2041 lines
306 KiB
HTML
2041 lines
306 KiB
HTML
<!DOCTYPE html>
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<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
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<meta charset="utf-8">
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<meta name="generator" content="quarto-1.8.26">
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<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
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<title>Fine-Tune Qwen3 14B with Axolotl – Axolotl</title>
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<script src="https://cdn.jsdelivr.net/npm/jquery@3.5.1/dist/jquery.min.js" integrity="sha384-ZvpUoO/+PpLXR1lu4jmpXWu80pZlYUAfxl5NsBMWOEPSjUn/6Z/hRTt8+pR6L4N2" crossorigin="anonymous"></script><script src="../../site_libs/quarto-nav/quarto-nav.js"></script>
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gtag('js', new Date());
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gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
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<link rel="stylesheet" href="../../styles.css">
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</head>
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<body class="nav-sidebar docked nav-fixed quarto-light">
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<div id="quarto-search-results"></div>
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<header id="quarto-header" class="headroom fixed-top">
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<nav class="navbar navbar-expand " data-bs-theme="dark">
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<div class="navbar-container container-fluid">
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<div class="navbar-brand-container mx-auto">
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<a href="../../index.html" class="navbar-brand navbar-brand-logo">
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<img src="../../image/axolotl_logo_digital_white.svg" alt="" class="navbar-logo light-content">
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<img src="../../image/axolotl_logo_digital_white.svg" alt="" class="navbar-logo dark-content">
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<div id="quarto-search" class="" title="Search"></div>
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</div> <!-- /container-fluid -->
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</nav>
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<nav class="quarto-secondary-nav">
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<div class="container-fluid d-flex">
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<button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" role="button" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
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<i class="bi bi-layout-text-sidebar-reverse"></i>
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</button>
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<nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item">Fine-Tune Qwen3 14B with Axolotl</li></ol></nav>
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<a class="flex-grow-1" role="navigation" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
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</a>
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</nav>
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</header>
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<!-- content -->
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<div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
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<!-- sidebar -->
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<nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal quarto-sidebar-collapse-item sidebar-navigation docked overflow-auto">
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<div class="sidebar-menu-container">
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<ul class="list-unstyled mt-1">
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<li class="sidebar-item">
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<div class="sidebar-item-container">
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<a href="../../index.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Home</span></a>
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</div>
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<li class="sidebar-item sidebar-item-section">
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<a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-1" role="navigation" aria-expanded="true">
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<span class="menu-text">Getting Started</span></a>
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<a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-1" role="navigation" aria-expanded="true" aria-label="Toggle section">
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<div class="sidebar-item-container">
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<a href="../../docs/getting-started.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Quickstart</span></a>
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<li class="sidebar-item">
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<div class="sidebar-item-container">
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<a href="../../docs/installation.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Installation</span></a>
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<a href="../../docs/inference.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Inference and Merging</span></a>
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<a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false">
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<span class="menu-text">Model Guides</span></a>
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<span class="menu-text">Kimi Linear</span></a>
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<a href="../../docs/models/plano.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Plano Orchestrator</span></a>
