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@@ -475,6 +475,7 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
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<li><a href="#supported-models" id="toc-supported-models" class="nav-link" data-scroll-target="#supported-models">Supported Models</a></li>
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<li><a href="#citation" id="toc-citation" class="nav-link" data-scroll-target="#citation">Citation</a></li>
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</ul></li>
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<li><a href="#densemixer" id="toc-densemixer" class="nav-link" data-scroll-target="#densemixer">DenseMixer</a></li>
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<li><a href="#grokfast" id="toc-grokfast" class="nav-link" data-scroll-target="#grokfast">Grokfast</a>
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<ul class="collapse">
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<li><a href="#usage-1" id="toc-usage-1" class="nav-link" data-scroll-target="#usage-1">Usage</a></li>
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@@ -484,31 +485,31 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
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<ul class="collapse">
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<li><a href="#usage-2" id="toc-usage-2" class="nav-link" data-scroll-target="#usage-2">Usage</a></li>
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</ul></li>
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<li><a href="#liger-kernels" id="toc-liger-kernels" class="nav-link" data-scroll-target="#liger-kernels">Liger Kernels</a>
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<li><a href="#llmcompressor" id="toc-llmcompressor" class="nav-link" data-scroll-target="#llmcompressor">LLMCompressor</a>
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<ul class="collapse">
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<li><a href="#requirements-1" id="toc-requirements-1" class="nav-link" data-scroll-target="#requirements-1">Requirements</a></li>
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<li><a href="#usage-3" id="toc-usage-3" class="nav-link" data-scroll-target="#usage-3">Usage</a></li>
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<li><a href="#supported-models-1" id="toc-supported-models-1" class="nav-link" data-scroll-target="#supported-models-1">Supported Models</a></li>
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<li><a href="#citation-2" id="toc-citation-2" class="nav-link" data-scroll-target="#citation-2">Citation</a></li>
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<li><a href="#storage-optimization-with-save_compressed" id="toc-storage-optimization-with-save_compressed" class="nav-link" data-scroll-target="#storage-optimization-with-save_compressed">Storage Optimization with save_compressed</a></li>
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<li><a href="#example-config" id="toc-example-config" class="nav-link" data-scroll-target="#example-config">Example Config</a></li>
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<li><a href="#inference-with-vllm" id="toc-inference-with-vllm" class="nav-link" data-scroll-target="#inference-with-vllm">Inference with vLLM</a></li>
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<li><a href="#learn-more" id="toc-learn-more" class="nav-link" data-scroll-target="#learn-more">Learn More</a></li>
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</ul></li>
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<li><a href="#language-model-evaluation-harness-lm-eval" id="toc-language-model-evaluation-harness-lm-eval" class="nav-link" data-scroll-target="#language-model-evaluation-harness-lm-eval">Language Model Evaluation Harness (LM Eval)</a>
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<ul class="collapse">
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<li><a href="#usage-4" id="toc-usage-4" class="nav-link" data-scroll-target="#usage-4">Usage</a></li>
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<li><a href="#citation-2" id="toc-citation-2" class="nav-link" data-scroll-target="#citation-2">Citation</a></li>
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</ul></li>
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<li><a href="#liger-kernels" id="toc-liger-kernels" class="nav-link" data-scroll-target="#liger-kernels">Liger Kernels</a>
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<ul class="collapse">
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<li><a href="#usage-5" id="toc-usage-5" class="nav-link" data-scroll-target="#usage-5">Usage</a></li>
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<li><a href="#supported-models-1" id="toc-supported-models-1" class="nav-link" data-scroll-target="#supported-models-1">Supported Models</a></li>
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<li><a href="#citation-3" id="toc-citation-3" class="nav-link" data-scroll-target="#citation-3">Citation</a></li>
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</ul></li>
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<li><a href="#spectrum" id="toc-spectrum" class="nav-link" data-scroll-target="#spectrum">Spectrum</a>
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<ul class="collapse">
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<li><a href="#overview" id="toc-overview" class="nav-link" data-scroll-target="#overview">Overview</a></li>
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<li><a href="#usage-5" id="toc-usage-5" class="nav-link" data-scroll-target="#usage-5">Usage</a></li>
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<li><a href="#citation-4" id="toc-citation-4" class="nav-link" data-scroll-target="#citation-4">Citation</a></li>
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</ul></li>
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<li><a href="#llmcompressor" id="toc-llmcompressor" class="nav-link" data-scroll-target="#llmcompressor">LLMCompressor</a>
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<ul class="collapse">
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<li><a href="#requirements-1" id="toc-requirements-1" class="nav-link" data-scroll-target="#requirements-1">Requirements</a></li>
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<li><a href="#usage-6" id="toc-usage-6" class="nav-link" data-scroll-target="#usage-6">Usage</a></li>
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<li><a href="#storage-optimization-with-save_compressed" id="toc-storage-optimization-with-save_compressed" class="nav-link" data-scroll-target="#storage-optimization-with-save_compressed">Storage Optimization with save_compressed</a></li>
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<li><a href="#example-config" id="toc-example-config" class="nav-link" data-scroll-target="#example-config">Example Config</a></li>
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<li><a href="#inference-with-vllm" id="toc-inference-with-vllm" class="nav-link" data-scroll-target="#inference-with-vllm">Inference with vLLM</a></li>
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<li><a href="#learn-more" id="toc-learn-more" class="nav-link" data-scroll-target="#learn-more">Learn More</a></li>
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<li><a href="#citation-4" id="toc-citation-4" class="nav-link" data-scroll-target="#citation-4">Citation</a></li>
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</ul></li>
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<li><a href="#adding-a-new-integration" id="toc-adding-a-new-integration" class="nav-link" data-scroll-target="#adding-a-new-integration">Adding a new integration</a></li>
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</ul>
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@@ -609,25 +610,33 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
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<p>Please see reference <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/cut_cross_entropy">here</a></p>
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</section>
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</section>
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<section id="densemixer" class="level2">
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<h2 class="anchored" data-anchor-id="densemixer">DenseMixer</h2>
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<p>See <a href="https://github.com/yaof20/DenseMixer/">DenseMixer</a></p>
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<p>Simply add the following to your axolotl YAML config:</p>
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<div class="sourceCode" id="cb5"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plugins</span><span class="kw">:</span></span>
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<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> axolotl.integrations.densemixer.DenseMixerPlugin</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<p>Please see reference <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/densemixer">here</a></p>
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</section>
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<section id="grokfast" class="level2">
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<h2 class="anchored" data-anchor-id="grokfast">Grokfast</h2>
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<p>See https://github.com/ironjr/grokfast</p>
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<section id="usage-1" class="level3">
