 <a href="https://codecov.io/gh/axolotl-ai-cloud/axolotl"><img src="https://codecov.io/gh/axolotl-ai-cloud/axolotl/branch/main/graph/badge.svg" alt="codecov"></a>
@@ -573,19 +576,20 @@ Expand older updates
 </section>
 <section id="overview" class="level2">
 <h2 class="anchored" data-anchor-id="overview">✨ Overview</h2>
-<p>Axolotl is a tool designed to streamline post-training for various AI models.</p>
+<p>Axolotl is a free and open-source tool designed to streamline post-training and fine-tuning for the latest large language models (LLMs).</p>
 <p>Features:</p>
 <ul>
-<li><strong>Multiple Model Support</strong>: Train various models like LLaMA, Mistral, Mixtral, Pythia, and more. We are compatible with HuggingFace transformers causal language models.</li>
+<li><strong>Multiple Model Support</strong>: Train various models like GPT-OSS, LLaMA, Mistral, Mixtral, Pythia, and many more models available on the Hugging Face Hub.</li>
-<li><strong>Training Methods</strong>: Full fine-tuning, LoRA, QLoRA, GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO), Multimodal, and Reward Modelling (RM) / Process Reward Modelling (PRM).</li>
+<li><strong>Multimodal Training</strong>: Fine-tune vision-language models (VLMs) including LLaMA-Vision, Qwen2-VL, Pixtral, LLaVA, SmolVLM2, and audio models like Voxtral with image, video, and audio support.</li>
-<li><strong>Easy Configuration</strong>: Re-use a single YAML file between dataset preprocess, training, evaluation, quantization, and inference.</li>
+<li><strong>Training Methods</strong>: Full fine-tuning, LoRA, QLoRA, GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO), and Reward Modelling (RM) / Process Reward Modelling (PRM).</li>
+<li><strong>Easy Configuration</strong>: Re-use a single YAML configuration file across the full fine-tuning pipeline: dataset preprocessing, training, evaluation, quantization, and inference.</li>
 <li><strong>Performance Optimizations</strong>: <a href="https://docs.axolotl.ai/docs/multipack.html">Multipacking</a>, <a href="https://github.com/Dao-AILab/flash-attention">Flash Attention</a>, <a href="https://github.com/facebookresearch/xformers">Xformers</a>, <a href="https://pytorch.org/blog/flexattention/">Flex Attention</a>, <a href="https://github.com/linkedin/Liger-Kernel">Liger Kernel</a>, <a href="https://github.com/apple/ml-cross-entropy/tree/main">Cut Cross Entropy</a>, <a href="https://docs.axolotl.ai/docs/sequence_parallelism.html">Sequence Parallelism (SP)</a>, <a href="https://docs.axolotl.ai/docs/lora_optims.html">LoRA optimizations</a>, <a href="https://docs.axolotl.ai/docs/multi-gpu.html">Multi-GPU training (FSDP1, FSDP2, DeepSpeed)</a>, <a href="https://docs.axolotl.ai/docs/multi-node.html">Multi-node training (Torchrun, Ray)</a>, and many more!</li>
 <li><strong>Flexible Dataset Handling</strong>: Load from local, HuggingFace, and cloud (S3, Azure, GCP, OCI) datasets.</li>
 <li><strong>Cloud Ready</strong>: We ship <a href="https://hub.docker.com/u/axolotlai">Docker images</a> and also <a href="https://pypi.org/project/axolotl/">PyPI packages</a> for use on cloud platforms and local hardware.</li>
 </ul>
 </section>
-<section id="quick-start" class="level2">
+<section id="quick-start---llm-fine-tuning-in-minutes" class="level2">
-<h2 class="anchored" data-anchor-id="quick-start">🚀 Quick Start</h2>
+<h2 class="anchored" data-anchor-id="quick-start---llm-fine-tuning-in-minutes">🚀 Quick Start - LLM Fine-tuning in Minutes</h2>
 <p><strong>Requirements</strong>:</p>
 <ul>
 <li>NVIDIA GPU (Ampere or newer for <code>bf16</code> and Flash Attention) or AMD GPU</li>
@@ -681,7 +685,7 @@ Expand older updates
 <h2 class="anchored" data-anchor-id="citing-axolotl">📝 Citing Axolotl</h2>
 <p>If you use Axolotl in your research or projects, please cite it as follows:</p>
 <div class="sourceCode" id="cb4"><pre class="sourceCode bibtex code-with-copy"><code class="sourceCode bibtex"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co">@software{axolotl,</span></span>
-<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="co">  title = {Axolotl: Post-Training for AI Models},</span></span>
+<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="co">  title = {Axolotl: Open Source LLM Post-Training},</span></span>
 <span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a><span class="co">  author = {{Axolotl maintainers and contributors}},</span></span>
 <span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a><span class="co">  url = {https://github.com/axolotl-ai-cloud/axolotl},</span></span>
 <span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a><span class="co">  license = {Apache-2.0},</span></span>

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