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<a href="../../docs/models/mimo.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">MiMo</span></a>
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<a href="../../docs/models/internvl3_5.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">InternVL 3.5</span></a>
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<a href="../../docs/models/olmo3.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">OLMo 3</span></a>
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<span class="menu-text">Trinity</span></a>
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<span class="menu-text">Arcee AFM</span></a>
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<span class="menu-text">Ministral3</span></a>
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<div class="sidebar-item-container">
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<a href="../../docs/models/ministral3.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Ministral3</span></a>
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<div class="sidebar-item-container">
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<a href="../../docs/models/ministral3/think.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Ministral 3 Thinking</span></a>
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<div class="sidebar-item-container">
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<a href="../../docs/models/ministral3/vision.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Ministral 3 Vision</span></a>
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<a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
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<span class="menu-text">Magistral</span></a>
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<span class="menu-text">Magistral</span></a>
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<a href="../../docs/models/magistral/think.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Magistral Thinking</span></a>
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<a href="../../docs/models/magistral/vision.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Magistral Vision</span></a>
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<div class="sidebar-item-container">
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<a href="../../docs/models/ministral.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Ministral</span></a>
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<a href="../../docs/models/mistral-small.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Mistral Small 3.1/3.2</span></a>
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<span class="menu-text">Voxtral</span></a>
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<a href="../../docs/models/mistral.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Mistral 7B</span></a>
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<span class="menu-text">Llama 4</span></a>
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<span class="menu-text">Llama 2</span></a>
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<a href="../../docs/models/qwen3-next.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Qwen 3 Next</span></a>
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<div class="sidebar-item-container">
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<a href="../../docs/models/qwen3.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Qwen 3</span></a>
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</div>
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<a href="../../docs/models/gemma3n.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Gemma 3n</span></a>
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<a href="../../docs/models/apertus.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Apertus</span></a>
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||
</nav>
|
||
</div>
|
||
<!-- main -->
|
||
<main class="content" id="quarto-document-content">
|
||
|
||
<header id="title-block-header" class="quarto-title-block default">
|
||
<div class="quarto-title">
|
||
<h1 class="title">Fine-Tune Qwen3 14B with Axolotl</h1>
|
||
</div>
|
||
|
||
|
||
|
||
<div class="quarto-title-meta">
|
||
|
||
|
||
|
||
|
||
</div>
|
||
|
||
|
||
|
||
</header>
|
||
|
||
|
||
<p><a href="https://github.com/axolotl-ai-cloud/axolotl"><img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"></a></p>
|
||
<p>Axolotl is the most performant LLM post-training framework available, delivering faster training with efficient, consistent and stable performance. Train your workload and ship your product 30% faster; saving you both time and money.</p>
|
||
<ul>
|
||
<li>⭐ us on <a href="https://github.com/axolotl-ai-cloud/axolotl">GitHub</a></li>
|
||
<li>📜 Read the <a href="http://docs.axolotl.ai/">Docs</a></li>
|
||
<li>💬 Chat with us on <a href="https://discord.gg/mnpEYgRUmD">Discord</a></li>
|
||
<li>📰 Get updates on <a href="https://x.com/axolotl_ai">X/Twitter</a></li>
|
||
</ul>
|
||
<section id="installation" class="level1">
|
||
<h1>Installation</h1>
|
||
<p>Axolotl is easy to install from <a href="https://pypi.org/project/axolotl/">pip</a>, or use our <a href="http://docs.axolotl.ai/docs/docker.html">pre-built Docker images</a> for a hassle free dependency experience. See our <a href="http://docs.axolotl.ai/docs/installation.html">docs</a> for more information.</p>
|
||
<div id="cell-3" class="cell">
|
||
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>capture</span>
|
||
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="co"># This step can take ~5-10 minutes to install dependencies</span></span>
|
||
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install <span class="op">--</span>no<span class="op">-</span>build<span class="op">-</span>isolation axolotl[flash<span class="op">-</span>attn]<span class="op">>=</span><span class="fl">0.9.1</span></span>
|
||
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install <span class="st">"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@318b7e2"</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
|
||
</div>
|
||
<section id="demo-talk-like-a-pirate" class="level2">
|
||
<h2 class="anchored" data-anchor-id="demo-talk-like-a-pirate">Demo: Talk Like a Pirate</h2>
|
||