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<h3 class="anchored" data-anchor-id="usage-1">Usage</h3>
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<div class="sourceCode" id="cb5"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plugins</span><span class="kw">:</span></span>
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<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> axolotl.integrations.grokfast.GrokfastPlugin</span></span>
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<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a></span>
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<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a><span class="fu">grokfast_alpha</span><span class="kw">:</span><span class="at"> </span><span class="fl">2.0</span></span>
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<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a><span class="fu">grokfast_lamb</span><span class="kw">:</span><span class="at"> </span><span class="fl">0.98</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<div class="sourceCode" id="cb6"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plugins</span><span class="kw">:</span></span>
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<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> axolotl.integrations.grokfast.GrokfastPlugin</span></span>
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<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a></span>
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<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a><span class="fu">grokfast_alpha</span><span class="kw">:</span><span class="at"> </span><span class="fl">2.0</span></span>
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<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a><span class="fu">grokfast_lamb</span><span class="kw">:</span><span class="at"> </span><span class="fl">0.98</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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</section>
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<section id="citation-1" class="level3">
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<h3 class="anchored" data-anchor-id="citation-1">Citation</h3>
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<div class="sourceCode" id="cb6"><pre class="sourceCode bib code-with-copy"><code class="sourceCode bibtex"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="va">@article</span>{<span class="ot">lee2024grokfast</span>,</span>
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<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a> <span class="dt">title</span>={{Grokfast}: Accelerated Grokking by Amplifying Slow Gradients},</span>
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<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a> <span class="dt">author</span>={Lee, Jaerin and Kang, Bong Gyun and Kim, Kihoon and Lee, Kyoung Mu},</span>
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<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a> <span class="dt">journal</span>={arXiv preprint arXiv:2405.20233},</span>
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<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a> <span class="dt">year</span>={2024}</span>
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<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<div class="sourceCode" id="cb7"><pre class="sourceCode bib code-with-copy"><code class="sourceCode bibtex"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="va">@article</span>{<span class="ot">lee2024grokfast</span>,</span>
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<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a> <span class="dt">title</span>={{Grokfast}: Accelerated Grokking by Amplifying Slow Gradients},</span>
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<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a> <span class="dt">author</span>={Lee, Jaerin and Kang, Bong Gyun and Kim, Kihoon and Lee, Kyoung Mu},</span>
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<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a> <span class="dt">journal</span>={arXiv preprint arXiv:2405.20233},</span>
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<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a> <span class="dt">year</span>={2024}</span>
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<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<p>Please see reference <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/grokfast">here</a></p>
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</section>
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</section>
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@@ -635,145 +644,25 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
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<h2 class="anchored" data-anchor-id="knowledge-distillation-kd">Knowledge Distillation (KD)</h2>
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<section id="usage-2" class="level3">
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<h3 class="anchored" data-anchor-id="usage-2">Usage</h3>
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||||
<div class="sourceCode" id="cb7"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plugins</span><span class="kw">:</span></span>
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||||
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> </span><span class="st">"axolotl.integrations.kd.KDPlugin"</span></span>
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<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a></span>
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<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a><span class="fu">kd_trainer</span><span class="kw">:</span><span class="at"> </span><span class="ch">True</span></span>
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<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a><span class="fu">kd_ce_alpha</span><span class="kw">:</span><span class="at"> </span><span class="fl">0.1</span></span>
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<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a><span class="fu">kd_alpha</span><span class="kw">:</span><span class="at"> </span><span class="fl">0.9</span></span>
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<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a><span class="fu">kd_temperature</span><span class="kw">:</span><span class="at"> </span><span class="fl">1.0</span></span>
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<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a></span>
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<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a><span class="fu">torch_compile</span><span class="kw">:</span><span class="at"> </span><span class="ch">True</span><span class="co"> # torch>=2.5.1, recommended to reduce vram</span></span>
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<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a></span>
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<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a><span class="fu">datasets</span><span class="kw">:</span></span>
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<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> </span><span class="fu">path</span><span class="kw">:</span><span class="at"> ...</span></span>
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<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">type</span><span class="kw">:</span><span class="at"> </span><span class="st">"axolotl.integrations.kd.chat_template"</span></span>
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||||
<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">field_messages</span><span class="kw">:</span><span class="at"> </span><span class="st">"messages_combined"</span></span>
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<span id="cb7-15"><a href="#cb7-15" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">logprobs_field</span><span class="kw">:</span><span class="at"> </span><span class="st">"llm_text_generation_vllm_logprobs"</span><span class="co"> # for kd only, field of logprobs</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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||||
<div class="sourceCode" id="cb8"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plugins</span><span class="kw">:</span></span>
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||||
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> </span><span class="st">"axolotl.integrations.kd.KDPlugin"</span></span>
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||||
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a></span>
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||||
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a><span class="fu">kd_trainer</span><span class="kw">:</span><span class="at"> </span><span class="ch">True</span></span>
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||||
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a><span class="fu">kd_ce_alpha</span><span class="kw">:</span><span class="at"> </span><span class="fl">0.1</span></span>
|
||||
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a><span class="fu">kd_alpha</span><span class="kw">:</span><span class="at"> </span><span class="fl">0.9</span></span>
|
||||
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a><span class="fu">kd_temperature</span><span class="kw">:</span><span class="at"> </span><span class="fl">1.0</span></span>
