Built site for gh-pages

2025-07-30 09:02:40 +00:00
parent 91590f1560
commit 1a2b198299
7 changed files with 233 additions and 197 deletions
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-64183d6f
+314ef855
--- a/docs/api/utils.dict.html
+++ b/docs/api/utils.dict.html
@@ -488,6 +488,10 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
  <ul class="collapse">
  <li><a href="#axolotl.utils.dict.DictDefault" id="toc-axolotl.utils.dict.DictDefault" class="nav-link" data-scroll-target="#axolotl.utils.dict.DictDefault">DictDefault</a></li>
  </ul></li>
+  <li><a href="#functions" id="toc-functions" class="nav-link" data-scroll-target="#functions">Functions</a>
+  <ul class="collapse">
+  <li><a href="#axolotl.utils.dict.remove_none_values" id="toc-axolotl.utils.dict.remove_none_values" class="nav-link" data-scroll-target="#axolotl.utils.dict.remove_none_values">remove_none_values</a></li>
+  </ul></li>
  </ul></li>
  </ul>
 </nav>
@@ -522,6 +526,30 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
 <h3 class="anchored" data-anchor-id="axolotl.utils.dict.DictDefault">DictDefault</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.<span class="bu">dict</span>.DictDefault()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <p>A Dict that returns None instead of returning empty Dict for missing keys.</p>
+</section>
+</section>
+<section id="functions" class="level2">
+<h2 class="anchored" data-anchor-id="functions">Functions</h2>
+<table class="caption-top table">
+<thead>
+<tr class="header">
+<th>Name</th>
+<th>Description</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td><a href="#axolotl.utils.dict.remove_none_values">remove_none_values</a></td>
+<td>Remove null from a dictionary-like obj or list.</td>
+</tr>
+</tbody>
+</table>
+<section id="axolotl.utils.dict.remove_none_values" class="level3">
+<h3 class="anchored" data-anchor-id="axolotl.utils.dict.remove_none_values">remove_none_values</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.<span class="bu">dict</span>.remove_none_values(obj)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<p>Remove null from a dictionary-like obj or list.
+These can appear due to Dataset loading causing schema merge.
+See https://github.com/axolotl-ai-cloud/axolotl/pull/2909</p>


 </section>
--- a/docs/custom_integrations.html
+++ b/docs/custom_integrations.html
@@ -574,7 +574,7 @@ 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@631d646"</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@010c3ac3f1e725098961832830303eeb4142dd88"</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>
--- a/examples/colab-notebooks/colab-axolotl-example.html
+++ b/examples/colab-notebooks/colab-axolotl-example.html
@@ -538,7 +538,7 @@ 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@631d646"</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@010c3ac3f1e725098961832830303eeb4142dd88"</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>
--- a/index.html
+++ b/index.html
@@ -528,6 +528,7 @@ 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>2025/05: 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>
--- a/search.json
+++ b/search.json
@@ -18,7 +18,7 @@
    "href": "index.html#latest-updates",
    "title": "Axolotl",
    "section": "🎉 Latest Updates",
-    "text": "🎉 Latest Updates\n\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: 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.",
    "crumbs": [
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@@ -1153,7 +1153,7 @@
    "href": "docs/custom_integrations.html#cut-cross-entropy",
    "title": "Custom Integrations",
    "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@631d646\"\n\n\nUsage\nplugins:\n  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin\n\n\nSupported Models\n\ncohere\ncohere2\ngemma\ngemma2\ngemma3\ngemma3_text\ngemma3n\ngemma3n_text\nglm\nglm4\nllama\nllama4\nllama4_text\nmistral\nmistral3\nmllama\nphi\nphi3\nphi4_multimodal\nqwen2\nqwen2_vl\nqwen2_moe\nqwen2_5_vl\nqwen3\nqwen3_moe\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@010c3ac3f1e725098961832830303eeb4142dd88\"\n\n\nUsage\nplugins:\n  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin\n\n\nSupported Models\n\ncohere\ncohere2\ngemma\ngemma2\ngemma3\ngemma3_text\ngemma3n\ngemma3n_text\nglm\nglm4\nllama\nllama4\nllama4_text\nmistral\nmistral3\nmllama\nphi\nphi3\nphi4_multimodal\nqwen2\nqwen2_vl\nqwen2_moe\nqwen2_5_vl\nqwen3\nqwen3_moe\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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@@ -3539,7 +3539,7 @@
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-    "text": "utils.dict\nModule containing the DictDefault class\n\n\n\n\n\nName\nDescription\n\n\n\n\nDictDefault\nA Dict that returns None instead of returning empty Dict for missing keys.\n\n\n\n\n\nutils.dict.DictDefault()\nA Dict that returns None instead of returning empty Dict for missing keys."
+    "text": "utils.dict\nModule containing the DictDefault class\n\n\n\n\n\nName\nDescription\n\n\n\n\nDictDefault\nA Dict that returns None instead of returning empty Dict for missing keys.\n\n\n\n\n\nutils.dict.DictDefault()\nA Dict that returns None instead of returning empty Dict for missing keys.\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nremove_none_values\nRemove null from a dictionary-like obj or list.\n\n\n\n\n\nutils.dict.remove_none_values(obj)\nRemove null from a dictionary-like obj or list.\nThese can appear due to Dataset loading causing schema merge.\nSee https://github.com/axolotl-ai-cloud/axolotl/pull/2909"
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+    "title": "utils.dict",
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+    "text": "Name\nDescription\n\n\n\n\nremove_none_values\nRemove null from a dictionary-like obj or list.\n\n\n\n\n\nutils.dict.remove_none_values(obj)\nRemove null from a dictionary-like obj or list.\nThese can appear due to Dataset loading causing schema merge.\nSee https://github.com/axolotl-ai-cloud/axolotl/pull/2909"
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