<p>In this demo, we are training the model <strong><em>to respond like a pirate</em></strong>. This was chosen as a way to easily show how to train a model to respond in a certain style of your choosing (without being prompted) and is quite easy to validate within the scope of a Colab.</p>
|
||
<section id="upload-your-own-dataset-or-use-a-huggingface-dataset" class="level3">
|
||
<h3 class="anchored" data-anchor-id="upload-your-own-dataset-or-use-a-huggingface-dataset">Upload your own dataset or use a Huggingface dataset</h3>
|
||
<p>You can choose to use your own JSONL file from your own <a href="https://drive.google.com/drive/home">Google Drive</a>; for example downloading the <a href="https://huggingface.co/datasets/winglian/pirate-ultrachat-10k/blob/main/train.jsonl">Pirate-Ultrachat JSONL</a> to your Google Drive. JSONL datasets should be formatted similar to the <a href="https://cookbook.openai.com/examples/chat_finetuning_data_prep">OpenAI dataset format</a>.</p>
|
||
<p>You can also simply use the <a href="https://huggingface.co/datasets/winglian/pirate-ultrachat-10k"><code>winglian/pirate-ultrachat-10k</code></a> dataset directly.</p>
|
||
<div id="cell-6" class="cell">
|
||
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Default to HF dataset location</span></span>
|
||
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>dataset_id <span class="op">=</span> <span class="st">"winglian/pirate-ultrachat-10k"</span></span>
|
||
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>uploaded <span class="op">=</span> {}</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
|
||
</div>
|
||
<div id="cell-7" class="cell">
|
||
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> os</span>
|
||
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Optionally, upload your own JSONL to your Google Drive</span></span>
|
||
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>GOOGLE_DRIVE_PATH <span class="op">=</span> <span class="st">""</span> <span class="co"># ex: "MyDrive/Colab\ Notebooks/train.jsonl"</span></span>
|
||
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a><span class="co"># "Select All" permissions, or you may get the error:</span></span>
|
||
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a><span class="co"># "MessageError: Error: credential propagation was unsuccessful"</span></span>
|
||
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> GOOGLE_DRIVE_PATH:</span>
|
||
<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a> <span class="im">from</span> google.colab <span class="im">import</span> drive</span>
|
||
<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a> <span class="co"># Mount your Google Drive</span></span>
|
||
<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a> GOOGLE_DRIVE_MNT <span class="op">=</span> <span class="st">"/content/drive/"</span></span>
|
||
<span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a> drive.mount(GOOGLE_DRIVE_MNT, force_remount<span class="op">=</span><span class="va">True</span>)</span>
|
||
<span id="cb3-14"><a href="#cb3-14" aria-hidden="true" tabindex="-1"></a> tmp_path <span class="op">=</span> os.path.join(GOOGLE_DRIVE_MNT, GOOGLE_DRIVE_PATH.lstrip(<span class="st">"/"</span>))</span>
|
||
<span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a> <span class="co"># make sure file exists</span></span>
|
||
<span id="cb3-16"><a href="#cb3-16" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> <span class="kw">not</span> os.path.isfile(tmp_path):</span>
|
||
<span id="cb3-17"><a href="#cb3-17" aria-hidden="true" tabindex="-1"></a> <span class="cf">raise</span> <span class="pp">ValueError</span>(<span class="ss">f"File </span><span class="sc">{</span>tmp_path<span class="sc">}</span><span class="ss"> does not exist"</span>)</span>
|
||
<span id="cb3-18"><a href="#cb3-18" aria-hidden="true" tabindex="-1"></a> dataset_id <span class="op">=</span> tmp_path</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
|
||
</div>
|
||
</section>
|
||
</section>
|
||
</section>
|
||
<section id="configure-for-supervised-fine-tuning-sft" class="level1">
|
||
<h1>Configure for Supervised Fine-Tuning (SFT)</h1>
|
||
<div id="cell-9" class="cell" data-quarto-private-1="{"key":"colab","value":{"base_uri":"https://localhost:8080/","height":151,"referenced_widgets":["388f618924274d21a066f098f4f1e744","7c95f85a2b1f47a1bd846d110c47bb3c","083f9cda8d754c168beee10d2f8955a2","62e1a65582f446a78612eaa804e08a7d","487a177d020f4605834878b2fdc7afa3","7fd44cf9ca6e4726bfd7ac21846d6a14","366a343b62fa47d8985a3bd464d99f9e","a0a11e929edd4189b79723d618522c33","e87ea87fcff247b5bbcc331ba79a8dc2","5e18768f7ad6434ba8b8b8a2e853e204","bb33aec33a6447078c31bfd728942994"]}}" data-outputid="f0acdcec-4b41-4a3f-ffed-c2d2d929158e">
|
||
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> axolotl.cli.config <span class="im">import</span> load_cfg</span>
|
||
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> axolotl.utils.<span class="bu">dict</span> <span class="im">import</span> DictDefault</span>
|
||
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Axolotl provides full control and transparency over model and training configuration</span></span>
|
||
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>config <span class="op">=</span> DictDefault(</span>
|
||
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a> base_model<span class="op">=</span><span class="st">"Qwen/Qwen3-14B"</span>, <span class="co"># Use the instruct tuned model, but we're aligning it to be a pirate</span></span>
|
||
<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a> load_in_4bit<span class="op">=</span><span class="va">True</span>, <span class="co"># set to True for qLoRA</span></span>
|
||
<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a> adapter<span class="op">=</span><span class="st">"qlora"</span>,</span>
|
||
<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a> lora_r<span class="op">=</span><span class="dv">32</span>,</span>
|
||
<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a> lora_alpha<span class="op">=</span><span class="dv">64</span>,</span>
|
||
<span id="cb4-11"><a href="#cb4-11" aria-hidden="true" tabindex="-1"></a> lora_target_modules<span class="op">=</span>[</span>
|
||
<span id="cb4-12"><a href="#cb4-12" aria-hidden="true" tabindex="-1"></a> <span class="st">"q_proj"</span>,</span>
|
||