|
||||
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a><span class="fu">torch_compile</span><span class="kw">:</span><span class="at"> </span><span class="ch">True</span><span class="co"> # torch>=2.5.1, recommended to reduce vram</span></span>
|
||||
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a><span class="fu">datasets</span><span class="kw">:</span></span>
|
||||
<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> </span><span class="fu">path</span><span class="kw">:</span><span class="at"> ...</span></span>
|
||||
<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">type</span><span class="kw">:</span><span class="at"> </span><span class="st">"axolotl.integrations.kd.chat_template"</span></span>
|
||||
<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">field_messages</span><span class="kw">:</span><span class="at"> </span><span class="st">"messages_combined"</span></span>
|
||||
<span id="cb8-15"><a href="#cb8-15" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">logprobs_field</span><span class="kw">:</span><span class="at"> </span><span class="st">"llm_text_generation_vllm_logprobs"</span><span class="co"> # for kd only, field of logprobs</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
<p>An example dataset can be found at <a href="https://huggingface.co/datasets/axolotl-ai-co/evolkit-logprobs-pipeline-75k-v2-sample"><code>axolotl-ai-co/evolkit-logprobs-pipeline-75k-v2-sample</code></a></p>
|
||||
<p>Please see reference <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/kd">here</a></p>
|
||||
</section>
|
||||
</section>
|
||||
<section id="liger-kernels" class="level2">
|
||||
<h2 class="anchored" data-anchor-id="liger-kernels">Liger Kernels</h2>
|
||||
<p>Liger Kernel provides efficient Triton kernels for LLM training, offering:</p>
|
||||
<ul>
|
||||
<li>20% increase in multi-GPU training throughput</li>
|
||||
<li>60% reduction in memory usage</li>
|
||||
<li>Compatibility with both FSDP and DeepSpeed</li>
|
||||
</ul>
|
||||
<p>See https://github.com/linkedin/Liger-Kernel</p>
|
||||
<section id="usage-3" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="usage-3">Usage</h3>
|
||||
<div class="sourceCode" id="cb8"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plugins</span><span class="kw">:</span></span>
|
||||
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> axolotl.integrations.liger.LigerPlugin</span></span>
|
||||
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="fu">liger_rope</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
|
||||
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a><span class="fu">liger_rms_norm</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
|
||||
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a><span class="fu">liger_glu_activation</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
|
||||
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a><span class="fu">liger_layer_norm</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
|
||||
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a><span class="fu">liger_fused_linear_cross_entropy</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
</section>
|
||||
<section id="supported-models-1" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="supported-models-1">Supported Models</h3>
|
||||
<ul>
|
||||
<li>deepseek_v2</li>
|
||||
<li>gemma</li>
|
||||
<li>gemma2</li>
|
||||
<li>gemma3</li>
|
||||
<li>granite</li>
|
||||
<li>jamba</li>
|
||||
<li>llama</li>
|
||||
<li>mistral</li>
|
||||
<li>mixtral</li>
|
||||
<li>mllama</li>
|
||||
<li>mllama_text_model</li>
|
||||
<li>olmo2</li>
|
||||
<li>paligemma</li>
|
||||
<li>phi3</li>
|
||||
<li>qwen2</li>
|
||||
<li>qwen2_5_vl</li>
|
||||
<li>qwen2_vl</li>
|
||||
</ul>
|
||||
</section>
|
||||
<section id="citation-2" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="citation-2">Citation</h3>
|
||||
<div class="sourceCode" id="cb9"><pre class="sourceCode bib code-with-copy"><code class="sourceCode bibtex"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="va">@article</span>{<span class="ot">hsu2024ligerkernelefficienttriton</span>,</span>
|
||||
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a> <span class="dt">title</span>={Liger Kernel: Efficient Triton Kernels for LLM Training},</span>
|
||||
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a> <span class="dt">author</span>={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen},</span>
|
||||
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a> <span class="dt">year</span>={2024},</span>
|
||||
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a> <span class="dt">eprint</span>={2410.10989},</span>
|
||||
<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a> <span class="dt">archivePrefix</span>={arXiv},</span>
|
||||
<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a> <span class="dt">primaryClass</span>={cs.LG},</span>
|
||||
<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a> <span class="dt">url</span>={https://arxiv.org/abs/2410.10989},</span>
|
||||
<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a> <span class="dt">journal</span>={arXiv preprint arXiv:2410.10989},</span>
|
||||
<span id="cb9-10"><a href="#cb9-10" 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>Please see reference <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/liger">here</a></p>
|
||||
</section>
|
||||
</section>
|
||||
<section id="language-model-evaluation-harness-lm-eval" class="level2">
|
||||
<h2 class="anchored" data-anchor-id="language-model-evaluation-harness-lm-eval">Language Model Evaluation Harness (LM Eval)</h2>
|
||||
<p>Run evaluation on model using the popular lm-evaluation-harness library.</p>
|
||||
<p>See https://github.com/EleutherAI/lm-evaluation-harness</p>
|
||||
<section id="usage-4" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="usage-4">Usage</h3>
|
||||
<div class="sourceCode" id="cb10"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plugins</span><span class="kw">:</span></span>
|
||||
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> axolotl.integrations.lm_eval.LMEvalPlugin</span></span>
|
||||
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a><span class="fu">lm_eval_tasks</span><span class="kw">:</span></span>
|
||||
<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> gsm8k</span></span>
|
||||
<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> hellaswag</span></span>
|
||||
<span id="cb10-7"><a href="#cb10-7" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> arc_easy</span></span>
|
||||
<span id="cb10-8"><a href="#cb10-8" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb10-9"><a href="#cb10-9" aria-hidden="true" tabindex="-1"></a><span class="fu">lm_eval_batch_size</span><span class="kw">:</span><span class="co"> # Batch size for evaluation</span></span>
|
||||
<span id="cb10-10"><a href="#cb10-10" aria-hidden="true" tabindex="-1"></a><span class="fu">output_dir</span><span class="kw">:</span><span class="co"> # Directory to save evaluation results</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
</section>
|
||||
<section id="citation-3" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="citation-3">Citation</h3>
|
||||
<div class="sourceCode" id="cb11"><pre class="sourceCode bib code-with-copy"><code class="sourceCode bibtex"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="va">@misc</span>{<span class="ot">eval</span>-<span class="ot">harness</span>,</span>
|
||||
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a> <span class="dt">author</span> = {Gao, Leo and Tow, Jonathan and Abbasi, Baber and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and Le Noac'h, Alain and Li, Haonan and McDonell, Kyle and Muennighoff, Niklas and Ociepa, Chris and Phang, Jason and Reynolds, Laria and Schoelkopf, Hailey and Skowron, Aviya and Sutawika, Lintang and Tang, Eric and Thite, Anish and Wang, Ben and Wang, Kevin and Zou, Andy},</span>
|
||||
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a> <span class="dt">title</span> = {A framework for few-shot language model evaluation},</span>
|
||||
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a> <span class="dt">month</span> = 07,</span>
|
||||
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a> <span class="dt">year</span> = 2024,</span>
|
||||
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a> <span class="dt">publisher</span> = {Zenodo},</span>
|
||||
<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a> <span class="dt">version</span> = {v0.4.3},</span>
|
||||
<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a> <span class="dt">doi</span> = {10.5281/zenodo.12608602},</span>
|
||||
<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a> <span class="dt">url</span> = {https://zenodo.org/records/12608602}</span>
|
||||
<span id="cb11-10"><a href="#cb11-10" 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>Please see reference <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/lm_eval">here</a></p>
|
||||
</section>
|
||||
</section>
|
||||
<section id="spectrum" class="level2">
|
||||
<h2 class="anchored" data-anchor-id="spectrum">Spectrum</h2>
|
||||
<p>by Eric Hartford, Lucas Atkins, Fernando Fernandes, David Golchinfar</p>
|
||||
<p>This plugin contains code to freeze the bottom fraction of modules in a model, based on the Signal-to-Noise Ratio (SNR).</p>
|
||||
<p>See https://github.com/cognitivecomputations/spectrum</p>
|
||||
<section id="overview" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="overview">Overview</h3>
|
||||
<p>Spectrum is a tool for scanning and evaluating the Signal-to-Noise Ratio (SNR) of layers in large language models.