<span id="cb4-13"><a href="#cb4-13" aria-hidden="true" tabindex="-1"></a> <span class="st">"k_proj"</span>,</span>
|
||
<span id="cb4-14"><a href="#cb4-14" aria-hidden="true" tabindex="-1"></a> <span class="st">"v_proj"</span>,</span>
|
||
<span id="cb4-15"><a href="#cb4-15" aria-hidden="true" tabindex="-1"></a> <span class="st">"o_proj"</span>, <span class="co"># train self_attn linear modules</span></span>
|
||
<span id="cb4-16"><a href="#cb4-16" aria-hidden="true" tabindex="-1"></a> <span class="st">"gate_proj"</span>,</span>
|
||
<span id="cb4-17"><a href="#cb4-17" aria-hidden="true" tabindex="-1"></a> <span class="st">"down_proj"</span>,</span>
|
||
<span id="cb4-18"><a href="#cb4-18" aria-hidden="true" tabindex="-1"></a> <span class="st">"up_proj"</span>, <span class="co"># train MLP linear modules</span></span>
|
||
<span id="cb4-19"><a href="#cb4-19" aria-hidden="true" tabindex="-1"></a> ],</span>
|
||
<span id="cb4-20"><a href="#cb4-20" aria-hidden="true" tabindex="-1"></a> lora_qkv_kernel<span class="op">=</span><span class="va">True</span>, <span class="co"># optimized triton kernels for LoRA</span></span>
|
||
<span id="cb4-21"><a href="#cb4-21" aria-hidden="true" tabindex="-1"></a> lora_o_kernel<span class="op">=</span><span class="va">True</span>,</span>
|
||
<span id="cb4-22"><a href="#cb4-22" aria-hidden="true" tabindex="-1"></a> lora_mlp_kernel<span class="op">=</span><span class="va">True</span>,</span>
|
||
<span id="cb4-23"><a href="#cb4-23" aria-hidden="true" tabindex="-1"></a> embeddings_skip_upcast<span class="op">=</span><span class="va">True</span>, <span class="co"># keep embeddings in fp16 so the model fits in 15GB VRAM</span></span>
|
||
<span id="cb4-24"><a href="#cb4-24" aria-hidden="true" tabindex="-1"></a> xformers_attention<span class="op">=</span><span class="va">True</span>, <span class="co"># use xformers on Colab w/ T4 for memory efficient attention, flash_attention only on Ampere or above</span></span>
|
||
<span id="cb4-25"><a href="#cb4-25" aria-hidden="true" tabindex="-1"></a> plugins<span class="op">=</span>[</span>
|
||
<span id="cb4-26"><a href="#cb4-26" aria-hidden="true" tabindex="-1"></a> <span class="co"># more efficient training using Apple's Cut Cross Entropy; https://github.com/apple/ml-cross-entropy</span></span>
|
||
<span id="cb4-27"><a href="#cb4-27" aria-hidden="true" tabindex="-1"></a> <span class="st">"axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin"</span>,</span>
|
||
<span id="cb4-28"><a href="#cb4-28" aria-hidden="true" tabindex="-1"></a> ],</span>
|
||
<span id="cb4-29"><a href="#cb4-29" aria-hidden="true" tabindex="-1"></a> sample_packing<span class="op">=</span><span class="va">True</span>, <span class="co"># 2-6x increase in tokens per micro-batch</span></span>
|
||
<span id="cb4-30"><a href="#cb4-30" aria-hidden="true" tabindex="-1"></a> <span class="co"># when using packing, use a slightly higher learning rate to account for fewer steps</span></span>
|
||
<span id="cb4-31"><a href="#cb4-31" aria-hidden="true" tabindex="-1"></a> <span class="co"># alternatively, reduce the micro_batch_size + gradient_accumulation_steps to achieve closer to the same number of steps/epoch</span></span>
|
||
<span id="cb4-32"><a href="#cb4-32" aria-hidden="true" tabindex="-1"></a> learning_rate<span class="op">=</span><span class="fl">0.00019</span>,</span>
|
||
<span id="cb4-33"><a href="#cb4-33" aria-hidden="true" tabindex="-1"></a> sequence_len<span class="op">=</span><span class="dv">4096</span>, <span class="co"># larger sequence length improves packing efficiency for more tokens/sec</span></span>
|
||
<span id="cb4-34"><a href="#cb4-34" aria-hidden="true" tabindex="-1"></a> micro_batch_size<span class="op">=</span><span class="dv">1</span>,</span>
|
||
<span id="cb4-35"><a href="#cb4-35" aria-hidden="true" tabindex="-1"></a> gradient_accumulation_steps<span class="op">=</span><span class="dv">1</span>,</span>
|
||
<span id="cb4-36"><a href="#cb4-36" aria-hidden="true" tabindex="-1"></a> gradient_checkpointing<span class="op">=</span><span class="va">True</span>, <span class="co"># tradeoff reduced VRAM for increased time</span></span>
|
||
<span id="cb4-37"><a href="#cb4-37" aria-hidden="true" tabindex="-1"></a> gradient_checkpointing_kwargs<span class="op">=</span>{</span>
|
||
<span id="cb4-38"><a href="#cb4-38" aria-hidden="true" tabindex="-1"></a> <span class="st">"use_reentrant"</span>: <span class="va">False</span>,</span>
|
||
<span id="cb4-39"><a href="#cb4-39" aria-hidden="true" tabindex="-1"></a> },</span>
|
||
<span id="cb4-40"><a href="#cb4-40" aria-hidden="true" tabindex="-1"></a> optimizer<span class="op">=</span><span class="st">"paged_adamw_8bit"</span>,</span>
|
||
<span id="cb4-41"><a href="#cb4-41" aria-hidden="true" tabindex="-1"></a> lr_scheduler<span class="op">=</span><span class="st">"cosine"</span>,</span>
|
||
<span id="cb4-42"><a href="#cb4-42" aria-hidden="true" tabindex="-1"></a> warmup_steps<span class="op">=</span><span class="dv">5</span>,</span>
|
||
<span id="cb4-43"><a href="#cb4-43" aria-hidden="true" tabindex="-1"></a> fp16<span class="op">=</span><span class="va">True</span>, <span class="co"># use float16 + automatic mixed precision, bfloat16 not supported on Colab w/ T4</span></span>
|
||
<span id="cb4-44"><a href="#cb4-44" aria-hidden="true" tabindex="-1"></a> bf16<span class="op">=</span><span class="va">False</span>,</span>
|
||
<span id="cb4-45"><a href="#cb4-45" aria-hidden="true" tabindex="-1"></a> max_grad_norm<span class="op">=</span><span class="fl">0.1</span>, <span class="co"># gradient clipping</span></span>
|
||
<span id="cb4-46"><a href="#cb4-46" aria-hidden="true" tabindex="-1"></a> num_epochs<span class="op">=</span><span class="dv">1</span>,</span>
|
||
<span id="cb4-47"><a href="#cb4-47" aria-hidden="true" tabindex="-1"></a> saves_per_epoch<span class="op">=</span><span class="dv">2</span>, <span class="co"># how many checkpoints to save over one epoch</span></span>
|
||
<span id="cb4-48"><a href="#cb4-48" aria-hidden="true" tabindex="-1"></a> logging_steps<span class="op">=</span><span class="dv">1</span>,</span>
|
||
<span id="cb4-49"><a href="#cb4-49" aria-hidden="true" tabindex="-1"></a> output_dir<span class="op">=</span><span class="st">"./outputs/qwen-sft-pirate-rrr"</span>,</span>
|
||