|
||||
By identifying the top n% of layers with the highest SNR, you can optimize training efficiency.</p>
|
||||
</section>
|
||||
<section id="usage-5" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="usage-5">Usage</h3>
|
||||
<div class="sourceCode" id="cb12"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plugins</span><span class="kw">:</span></span>
|
||||
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> axolotl.integrations.spectrum.SpectrumPlugin</span></span>
|
||||
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a><span class="fu">spectrum_top_fraction</span><span class="kw">:</span><span class="at"> </span><span class="fl">0.5</span></span>
|
||||
<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a><span class="fu">spectrum_model_name</span><span class="kw">:</span><span class="at"> meta-llama/Meta-Llama-3.1-8B</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
</section>
|
||||
<section id="citation-4" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="citation-4">Citation</h3>
|
||||
<div class="sourceCode" id="cb13"><pre class="sourceCode bib code-with-copy"><code class="sourceCode bibtex"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="va">@misc</span>{<span class="ot">hartford2024spectrumtargetedtrainingsignal</span>,</span>
|
||||
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a> <span class="dt">title</span>={Spectrum: Targeted Training on Signal to Noise Ratio},</span>
|
||||
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a> <span class="dt">author</span>={Eric Hartford and Lucas Atkins and Fernando Fernandes Neto and David Golchinfar},</span>
|
||||
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a> <span class="dt">year</span>={2024},</span>
|
||||
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a> <span class="dt">eprint</span>={2406.06623},</span>
|
||||
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a> <span class="dt">archivePrefix</span>={arXiv},</span>
|
||||
<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a> <span class="dt">primaryClass</span>={cs.LG},</span>
|
||||
<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a> <span class="dt">url</span>={https://arxiv.org/abs/2406.06623},</span>
|
||||
<span id="cb13-9"><a href="#cb13-9" 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>Please see reference <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/spectrum">here</a></p>
|
||||
</section>
|
||||
</section>
|
||||
<section id="llmcompressor" class="level2">
|
||||
<h2 class="anchored" data-anchor-id="llmcompressor">LLMCompressor</h2>
|
||||
<p>Fine-tune sparsified models in Axolotl using Neural Magic’s <a href="https://github.com/vllm-project/llm-compressor">LLMCompressor</a>.</p>
|
||||
@@ -784,34 +673,34 @@ By identifying the top n% of layers with the highest SNR, you can optimize train
|
||||
<h3 class="anchored" data-anchor-id="requirements-1">Requirements</h3>
|
||||
<ul>
|
||||
<li><p>Axolotl with <code>llmcompressor</code> extras:</p>
|
||||
<div class="sourceCode" id="cb14"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="ex">pip</span> install <span class="st">"axolotl[llmcompressor]"</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div></li>
|
||||
<div class="sourceCode" id="cb9"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="ex">pip</span> install <span class="st">"axolotl[llmcompressor]"</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div></li>
|
||||
<li><p>Requires <code>llmcompressor >= 0.5.1</code></p></li>
|
||||
</ul>
|
||||
<p>This will install all necessary dependencies to fine-tune sparsified models using the integration.</p>
|
||||
<hr>
|
||||
</section>
|
||||
<section id="usage-6" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="usage-6">Usage</h3>
|
||||
<section id="usage-3" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="usage-3">Usage</h3>
|
||||
<p>To enable sparse fine-tuning with this integration, include the plugin in your Axolotl config:</p>
|
||||
<div class="sourceCode" id="cb15"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plugins</span><span class="kw">:</span></span>
|
||||
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> axolotl.integrations.llm_compressor.LLMCompressorPlugin</span></span>
|
||||
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a><span class="fu">llmcompressor</span><span class="kw">:</span></span>
|
||||
<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">recipe</span><span class="kw">:</span></span>
|
||||
<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">finetuning_stage</span><span class="kw">:</span></span>
|
||||
<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">finetuning_modifiers</span><span class="kw">:</span></span>
|
||||
<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">ConstantPruningModifier</span><span class="kw">:</span></span>
|
||||
<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">targets</span><span class="kw">:</span><span class="at"> </span><span class="kw">[</span></span>
|
||||
<span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="st">'re:.*q_proj.weight'</span><span class="kw">,</span></span>
|
||||
<span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="st">'re:.*k_proj.weight'</span><span class="kw">,</span></span>
|
||||
<span id="cb15-12"><a href="#cb15-12" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="st">'re:.*v_proj.weight'</span><span class="kw">,</span></span>
|
||||
<span id="cb15-13"><a href="#cb15-13" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="st">'re:.*o_proj.weight'</span><span class="kw">,</span></span>
|
||||
<span id="cb15-14"><a href="#cb15-14" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="st">'re:.*gate_proj.weight'</span><span class="kw">,</span></span>
|
||||
<span id="cb15-15"><a href="#cb15-15" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="st">'re:.*up_proj.weight'</span><span class="kw">,</span></span>
|
||||
<span id="cb15-16"><a href="#cb15-16" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="st">'re:.*down_proj.weight'</span><span class="kw">,</span></span>
|
||||
<span id="cb15-17"><a href="#cb15-17" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">]</span></span>
|
||||
<span id="cb15-18"><a href="#cb15-18" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">start</span><span class="kw">:</span><span class="at"> </span><span class="dv">0</span></span>
|
||||
<span id="cb15-19"><a href="#cb15-19" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">save_compressed</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
<div class="sourceCode" id="cb10"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plugins</span><span class="kw">:</span></span>
|
||||
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> axolotl.integrations.llm_compressor.LLMCompressorPlugin</span></span>
|
||||
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a><span class="fu">llmcompressor</span><span class="kw">:</span></span>
|
||||
<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">recipe</span><span class="kw">:</span></span>
|
||||
<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">finetuning_stage</span><span class="kw">:</span></span>
|
||||
<span id="cb10-7"><a href="#cb10-7" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">finetuning_modifiers</span><span class="kw">:</span></span>
|
||||
<span id="cb10-8"><a href="#cb10-8" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">ConstantPruningModifier</span><span class="kw">:</span></span>
|
||||
<span id="cb10-9"><a href="#cb10-9" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">targets</span><span class="kw">:</span><span class="at"> </span><span class="kw">[</span></span>
|
||||
<span id="cb10-10"><a href="#cb10-10" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="st">'re:.*q_proj.weight'</span><span class="kw">,</span></span>
|
||||
<span id="cb10-11"><a href="#cb10-11" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="st">'re:.*k_proj.weight'</span><span class="kw">,</span></span>
|
||||
<span id="cb10-12"><a href="#cb10-12" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="st">'re:.*v_proj.weight'</span><span class="kw">,</span></span>
|
||||
<span id="cb10-13"><a href="#cb10-13" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="st">'re:.*o_proj.weight'</span><span class="kw">,</span></span>
|
||||
<span id="cb10-14"><a href="#cb10-14" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="st">'re:.*gate_proj.weight'</span><span class="kw">,</span></span>
|
||||
<span id="cb10-15"><a href="#cb10-15" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="st">'re:.*up_proj.weight'</span><span class="kw">,</span></span>
|
||||
<span id="cb10-16"><a href="#cb10-16" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="st">'re:.*down_proj.weight'</span><span class="kw">,</span></span>
|
||||
<span id="cb10-17"><a href="#cb10-17" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">]</span></span>
|
||||
<span id="cb10-18"><a href="#cb10-18" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">start</span><span class="kw">:</span><span class="at"> </span><span class="dv">0</span></span>
|
||||
<span id="cb10-19"><a href="#cb10-19" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">save_compressed</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
<p>This plugin <strong>does not apply pruning or sparsification itself</strong> — it is intended for <strong>fine-tuning models that have already been sparsified</strong>.</p>
|
||||
<p>Pre-sparsified checkpoints can be:
|
||||
- Generated using <a href="https://github.com/vllm-project/llm-compressor">LLMCompressor</a>
|
||||
@@ -838,22 +727,22 @@ By identifying the top n% of layers with the highest SNR, you can optimize train
|
||||
<p>After fine-tuning your sparse model, you can leverage vLLM for efficient inference.