<span id="cb4-50"><a href="#cb4-50" aria-hidden="true" tabindex="-1"></a> chat_template<span class="op">=</span><span class="st">"qwen3"</span>,</span>
|
||
<span id="cb4-51"><a href="#cb4-51" aria-hidden="true" tabindex="-1"></a> datasets<span class="op">=</span>[</span>
|
||
<span id="cb4-52"><a href="#cb4-52" aria-hidden="true" tabindex="-1"></a> {</span>
|
||
<span id="cb4-53"><a href="#cb4-53" aria-hidden="true" tabindex="-1"></a> <span class="st">"path"</span>: dataset_id, <span class="co"># Huggingface Dataset id or path to train.jsonl</span></span>
|
||
<span id="cb4-54"><a href="#cb4-54" aria-hidden="true" tabindex="-1"></a> <span class="st">"type"</span>: <span class="st">"chat_template"</span>,</span>
|
||
<span id="cb4-55"><a href="#cb4-55" aria-hidden="true" tabindex="-1"></a> <span class="st">"split"</span>: <span class="st">"train"</span>,</span>
|
||
<span id="cb4-56"><a href="#cb4-56" aria-hidden="true" tabindex="-1"></a> <span class="st">"eot_tokens"</span>: [<span class="st">"<|im_end|>"</span>],</span>
|
||
<span id="cb4-57"><a href="#cb4-57" aria-hidden="true" tabindex="-1"></a> }</span>
|
||
<span id="cb4-58"><a href="#cb4-58" aria-hidden="true" tabindex="-1"></a> ],</span>
|
||
<span id="cb4-59"><a href="#cb4-59" aria-hidden="true" tabindex="-1"></a> dataloader_prefetch_factor<span class="op">=</span><span class="dv">8</span>, <span class="co"># dataloader optimizations</span></span>
|
||
<span id="cb4-60"><a href="#cb4-60" aria-hidden="true" tabindex="-1"></a> dataloader_num_workers<span class="op">=</span><span class="dv">2</span>,</span>
|
||
<span id="cb4-61"><a href="#cb4-61" aria-hidden="true" tabindex="-1"></a> dataloader_pin_memory<span class="op">=</span><span class="va">True</span>,</span>
|
||
<span id="cb4-62"><a href="#cb4-62" aria-hidden="true" tabindex="-1"></a>)</span>
|
||
<span id="cb4-63"><a href="#cb4-63" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb4-64"><a href="#cb4-64" aria-hidden="true" tabindex="-1"></a><span class="co"># validates the configuration</span></span>
|
||
<span id="cb4-65"><a href="#cb4-65" aria-hidden="true" tabindex="-1"></a>cfg <span class="op">=</span> load_cfg(config)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
|
||
<div class="cell-output cell-output-stdout">
|
||
<div class="ansi-escaped-output">
|
||
<pre>[2025-05-08 13:40:27,488] [INFO] [root.register:348] [PID:174] Attempting to load plugin: axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
|
||
|
||
[2025-05-08 13:40:27,493] [INFO] [root.register:351] [PID:174] Plugin loaded successfully: axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
|
||
|
||
[2025-05-08 13:40:27,959] [INFO] [axolotl.utils.schemas.config.check_eval_packing:721] [PID:174] [RANK:0] explicitly setting `eval_sample_packing` to match `sample_packing`
|
||
|
||
[2025-05-08 13:40:27,960] [INFO] [axolotl.utils.schemas.config.hint_sample_packing_padding:514] [PID:174] [RANK:0] Setting `pad_to_sequence_len: true` to prevent memory leaks when sample_packing
|
||
|
||
[2025-05-08 13:40:27,961] [INFO] [axolotl.utils.schemas.config.check_bf16:1251] [PID:174] [RANK:0] bf16 support detected, but not enabled for this configuration.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
<div class="cell-output cell-output-display">
|
||
<script type="application/vnd.jupyter.widget-view+json">
|
||
{"model_id":"388f618924274d21a066f098f4f1e744","version_major":2,"version_minor":0,"quarto_mimetype":"application/vnd.jupyter.widget-view+json"}
|
||
</script>
|
||
</div>
|
||
<div class="cell-output cell-output-stdout">
|
||
<div class="ansi-escaped-output">
|
||
<pre>[2025-05-08 13:40:28,590] [INFO] [axolotl.normalize_config:237] [PID:174] [RANK:0] cuda memory usage baseline: 0.000GB (+0.002GB cache, +0.359GB misc)
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div id="cell-10" class="cell">
|
||
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> axolotl.utils <span class="im">import</span> set_pytorch_cuda_alloc_conf</span>
|
||
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>set_pytorch_cuda_alloc_conf()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
|
||
</div>
|
||
</section>
|
||
<section id="datasets" class="level1">
|
||
<h1>Datasets</h1>
|
||
<p>Axolotl has a robust suite of loaders and transforms to parse most open datasets of any format into the appropriate chat template for your model. Axolotl will mask input tokens from the user’s prompt so that the train loss is only calculated against the model’s response. For more information, <a href="http://docs.axolotl.ai/docs/dataset-formats/conversation.html">see our documentation</a> on dataset preparation.</p>
|
||
<div id="cell-12" class="cell" data-quarto-private-1="{"key":"colab","value":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["b82aa8c57f7c422a9a9c90f333ed2a99","c0991cf63ee6458b96e9a75e7a88b61a","71c8af139cd248b1b51101fd46a93f35","1d5117195d4b49eb8f1a73b18419f7ce","3c21e4a511b4441192c03b7f1d0976e9","ed28e2e0410d4e0b855467e798e53d66","d93f134f802b4b69b575bdaf07dbd27c","d0e9dce55cec4c1ca619a0ccf209d924","4c727d40ef0443449afc31724ee79f0c","0dea5caa27384f5689e3cab51f558727","a6f48410b9964fefba0c3009a77dc838","95caff42f08a4c2aa14c867b8f37f231","de7c37ee83e24f0c889e84d07279c2ec","9d4897eefb5f48259ffb2d23e332f752","253017b0d0534e54ab44e181f6d7c82d","27beaf06e41b472abdb544a43c720c5a","34cf3df51fbc41cabfdbba153c007f0e","ac764024cf1c4e08ba7749afd2cd20ac","30a81da86f8043eca301e86a8651201a","e8b7a81040904c1e89e58978223b1737","1c6f1f10667545aaab958016ba7e2c94","e6e969610738449887259063967f82b0","a138859f19b74fc0928dc236ab5359db","9b42e08b3c9548818488268768a118b1","12b56912736849fea2ad8124456fdc5c","879c8ab5873847a8833bd74123be90a4","20352e5f58d24bb8b1f3940efd14fe4a","d955dcaa0e944e719f3a06139dd54a03","d3de2662c7964f1ba96e58da382af720","97e36007e1304e1583fd81bfb13f0edd","c65dc74c7d6f4bab8f7dd28455161dd8","ef223e8504b64e3592589880326aaf41","598da69727bd4fb8b1caf465ac736d7a","5f86cd894de94c3280fadc1e2fd0ee13","a20927bf5f2c41f58c1e31ac858ab36c","0a46ad75c198463d843fb35e813642cb","09007681cf8d42aeb8c1d2f6a74e470a","ebc80d1a55fa47f4a5ea2756588569ec","1811cda0644e4190a9469d1774435d82","35c811d2ae8e43f3b5cecbdd3cfa857f","b8e39e4dddc3497fbc29ae45c66da759","63b4e563e85c4f03b1b72beda9577bcc","b195f160ca20442fadd8b5aed0ee41af","ca65e32eb52f48c09a84b33cb18f22cd","7cd0b85ebd204b7aba908417811ce4e0","7baeab52d6694c32b1efd1ea1a0a7782