|
||||
You can also use LLMCompressor to apply additional quantization to your fine-tuned
|
||||
sparse model before inference for even greater performance benefits.:</p>
|
||||
<div class="sourceCode" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> vllm <span class="im">import</span> LLM, SamplingParams</span>
|
||||
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>prompts <span class="op">=</span> [</span>
|
||||
<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a> <span class="st">"Hello, my name is"</span>,</span>
|
||||
<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a> <span class="st">"The president of the United States is"</span>,</span>
|
||||
<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a> <span class="st">"The capital of France is"</span>,</span>
|
||||
<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a> <span class="st">"The future of AI is"</span>,</span>
|
||||
<span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a>]</span>
|
||||
<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a>sampling_params <span class="op">=</span> SamplingParams(temperature<span class="op">=</span><span class="fl">0.8</span>, top_p<span class="op">=</span><span class="fl">0.95</span>)</span>
|
||||
<span id="cb16-10"><a href="#cb16-10" aria-hidden="true" tabindex="-1"></a>llm <span class="op">=</span> LLM(<span class="st">"path/to/your/sparse/model"</span>)</span>
|
||||
<span id="cb16-11"><a href="#cb16-11" aria-hidden="true" tabindex="-1"></a>outputs <span class="op">=</span> llm.generate(prompts, sampling_params)</span>
|
||||
<span id="cb16-12"><a href="#cb16-12" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb16-13"><a href="#cb16-13" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> output <span class="kw">in</span> outputs:</span>
|
||||
<span id="cb16-14"><a href="#cb16-14" aria-hidden="true" tabindex="-1"></a> prompt <span class="op">=</span> output.prompt</span>
|
||||
<span id="cb16-15"><a href="#cb16-15" aria-hidden="true" tabindex="-1"></a> generated_text <span class="op">=</span> output.outputs[<span class="dv">0</span>].text</span>
|
||||
<span id="cb16-16"><a href="#cb16-16" aria-hidden="true" tabindex="-1"></a> <span class="bu">print</span>(<span class="ss">f"Prompt: </span><span class="sc">{</span>prompt<span class="sc">!r}</span><span class="ss">, Generated text: </span><span class="sc">{</span>generated_text<span class="sc">!r}</span><span class="ss">"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
<div class="sourceCode" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> vllm <span class="im">import</span> LLM, SamplingParams</span>
|
||||
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>prompts <span class="op">=</span> [</span>
|
||||
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a> <span class="st">"Hello, my name is"</span>,</span>
|
||||
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a> <span class="st">"The president of the United States is"</span>,</span>
|
||||
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a> <span class="st">"The capital of France is"</span>,</span>
|
||||
<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a> <span class="st">"The future of AI is"</span>,</span>
|
||||
<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a>]</span>
|
||||
<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a>sampling_params <span class="op">=</span> SamplingParams(temperature<span class="op">=</span><span class="fl">0.8</span>, top_p<span class="op">=</span><span class="fl">0.95</span>)</span>
|
||||
<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a>llm <span class="op">=</span> LLM(<span class="st">"path/to/your/sparse/model"</span>)</span>
|
||||
<span id="cb11-11"><a href="#cb11-11" aria-hidden="true" tabindex="-1"></a>outputs <span class="op">=</span> llm.generate(prompts, sampling_params)</span>
|
||||
<span id="cb11-12"><a href="#cb11-12" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb11-13"><a href="#cb11-13" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> output <span class="kw">in</span> outputs:</span>
|
||||
<span id="cb11-14"><a href="#cb11-14" aria-hidden="true" tabindex="-1"></a> prompt <span class="op">=</span> output.prompt</span>
|
||||
<span id="cb11-15"><a href="#cb11-15" aria-hidden="true" tabindex="-1"></a> generated_text <span class="op">=</span> output.outputs[<span class="dv">0</span>].text</span>
|
||||
<span id="cb11-16"><a href="#cb11-16" aria-hidden="true" tabindex="-1"></a> <span class="bu">print</span>(<span class="ss">f"Prompt: </span><span class="sc">{</span>prompt<span class="sc">!r}</span><span class="ss">, Generated text: </span><span class="sc">{</span>generated_text<span class="sc">!r}</span><span class="ss">"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
<p>For more details on vLLM’s capabilities and advanced configuration options, see the <a href="https://docs.vllm.ai/">official vLLM documentation</a>.</p>
|
||||
</section>
|
||||
<section id="learn-more" class="level3">
|
||||
@@ -863,6 +752,126 @@ sparse model before inference for even greater performance benefits.:</p>
|
||||
<p>Please see reference <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/llm_compressor">here</a></p>
|
||||
</section>
|
||||
</section>
|
||||
<section id="language-model-evaluation-harness-lm-eval" class="level2">
|
||||
<h2 class="anchored" data-anchor-id="language-model-evaluation-harness-lm-eval">Language Model Evaluation Harness (LM Eval)</h2>
|
||||
<p>Run evaluation on model using the popular lm-evaluation-harness library.</p>
|
||||
<p>See https://github.com/EleutherAI/lm-evaluation-harness</p>
|
||||
<section id="usage-4" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="usage-4">Usage</h3>
|
||||
<div class="sourceCode" id="cb12"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plugins</span><span class="kw">:</span></span>
|
||||
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> axolotl.integrations.lm_eval.LMEvalPlugin</span></span>
|
||||
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a><span class="fu">lm_eval_tasks</span><span class="kw">:</span></span>
|
||||
<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> gsm8k</span></span>
|
||||
<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> hellaswag</span></span>
|
||||
<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> arc_easy</span></span>
|
||||
<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a><span class="fu">lm_eval_batch_size</span><span class="kw">:</span><span class="co"> # Batch size for evaluation</span></span>
|
||||
<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a><span class="fu">output_dir</span><span class="kw">:</span><span class="co"> # Directory to save evaluation results</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
</section>
|
||||
<section id="citation-2" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="citation-2">Citation</h3>
|
||||
<div class="sourceCode" id="cb13"><pre class="sourceCode bib code-with-copy"><code class="sourceCode bibtex"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="va">@misc</span>{<span class="ot">eval</span>-<span class="ot">harness</span>,</span>
|
||||
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a> <span class="dt">author</span> = {Gao, Leo and Tow, Jonathan and Abbasi, Baber and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and Le Noac'h, Alain and Li, Haonan and McDonell, Kyle and Muennighoff, Niklas and Ociepa, Chris and Phang, Jason and Reynolds, Laria and Schoelkopf, Hailey and Skowron, Aviya and Sutawika, Lintang and Tang, Eric and Thite, Anish and Wang, Ben and Wang, Kevin and Zou, Andy},</span>
|
||||
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a> <span class="dt">title</span> = {A framework for few-shot language model evaluation},</span>
|
||||
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a> <span class="dt">month</span> = 07,</span>
|
||||
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a> <span class="dt">year</span> = 2024,</span>
|
||||
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a> <span class="dt">publisher</span> = {Zenodo},</span>
|
||||
<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a> <span class="dt">version</span> = {v0.4.3},</span>
|
||||
<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a> <span class="dt">doi</span> = {10.5281/zenodo.12608602},</span>
|
||||
<span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a> <span class="dt">url</span> = {https://zenodo.org/records/12608602}</span>
|
||||
<span id="cb13-10"><a href="#cb13-10" 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>Please see reference <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/lm_eval">here</a></p>
|
||||
</section>
|
||||
</section>
|
||||
<section id="liger-kernels" class="level2">
|
||||
<h2 class="anchored" data-anchor-id="liger-kernels">Liger Kernels</h2>
|
||||
<p>Liger Kernel provides efficient Triton kernels for LLM training, offering:</p>
|
||||
<ul>
|
||||
<li>20% increase in multi-GPU training throughput</li>
|
||||
<li>60% reduction in memory usage</li>
|
||||
<li>Compatibility with both FSDP and DeepSpeed</li>
|
||||
</ul>
|
||||
<p>See https://github.com/linkedin/Liger-Kernel</p>
|
||||
<section id="usage-5" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="usage-5">Usage</h3>
|
||||