","519a7b154022443db6703f04a9142bae","d4183e9715f34d249942b8271cca3bdf","da2347ac94764a3fa2743343cf0d3cd2","93a44a11aa4846fa8efc6c1413ef1627","a55060adc3564407ac81ad7297d34aaa","d02274afd47b462291c745f261209d42","0f417447a7bd4a33acca96fa37aec877","63580b6fb30642479fe3000915bf551a","8f726dbfb45d4528afa33e36a6313267","03b093d592ba4386aa61f7b8483da660","b8766a88716948cf968f4563531a76d9","6f3a28b912714c6e931003549664bfa3","16d1283741404b7bb319094c992fce01","2a5bb0e818ab47be8cf6465988328503","2b3a2659b12244bd8548320320016dbf","0cd7efffbb3c4c4b972e63749f61ab97","5ca240f31e6b44e3882c5eb37cd5a309","5eb06edeb58e4930b1affef2a59eae81","a4e5789584564049b83df7c6c54a3e08","ff3a94b146a948b6907f5d80c7157f99","258b7c635c1045329d4669e48c46ccd5","6f68ed9889f54ad2ae8a3b95ac263a83","80366349d81e4dcc892db6cd56e384f3","c73055099c084dca996159e23e162d0b","977f799afaac4a55b2dc1cffa7d5b63b","41f3b32c2f6b4034ae7a3b9124e28bc7","a10d0a76010f4e508c65a9b69ebc5156","f8ef805b776145c3bfa9ba8d90972058","cc587493c33c4f118d1b1170f85be24c","e40d1c1ac9494b3bade9858324e7ffdf","d65b6b060d9845779299491ac5599c31","0f6907ebbc6242c8bde059cef1e1bd29","5bdfd87fc6cd4f9dabef7cfee29c8060","64f54d4a744a4627a07c3c0120276f3b","65b75b9b8bc143cf997796af68ff6668","d6fe74e4255444368f8f90a62157d869","4d468f96ec924681ad65eb671674b93e","ad7599de524549c48bf2d3124ad4b299","0546d04aae644dde846c58a4afb598a6","897b77a56c09479bb11d7f2a30997e55","81c3db71ac704280ad030072655f1537","042e091f75694c47aee761e760e76773","ef0a3c7a6f14460fb4da096928ae249e","07fb3a2c8315494e97b447e672dfae06","ec030fc3c346426f9abc3a89892258d3","e3fb3fc6afe04b3c9b7ac61809ce78fa","c3be9109d63c485d9c0ef4f9bc0f9218","12815f401eba44658caa7b2e490137a8","30e02aa2d0d241979369e598287f2639","dfd2a2649b8341ef913207526708aff1","4f1977d7e4824ef1a14b65f0f42bba10","c6164e05a1914ae48083db9ad7f4ef7c","813621384dc748b0ad06775e22761c0b","dc892a596f6942d7973c616c38f0eebb","c84cc07789be48aebb322c23d355289e","bed8726b8069434687c75452e21f19e5","16a188a0b06d45f980dcf3933509fe0a","60c1a0d765c14a1d888317e6a507e4ea","0077aedc3d174560bce924ee89e9c006","00321cce58884f6f9b3855a21fcd9187","fa864b41586f4a7aa56aeafd1d84eb75","3225603166b54e7aab766b9964a2f660","349eee9f56d64f0cba6fc24ff2c50c9b","7e5d3774060e4589aa65982da5ea4ef4","7c2485c6cdfe463da6fdb35982a1070d","ad1236893754446881e153adc9d5c962","daee63fd167e4441a32324b51b00ad2b","fe41858c6bd04c58840112b67c19a336","d262c82138024169b9f3aa034ca756fa","62e302ebdad64aada0ffe64ae1c873f3","bd1b0dfed6d34d16af33a4a58330f5ec","d07c8b97d3314f1c852e44bdd40f61ed","ebb69a2c3d0a4299a484698287b3087c","e5a82df528bb4e408797a3b6c2758f4a","f113ebd8c1c34806bea4dd7ed3035173"]}}" data-outputid="f69441d8-95f9-4885-c306-6c8709090ff6">
|
||
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> axolotl.common.datasets <span class="im">import</span> load_datasets</span>
|
||
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Load, parse and tokenize the datasets to be formatted with qwen3 chat template</span></span>
|
||
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Drop long samples from the dataset that overflow the max sequence length</span></span>
|
||
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>dataset_meta <span class="op">=</span> load_datasets(cfg<span class="op">=</span>cfg)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
|
||
<div class="cell-output cell-output-display">
|
||
<script type="application/vnd.jupyter.widget-view+json">
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|
||
</script>
|
||
</div>
|
||
<div class="cell-output cell-output-display">
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<script type="application/vnd.jupyter.widget-view+json">
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|
||
</script>
|
||
</div>
|
||
<div class="cell-output cell-output-display">
|
||
<script type="application/vnd.jupyter.widget-view+json">
|
||
{"model_id":"a138859f19b74fc0928dc236ab5359db","version_major":2,"version_minor":0,"quarto_mimetype":"application/vnd.jupyter.widget-view+json"}
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||
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|
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|
||
<div class="cell-output cell-output-display">
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<script type="application/vnd.jupyter.widget-view+json">
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|
||
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|
||
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|
||
<div class="cell-output cell-output-stdout">
|
||
<div class="ansi-escaped-output">
|
||
<pre>[2025-05-08 13:41:00,844] [DEBUG] [axolotl.utils.models.load_tokenizer:441] [PID:174] [RANK:0] EOS: 151645 / <|im_end|>
|
||
|
||
[2025-05-08 13:41:00,845] [DEBUG] [axolotl.utils.models.load_tokenizer:442] [PID:174] [RANK:0] BOS: None / None
|
||
|
||
[2025-05-08 13:41:00,846] [DEBUG] [axolotl.utils.models.load_tokenizer:443] [PID:174] [RANK:0] PAD: 151643 / <|endoftext|>
|
||
|
||
[2025-05-08 13:41:00,847] [DEBUG] [axolotl.utils.models.load_tokenizer:444] [PID:174] [RANK:0] UNK: None / None
|
||
|
||
[2025-05-08 13:41:00,869] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:271] [PID:174] [RANK:0] Unable to find prepared dataset in last_run_prepared/97037817611d38b3a9c681753c3c4c95
|
||
|
||
[2025-05-08 13:41:00,870] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:272] [PID:174] [RANK:0] Loading raw datasets...
|
||
|
||
<span class="ansi-yellow-fg">[2025-05-08 13:41:00,870] [WARNING] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:274] [PID:174] [RANK:0] Processing datasets during training can lead to VRAM instability. Please pre-process your dataset.</span>
|
||
|
||
[2025-05-08 13:41:00,871] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:281] [PID:174] [RANK:0] No seed provided, using default seed of 42
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
<div class="cell-output cell-output-display">
|
||
<script type="application/vnd.jupyter.widget-view+json">
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|
||
</script>
|
||
</div>
|
||
<div class="cell-output cell-output-display">
|
||
<script type="application/vnd.jupyter.widget-view+json">
|
||
{"model_id":"03b093d592ba4386aa61f7b8483da660","version_major":2,"version_minor":0,"quarto_mimetype":"application/vnd.jupyter.widget-view+json"}
|
||
</script>
|
||
</div>
|
||
<div class="cell-output cell-output-stdout">
|
||
<div class="ansi-escaped-output">
|
||
<pre>[2025-05-08 13:41:04,196] [INFO] [axolotl.utils.data.sft.get_dataset_wrapper:484] [PID:174] [RANK:0] Loading dataset with base_type: chat_template and prompt_style: None
|
||
|
||
[2025-05-08 13:41:04,233] [INFO] [axolotl.__call__:761] [PID:174] [RANK:0] Using chat template:
|
||
|
||
---
|
||
|
||
{%- if tools %}
|
||
|
||
{{- '<|im_start|>system\n' }}
|
||
|
||
{%- if messages[0].role == 'system' %}
|
||
|
||
{{- messages[0].content + '\n\n' }}
|
||
|
||
{%- endif %}
|
||
|
||
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||
|
||
{%- for tool in tools %}
|
||
|
||
{{- "\n" }}
|
||
|
||
{{- tool | tojson }}
|
||
|
||
{%- endfor %}
|
||
|
||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||
|
||
{%- else %}
|
||
|
||
{%- if messages[0].role == 'system' %}
|
||
|
||
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
||
|
||
{%- endif %}
|
||
|
||
{%- endif %}
|
||
|
||
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
||
|
||
{%- for message in messages[::-1] %}
|
||
|
||
{%- set index = (messages|length - 1) - loop.index0 %}
|
||
|
||
{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
|
||
|
||
{%- set ns.multi_step_tool = false %}
|
||
|
||
{%- set ns.last_query_index = index %}
|
||
|
||
{%- endif %}
|
||
|
||
{%- endfor %}
|
||
|
||
{%- for message in messages %}
|
||
|
||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||
|
||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||
|
||
{%- elif message.role == "assistant" %}
|
||
|
||
{%- set content = message.content %}
|
||
|
||
{%- set reasoning_content = '' %}
|
||
|
||
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
|
||
|
||
{%- set reasoning_content = message.reasoning_content %}
|
||
|
||
{%- else %}
|
||
|
||
{%- if '</think>' in message.content %}
|
||
|
||
{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
|
||
|
||
{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
||
|
||
{%- endif %}
|
||
|
||
{%- endif %}
|
||
|
||
{%- if loop.index0 > ns.last_query_index %}
|
||
|
||
{%- if loop.last or (not loop.last and reasoning_content) %}
|
||
|
||
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||
|
||
{%- else %}
|
||
|
||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||
|
||
{%- endif %}
|
||
|
||
{%- else %}
|
||
|
||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||
|
||
{%- endif %}
|
||
|
||
{%- if message.tool_calls %}
|
||
|
||
{%- for tool_call in message.tool_calls %}
|
||
|
||
{%- if (loop.first and content) or (not loop.first) %}
|
||
|
||
{{- '\n' }}
|
||
|
||
{%- endif %}
|
||
|
||
{%- if tool_call.function %}
|
||
|
||
{%- set tool_call = tool_call.function %}
|
||
|
||
{%- endif %}
|
||
|
||
{{- '<tool_call>\n{"name": "' }}
|
||
|
||
{{- tool_call.name }}
|
||
|
||
{{- '", "arguments": ' }}
|
||
|
||
{%- if tool_call.arguments is string %}
|
||
|
||
{{- tool_call.arguments }}
|
||
|
||
{%- else %}
|
||
|
||
{{- tool_call.arguments | tojson }}
|
||
|
||
{%- endif %}
|
||
|
||
{{- '}\n</tool_call>' }}
|
||
|
||
{%- endfor %}
|
||
|
||
{%- endif %}
|
||
|
||
{{- '<|im_end|>\n' }}
|
||
|
||
{%- elif message.role == "tool" %}
|
||
|
||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||
|
||
{{- '<|im_start|>user' }}
|
||
|
||
{%- endif %}
|
||
|
||
{{- '\n<tool_response>\n' }}
|
||
|
||
{{- message.content }}
|
||
|
||
{{- '\n</tool_response>' }}
|
||
|
||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||
|
||
{{- '<|im_end|>\n' }}
|
||
|
||
{%- endif %}
|
||
|
||
{%- endif %}
|
||
|
||
{%- endfor %}
|
||
|
||
{%- if add_generation_prompt %}
|
||
|
||
{{- '<|im_start|>assistant\n' }}
|
||
|
||
{%- if enable_thinking is defined and enable_thinking is false %}
|
||
|
||
{{- '<think>\n\n</think>\n\n' }}
|
||
|
||
{%- endif %}
|
||
|
||
{%- endif %}
|
||
|
||
---
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
<div class="cell-output cell-output-display">
|
||
<script type="application/vnd.jupyter.widget-view+json">
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|
||
</script>
|
||
</div>
|
||
<div class="cell-output cell-output-stdout">
|
||
<div class="ansi-escaped-output">
|
||
<pre>[2025-05-08 13:42:09,195] [INFO] [axolotl.utils.data.utils.drop_long_seq_in_dataset:177] [PID:174] [RANK:0] min_input_len: 23
|
||
|
||
[2025-05-08 13:42:09,196] [INFO] [axolotl.utils.data.utils.drop_long_seq_in_dataset:179] [PID:174] [RANK:0] max_input_len: 3380
|
||
</pre>
|
||
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||
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<pre>[2025-05-08 13:42:21,651] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:351] [PID:174] [RANK:0] Saving merged prepared dataset to disk... last_run_prepared/97037817611d38b3a9c681753c3c4c95
|
||
</pre>
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||
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||
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<pre>[2025-05-08 13:42:25,711] [INFO] [axolotl.utils.samplers.multipack.calc_min_len:411] [PID:174] [RANK:0] gather_len_batches: [1540]
|
||
|
||
[2025-05-08 13:42:25,714] [INFO] [axolotl.calc_sample_packing_eff_est:491] [PID:174] [RANK:0] sample_packing_eff_est across ranks: [0.9987832601968344]
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
<section id="training" class="level1">
|
||
<h1>Training</h1>
|
||