<div class="sourceCode" id="cb14"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plugins</span><span class="kw">:</span></span>
|
||||
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> axolotl.integrations.liger.LigerPlugin</span></span>
|
||||
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a><span class="fu">liger_rope</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
|
||||
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a><span class="fu">liger_rms_norm</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
|
||||
<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a><span class="fu">liger_glu_activation</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
|
||||
<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a><span class="fu">liger_layer_norm</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
|
||||
<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a><span class="fu">liger_fused_linear_cross_entropy</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
</section>
|
||||
<section id="supported-models-1" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="supported-models-1">Supported Models</h3>
|
||||
<ul>
|
||||
<li>deepseek_v2</li>
|
||||
<li>gemma</li>
|
||||
<li>gemma2</li>
|
||||
<li>gemma3</li>
|
||||
<li>granite</li>
|
||||
<li>jamba</li>
|
||||
<li>llama</li>
|
||||
<li>mistral</li>
|
||||
<li>mixtral</li>
|
||||
<li>mllama</li>
|
||||
<li>mllama_text_model</li>
|
||||
<li>olmo2</li>
|
||||
<li>paligemma</li>
|
||||
<li>phi3</li>
|
||||
<li>qwen2</li>
|
||||
<li>qwen2_5_vl</li>
|
||||
<li>qwen2_vl</li>
|
||||
</ul>
|
||||
</section>
|
||||
<section id="citation-3" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="citation-3">Citation</h3>
|
||||
<div class="sourceCode" id="cb15"><pre class="sourceCode bib code-with-copy"><code class="sourceCode bibtex"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="va">@article</span>{<span class="ot">hsu2024ligerkernelefficienttriton</span>,</span>
|
||||
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a> <span class="dt">title</span>={Liger Kernel: Efficient Triton Kernels for LLM Training},</span>
|
||||
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a> <span class="dt">author</span>={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen},</span>
|
||||
<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a> <span class="dt">year</span>={2024},</span>
|
||||
<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a> <span class="dt">eprint</span>={2410.10989},</span>
|
||||
<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a> <span class="dt">archivePrefix</span>={arXiv},</span>
|
||||
<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a> <span class="dt">primaryClass</span>={cs.LG},</span>
|
||||
<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a> <span class="dt">url</span>={https://arxiv.org/abs/2410.10989},</span>
|
||||
<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a> <span class="dt">journal</span>={arXiv preprint arXiv:2410.10989},</span>
|
||||
<span id="cb15-10"><a href="#cb15-10" 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>Please see reference <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/liger">here</a></p>
|
||||
</section>
|
||||
</section>
|
||||
<section id="spectrum" class="level2">
|
||||
<h2 class="anchored" data-anchor-id="spectrum">Spectrum</h2>
|
||||
<p>by Eric Hartford, Lucas Atkins, Fernando Fernandes, David Golchinfar</p>
|
||||
<p>This plugin contains code to freeze the bottom fraction of modules in a model, based on the Signal-to-Noise Ratio (SNR).</p>
|
||||
<p>See https://github.com/cognitivecomputations/spectrum</p>
|
||||
<section id="overview" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="overview">Overview</h3>
|
||||
<p>Spectrum is a tool for scanning and evaluating the Signal-to-Noise Ratio (SNR) of layers in large language models.
|
||||
By identifying the top n% of layers with the highest SNR, you can optimize training efficiency.</p>
|
||||
</section>
|
||||
<section id="usage-6" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="usage-6">Usage</h3>
|
||||
<div class="sourceCode" id="cb16"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plugins</span><span class="kw">:</span></span>
|
||||
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> axolotl.integrations.spectrum.SpectrumPlugin</span></span>
|
||||
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a><span class="fu">spectrum_top_fraction</span><span class="kw">:</span><span class="at"> </span><span class="fl">0.5</span></span>
|
||||
<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a><span class="fu">spectrum_model_name</span><span class="kw">:</span><span class="at"> meta-llama/Meta-Llama-3.1-8B</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
</section>
|
||||
<section id="citation-4" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="citation-4">Citation</h3>
|
||||
<div class="sourceCode" id="cb17"><pre class="sourceCode bib code-with-copy"><code class="sourceCode bibtex"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="va">@misc</span>{<span class="ot">hartford2024spectrumtargetedtrainingsignal</span>,</span>
|
||||
<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a> <span class="dt">title</span>={Spectrum: Targeted Training on Signal to Noise Ratio},</span>
|
||||
<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a> <span class="dt">author</span>={Eric Hartford and Lucas Atkins and Fernando Fernandes Neto and David Golchinfar},</span>
|
||||
<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a> <span class="dt">year</span>={2024},</span>
|
||||
<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a> <span class="dt">eprint</span>={2406.06623},</span>
|
||||
<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a> <span class="dt">archivePrefix</span>={arXiv},</span>
|
||||
<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a> <span class="dt">primaryClass</span>={cs.LG},</span>
|
||||
<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a> <span class="dt">url</span>={https://arxiv.org/abs/2406.06623},</span>
|
||||
<span id="cb17-9"><a href="#cb17-9" 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>Please see reference <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/spectrum">here</a></p>
|
||||
</section>
|
||||
</section>
|
||||
<section id="adding-a-new-integration" class="level2">
|
||||
<h2 class="anchored" data-anchor-id="adding-a-new-integration">Adding a new integration</h2>
|
||||
<p>Plugins can be used to customize the behavior of the training pipeline through <a href="https://en.wikipedia.org/wiki/Hooking">hooks</a>. See <a href="https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/integrations/base.py"><code>axolotl.integrations.BasePlugin</code></a> for the possible hooks.</p>
|
||||
@@ -903,10 +912,10 @@ Warning
|
||||
</div>
|
||||
<div class="callout-body-container callout-body">
|
||||
<p>If you could not load your integration, please ensure you are pip installing in editable mode.</p>
|
||||
<div class="sourceCode" id="cb17"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="ex">pip</span> install <span class="at">-e</span> .</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
<div class="sourceCode" id="cb18"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="ex">pip</span> install <span class="at">-e</span> .</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
<p>and correctly spelled the integration name in the config file.</p>
|
||||
<div class="sourceCode" id="cb18"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plugins</span><span class="kw">:</span></span>
|
||||
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> axolotl.integrations.your_integration_name.YourIntegrationPlugin</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
<div class="sourceCode" id="cb19"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plugins</span><span class="kw">:</span></span>
|
||||
<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> axolotl.integrations.your_integration_name.YourIntegrationPlugin</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="callout callout-style-default callout-note callout-titled">
|
||||
|
||||
35
search.json
35
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@@ -3070,6 +3070,17 @@
|
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|
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|
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|
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|
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|
||||
"title": "Custom Integrations",
|
||||
"section": "DenseMixer",
|
||||
"text": "DenseMixer\nSee DenseMixer\nSimply add the following to your axolotl YAML config:\nplugins:\n - axolotl.integrations.densemixer.DenseMixerPlugin\nPlease see reference here",
|
||||
"crumbs": [
|
||||
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|
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|
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|
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|
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|
||||
"title": "Custom Integrations",
|
||||
"section": "Liger Kernels",
|
||||
"text": "Liger Kernels\nLiger Kernel provides efficient Triton kernels for LLM training, offering:\n\n20% increase in multi-GPU training throughput\n60% reduction in memory usage\nCompatibility with both FSDP and DeepSpeed\n\nSee https://github.com/linkedin/Liger-Kernel\n\nUsage\nplugins:\n - axolotl.integrations.liger.LigerPlugin\nliger_rope: true\nliger_rms_norm: true\nliger_glu_activation: true\nliger_layer_norm: true\nliger_fused_linear_cross_entropy: true\n\n\nSupported Models\n\ndeepseek_v2\ngemma\ngemma2\ngemma3\ngranite\njamba\nllama\nmistral\nmixtral\nmllama\nmllama_text_model\nolmo2\npaligemma\nphi3\nqwen2\nqwen2_5_vl\nqwen2_vl\n\n\n\nCitation\n@article{hsu2024ligerkernelefficienttriton,\n title={Liger Kernel: Efficient Triton Kernels for LLM Training},\n author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen},\n year={2024},\n eprint={2410.10989},\n archivePrefix={arXiv},\n primaryClass={cs.LG},\n url={https://arxiv.org/abs/2410.10989},\n journal={arXiv preprint arXiv:2410.10989},\n}\nPlease see reference here",
|
||||
"section": "LLMCompressor",
|
||||
"text": "LLMCompressor\nFine-tune sparsified models in Axolotl using Neural Magic’s LLMCompressor.\nThis integration enables fine-tuning of models sparsified using LLMCompressor within the Axolotl training framework. By combining LLMCompressor’s model compression capabilities with Axolotl’s distributed training pipelines, users can efficiently fine-tune sparse models at scale.\nIt uses Axolotl’s plugin system to hook into the fine-tuning flows while maintaining sparsity throughout training.\n\n\nRequirements\n\nAxolotl with llmcompressor extras:\npip install \"axolotl[llmcompressor]\"\nRequires llmcompressor >= 0.5.1\n\nThis will install all necessary dependencies to fine-tune sparsified models using the integration.\n\n\n\nUsage\nTo enable sparse fine-tuning with this integration, include the plugin in your Axolotl config:\nplugins:\n - axolotl.integrations.llm_compressor.LLMCompressorPlugin\n\nllmcompressor:\n recipe:\n finetuning_stage:\n finetuning_modifiers:\n ConstantPruningModifier:\n targets: [\n 're:.*q_proj.weight',\n 're:.*k_proj.weight',\n 're:.*v_proj.weight',\n 're:.*o_proj.weight',\n 're:.*gate_proj.weight',\n 're:.*up_proj.weight',\n 're:.*down_proj.weight',\n ]\n start: 0\n save_compressed: true\nThis plugin does not apply pruning or sparsification itself — it is intended for fine-tuning models that have already been sparsified.\nPre-sparsified checkpoints can be:\n- Generated using LLMCompressor\n- Downloaded from Neural Magic’s Hugging Face page\n- Any custom LLM with compatible sparsity patterns that you’ve created yourself\nTo learn more about writing and customizing LLMCompressor recipes, refer to the official documentation:\nhttps://github.com/vllm-project/llm-compressor/blob/main/README.md\n\n\nStorage Optimization with save_compressed\nSetting save_compressed: true in your configuration enables saving models in a compressed format, which:\n- Reduces disk space usage by approximately 40%\n- Maintains compatibility with vLLM for accelerated inference\n- Maintains compatibility with llmcompressor for further optimization (example: quantization)\nThis option is highly recommended when working with sparse models to maximize the benefits of model compression.\n\n\nExample Config\nSee examples/llama-3/sparse-finetuning.yaml for a complete example.\n\n\n\nInference with vLLM\nAfter fine-tuning your sparse model, you can leverage vLLM for efficient inference.\nYou can also use LLMCompressor to apply additional quantization to your fine-tuned\nsparse model before inference for even greater performance benefits.:\nfrom vllm import LLM, SamplingParams\n\nprompts = [\n \"Hello, my name is\",\n \"The president of the United States is\",\n \"The capital of France is\",\n \"The future of AI is\",\n]\nsampling_params = SamplingParams(temperature=0.8, top_p=0.95)\nllm = LLM(\"path/to/your/sparse/model\")\noutputs = llm.generate(prompts, sampling_params)\n\nfor output in outputs:\n prompt = output.prompt\n generated_text = output.outputs[0].text\n print(f\"Prompt: {prompt!r}, Generated text: {generated_text!r}\")\nFor more details on vLLM’s capabilities and advanced configuration options, see the official vLLM documentation.\n\n\nLearn More\nFor details on available sparsity and quantization schemes, fine-tuning recipes, and usage examples, visit the official LLMCompressor repository:\nhttps://github.com/vllm-project/llm-compressor\nPlease see reference here",
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"section": "Spectrum",
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"text": "Spectrum\nby Eric Hartford, Lucas Atkins, Fernando Fernandes, David Golchinfar\nThis plugin contains code to freeze the bottom fraction of modules in a model, based on the Signal-to-Noise Ratio (SNR).\nSee https://github.com/cognitivecomputations/spectrum\n\nOverview\nSpectrum is a tool for scanning and evaluating the Signal-to-Noise Ratio (SNR) of layers in large language models.\nBy identifying the top n% of layers with the highest SNR, you can optimize training efficiency.\n\n\nUsage\nplugins:\n - axolotl.integrations.spectrum.SpectrumPlugin\n\nspectrum_top_fraction: 0.5\nspectrum_model_name: meta-llama/Meta-Llama-3.1-8B\n\n\nCitation\n@misc{hartford2024spectrumtargetedtrainingsignal,\n title={Spectrum: Targeted Training on Signal to Noise Ratio},\n author={Eric Hartford and Lucas Atkins and Fernando Fernandes Neto and David Golchinfar},\n year={2024},\n eprint={2406.06623},\n archivePrefix={arXiv},\n primaryClass={cs.LG},\n url={https://arxiv.org/abs/2406.06623},\n}\nPlease see reference here",
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"section": "Liger Kernels",
|
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"text": "Liger Kernels\nLiger Kernel provides efficient Triton kernels for LLM training, offering:\n\n20% increase in multi-GPU training throughput\n60% reduction in memory usage\nCompatibility with both FSDP and DeepSpeed\n\nSee https://github.com/linkedin/Liger-Kernel\n\nUsage\nplugins:\n - axolotl.integrations.liger.LigerPlugin\nliger_rope: true\nliger_rms_norm: true\nliger_glu_activation: true\nliger_layer_norm: true\nliger_fused_linear_cross_entropy: true\n\n\nSupported Models\n\ndeepseek_v2\ngemma\ngemma2\ngemma3\ngranite\njamba\nllama\nmistral\nmixtral\nmllama\nmllama_text_model\nolmo2\npaligemma\nphi3\nqwen2\nqwen2_5_vl\nqwen2_vl\n\n\n\nCitation\n@article{hsu2024ligerkernelefficienttriton,\n title={Liger Kernel: Efficient Triton Kernels for LLM Training},\n author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen},\n year={2024},\n eprint={2410.10989},\n archivePrefix={arXiv},\n primaryClass={cs.LG},\n url={https://arxiv.org/abs/2410.10989},\n journal={arXiv preprint arXiv:2410.10989},\n}\nPlease see reference here",
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"text": "LLMCompressor\nFine-tune sparsified models in Axolotl using Neural Magic’s LLMCompressor.\nThis integration enables fine-tuning of models sparsified using LLMCompressor within the Axolotl training framework. By combining LLMCompressor’s model compression capabilities with Axolotl’s distributed training pipelines, users can efficiently fine-tune sparse models at scale.\nIt uses Axolotl’s plugin system to hook into the fine-tuning flows while maintaining sparsity throughout training.\n\n\nRequirements\n\nAxolotl with llmcompressor extras:\npip install \"axolotl[llmcompressor]\"\nRequires llmcompressor >= 0.5.1\n\nThis will install all necessary dependencies to fine-tune sparsified models using the integration.\n\n\n\nUsage\nTo enable sparse fine-tuning with this integration, include the plugin in your Axolotl config:\nplugins:\n - axolotl.integrations.llm_compressor.LLMCompressorPlugin\n\nllmcompressor:\n recipe:\n finetuning_stage:\n finetuning_modifiers:\n ConstantPruningModifier:\n targets: [\n 're:.*q_proj.weight',\n 're:.*k_proj.weight',\n 're:.*v_proj.weight',\n 're:.*o_proj.weight',\n 're:.*gate_proj.weight',\n 're:.*up_proj.weight',\n 're:.*down_proj.weight',\n ]\n start: 0\n save_compressed: true\nThis plugin does not apply pruning or sparsification itself — it is intended for fine-tuning models that have already been sparsified.\nPre-sparsified checkpoints can be:\n- Generated using LLMCompressor\n- Downloaded from Neural Magic’s Hugging Face page\n- Any custom LLM with compatible sparsity patterns that you’ve created yourself\nTo learn more about writing and customizing LLMCompressor recipes, refer to the official documentation:\nhttps://github.com/vllm-project/llm-compressor/blob/main/README.md\n\n\nStorage Optimization with save_compressed\nSetting save_compressed: true in your configuration enables saving models in a compressed format, which:\n- Reduces disk space usage by approximately 40%\n- Maintains compatibility with vLLM for accelerated inference\n- Maintains compatibility with llmcompressor for further optimization (example: quantization)\nThis option is highly recommended when working with sparse models to maximize the benefits of model compression.\n\n\nExample Config\nSee examples/llama-3/sparse-finetuning.yaml for a complete example.\n\n\n\nInference with vLLM\nAfter fine-tuning your sparse model, you can leverage vLLM for efficient inference.\nYou can also use LLMCompressor to apply additional quantization to your fine-tuned\nsparse model before inference for even greater performance benefits.:\nfrom vllm import LLM, SamplingParams\n\nprompts = [\n \"Hello, my name is\",\n \"The president of the United States is\",\n \"The capital of France is\",\n \"The future of AI is\",\n]\nsampling_params = SamplingParams(temperature=0.8, top_p=0.95)\nllm = LLM(\"path/to/your/sparse/model\")\noutputs = llm.generate(prompts, sampling_params)\n\nfor output in outputs:\n prompt = output.prompt\n generated_text = output.outputs[0].text\n print(f\"Prompt: {prompt!r}, Generated text: {generated_text!r}\")\nFor more details on vLLM’s capabilities and advanced configuration options, see the official vLLM documentation.\n\n\nLearn More\nFor details on available sparsity and quantization schemes, fine-tuning recipes, and usage examples, visit the official LLMCompressor repository:\nhttps://github.com/vllm-project/llm-compressor\nPlease see reference here",
|
||||
"section": "Spectrum",
|
||||
"text": "Spectrum\nby Eric Hartford, Lucas Atkins, Fernando Fernandes, David Golchinfar\nThis plugin contains code to freeze the bottom fraction of modules in a model, based on the Signal-to-Noise Ratio (SNR).\nSee https://github.com/cognitivecomputations/spectrum\n\nOverview\nSpectrum is a tool for scanning and evaluating the Signal-to-Noise Ratio (SNR) of layers in large language models.\nBy identifying the top n% of layers with the highest SNR, you can optimize training efficiency.\n\n\nUsage\nplugins:\n - axolotl.integrations.spectrum.SpectrumPlugin\n\nspectrum_top_fraction: 0.5\nspectrum_model_name: meta-llama/Meta-Llama-3.1-8B\n\n\nCitation\n@misc{hartford2024spectrumtargetedtrainingsignal,\n title={Spectrum: Targeted Training on Signal to Noise Ratio},\n author={Eric Hartford and Lucas Atkins and Fernando Fernandes Neto and David Golchinfar},\n year={2024},\n eprint={2406.06623},\n archivePrefix={arXiv},\n primaryClass={cs.LG},\n url={https://arxiv.org/abs/2406.06623},\n}\nPlease see reference here",
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||||
<url>
|
||||
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||||
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||||
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||||
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||||
<url>
|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
<loc>https://docs.axolotl.ai/docs/api/prompt_strategies.kto.llama3.html</loc>
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
<loc>https://docs.axolotl.ai/docs/api/cli.quantize.html</loc>
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||||
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||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/api/integrations.lm_eval.args.html</loc>
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||||
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||||
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||||
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|
||||
<url>
|
||||
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||||
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||||
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|
||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/api/core.builders.causal.html</loc>
|
||||
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|
||||
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||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/api/core.trainers.relora.html</loc>
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||||
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||||
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||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/api/models.mamba.modeling_mamba.html</loc>
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||||
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||||
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||||
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||||
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||||
<loc>https://docs.axolotl.ai/docs/api/monkeypatch.gradient_checkpointing.offload_cpu.html</loc>
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||||
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||||
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||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/api/core.trainers.mamba.html</loc>
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||||
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||||
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||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/api/core.datasets.transforms.chat_builder.html</loc>
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
<url>
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||||
<loc>https://docs.axolotl.ai/docs/api/core.chat.format.llama3x.html</loc>
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||||
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||||
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||||
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||||
<url>
|
||||
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||||
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||||
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||||
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||||
<url>
|
||||
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||||
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||||
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||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/api/core.trainers.mixins.optimizer.html</loc>
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||||
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||||
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||||
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||||
<url>
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
<url>
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||||
<loc>https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.user_defined.html</loc>
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||||
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||||
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||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/api/prompt_tokenizers.html</loc>
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||||
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||||
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||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/api/common.const.html</loc>
|
||||
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|
||||
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|
||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/fsdp_qlora.html</loc>
|
||||
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|
||||
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||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/custom_integrations.html</loc>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/faq.html</loc>
|
||||
<lastmod>2025-07-07T19:24:01.495Z</lastmod>
|
||||
<lastmod>2025-07-07T21:05:27.503Z</lastmod>
|
||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/lora_optims.html</loc>
|
||||
<lastmod>2025-07-07T19:24:01.498Z</lastmod>
|
||||
<lastmod>2025-07-07T21:05:27.507Z</lastmod>
|
||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/rlhf.html</loc>
|
||||
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|
||||
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|
||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/amd_hpc.html</loc>
|
||||
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|
||||
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|
||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/installation.html</loc>
|
||||
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|
||||
<lastmod>2025-07-07T21:05:27.507Z</lastmod>
|
||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/multipack.html</loc>
|
||||
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|
||||
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|
||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/dataset_preprocessing.html</loc>
|
||||
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|
||||
<lastmod>2025-07-07T21:05:27.503Z</lastmod>
|
||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/dataset_loading.html</loc>
|
||||
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|
||||
<lastmod>2025-07-07T21:05:27.503Z</lastmod>
|
||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/dataset-formats/inst_tune.html</loc>
|
||||
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|
||||
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||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/dataset-formats/template_free.html</loc>
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||||
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||||
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||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/dataset-formats/index.html</loc>
|
||||
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|
||||
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|
||||
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|
||||
<url>
|
||||
<loc>https://docs.axolotl.ai/docs/dataset-formats/pretraining.html</loc>
|
||||
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|
||||
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|
||||
</url>
|
||||
</urlset>
|
||||
|
||||
Reference in New Issue
Block a user