<div id="cell-14" class="cell" data-quarto-private-1="{"key":"colab","value":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["004d9177a6a14118a5930dc3cc13147b","a80410b919e442c49aea15acc1ce1a72","c6e00f5224364822bc4239b176686919","ec11d1e5ae7b42c883d9b1f38a65356e","734185351eb543fa9a00a881dcbb9fe7","fa1282ccc7544e4f818e2f03ccffe4a5","bbbf575d2a4b4c6ea8389be79b2a6039","2a51b36be41745468e4c2d7a21b1c0d2","4fd114abe9f5494ab59858949f5055f1","936d04b5fe1b4c63bf0b080e423d051b","f1cef8e8dc2646fb9fd09f3b09081074","cdebbc55a1164c018546c2ac6f8c620c","a44f630e099e43899f20a77084ae60cd","c3725c7f79fe415fbd1ea336f0cc9cf1","0e50870ed0c643e0b6c18cc5d7ddae7f","c33ced495f70464aa4a3a91922090853","ed5ca967ad5342929e578ac6aa4dc4c0","af401d117d5047629d3a6e2361757b62","b191ac001a2e4962bc9a245fcdf26e6b","054c8dffadba48c6b895a6cc62448ecc","bfcdbba993b74972a9e3e575f86908ff","6ebb2ec171414e47a14765505f64bb3c","500e272208a246089613bf788a165271","200df5e79b9244849e589ecb0250a520","cc94432d08464affa3e58b560bdad194","3036608c71904ce9ae4bb2a9fa8802d9","adacfdcc1b0140efac56918e9ccf064e","f4a1795dc7514a718f478245f521f0ba","5e746eb25bbe416fb585fa24e79f5177","b5b65414154544aa8a71b1a39164aad7","f0a58fbd0fca4340890041f99fa2f8c8","5ca6be24acb548cea130bd58e9954c7c","5cfb02ee044b4011a378efa8b54a370f","4d05314858354e729d76094b3b0ce761","c42acf646f344a88b8c11f81e67f7206","7be6f04c284e4326bb4ff3d301e7b3c6","ffdbb12a2f2c4d14911685e7683e0ef0","bee3501b2a17427784a717e50a85e7fa","8bc9d8ba866c442b9118d9630009939c","9f56a2d9979c4bd8928c644c22c3ecdf","9503a45960984adc97b58e16c50662e0","da6e93f3e4984780b930fe7a706983ea","ab93eabd7cea4b94b4b7a387f101e8a1","704f2f5a9b1c49d5a75a0025a5dda11b","dd0e646fad3f4a89ba23b39d162bd8d9","d43c6df07ddb466587807d6dbe1ff614","e0e8b840b8ea4d0d9db09afe99fa287d","9327977822be4b1294f80e876552e305","77304d1a46b3468a98483e02ec0ac4a4","8c4d4fc5a30f4e7cb3be53fe2adda33d","e90658f4bcb642baa78426012f863152","f7434f3e03124a1c938a39af79d7fa59","c1314f241a434c41b45d84dc4d3b30f8","37de928300e34184881039378bd75e7f","0e936d9dbf9c4fdd86bbfe9730dedc47","e21e180307e5485cbbe908672fd6639a","2e2b0c1599c341a198f632f46a40c90e","bff139df987d4a62abec6456cb27f3d4","ebe1cc366d324ad59b264c8b3c431441","114dece49dba437c8572ef94b23c3b1e","be724f04b03942b2a033a7e8898bb4fd","fcbab4d8dced41a18dfccce81e3a45a0","c1f9c267ba3f40039cdb5eb3267e8043","33b3b1d0295646edaac7b4822761aeb0","fba7aa824b38467ab3061b226114cdec","f3075dccbd2747b4a7913b66f44f2596","fe18bba7f3fb4c31bf840541f36b3425","fd4f333f7ece4450b04e1a9af1f9d2f6","f60a2bdb6b6b4e0e8c3508580e247132","c0892a1881de4eb4bfabc6a68f87ae99","1bec6297c90242a88672d195bc09d429","d1f9b10c130542f094c8fd3d1e23b5e9","e575d87a7efe4ec7b1efde489839d4a6","edc99591b9c747b689b94d0052fec14c","35cc989ca3374e7dba0cb166febc4bde","158c8b85dbf34de6a94b4e35e2fc7d5a","0b4c9753a7cb4354b8e5f187e6e1ad7c","4471ff62258549fba9514bb67050f965","9cd5211b5d8b457aa0002f1d17b80028","19127c7bb1554ccbac877059f9a82db0","f4667818b9d34a09891cd727a429a610","9ed02dc43412471a9ab47f3620ccf3a5","6932489232ec4ab18a160b1e7fbcdfe1","4540927d98f54466b434ba4c0edf045d","e400cbf14bcc446a9d33b210cd93550b","71002199df6b40c9a1ac40df5fb27a1b","4b27c267393640f28f6eae0875bd2ed9","9858cb74a09748a39e8149baac96702c","eb1c9535e6a546098b760528b2ea387c","18357b321ce44d7b8bd9d1c886f69275","279937fe03bc4e4eb25b472d7e9df163","bca2c7185b6749fd899c06a2ba4c5e46","1f7d30f71bbd4547a9150d21da071055","e366ae3fceec4566b9ed303d6c5f90af","5dd7d150dbe04f08b165ce7f2c27cd11","b634bb73cfa743d09a5999101b840976","742b1030acfd414bbd9d5327b7e3826d","0f480e3a0b0a45d2a2d2dec3cad923f3","fcb30372e7404c5d8a1ad4df91e6c7b2","2860e3bb3baf4f7da058465850e800c5","3efd18ea8eaa41918894883da9541bfa","e09f1bcbb9d94c09be53e5e1303642c2","82177df57a494de8900c14c2f5185175","ccfcdc95baf646f8aeb3d516742383f2","8f5bd719974e41c3a8dd9a5b0d3d71e6","b87c84de30e84b3abf4871461fb9cbd3","e7d8e4fe58384e93a106de546068c65e","0aa8ab56b85f4171a79c3bc210594025","67da6c4260574869aa24c3cbc1bc1654","94b9088614464f60a203de39dbcae853","fea1b70fb46745feb5111b3929175b5d","f365820a3d3c42b2948abfe32065de14","823f1c78f15043e38bbd4dca3932a86a","a1959759c5424da9961fb2a308d4dee4","34c9c0137b504cd799c6bd6de69507c2","735d4f225b24414294fc1b213c61223c","5e5e15b0569b474c9620083b3ec6af55","03a3c744d716431488163b4358b80f92","a5434ee714f9498d83870544b67c0cb7","3aaecbf540f54a2db9ab0931e3b1fe57","9e333ed3b5014069ac1dd969255dd591"]}}" data-outputid="84fa167f-ba27-4255-d508-dc9df56ad39b">
|
||
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> axolotl.train <span class="im">import</span> train</span>
|
||
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="co"># just train the first 25 steps for demo.</span></span>
|
||
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a><span class="co"># This is sufficient to align the model as we've used packing to maximize the trainable samples per step.</span></span>
|
||
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>cfg.max_steps <span class="op">=</span> <span class="dv">25</span></span>
|
||
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a>model, tokenizer, trainer <span class="op">=</span> train(cfg<span class="op">=</span>cfg, dataset_meta<span class="op">=</span>dataset_meta)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
|
||
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|
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@@@@ @@@@@@@@@@@@@@@@
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||
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||
|
||
[2025-05-07 22:08:14,344] [INFO] [axolotl.monkeypatch.peft.utils.patch_peft_prep_code:76] [PID:1336] [RANK:0] patching prepare_model_for_kbit_training to allow for overrides
|
||
|
||
[2025-05-07 22:08:14,549] [INFO] [axolotl.integrations.cut_cross_entropy.pre_model_load:80] [PID:1336] [RANK:0] Applying Cut Cross Entropy to model type: qwen3
|
||
</pre>
|
||
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|
||
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|
||
<div class="cell-output cell-output-display">
|
||
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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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|
||
<div class="cell-output cell-output-display">
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||
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|
||
{"model_id":"e21e180307e5485cbbe908672fd6639a","version_major":2,"version_minor":0,"quarto_mimetype":"application/vnd.jupyter.widget-view+json"}
|
||
</script>
|
||
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|
||
<div class="cell-output cell-output-display">
|
||
<script type="application/vnd.jupyter.widget-view+json">
|
||
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|
||
</script>
|
||
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|
||
<div class="cell-output cell-output-display">
|
||
<script type="application/vnd.jupyter.widget-view+json">
|
||
{"model_id":"4471ff62258549fba9514bb67050f965","version_major":2,"version_minor":0,"quarto_mimetype":"application/vnd.jupyter.widget-view+json"}
|
||
</script>
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||
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|
||
<div class="cell-output cell-output-display">
|
||
<script type="application/vnd.jupyter.widget-view+json">
|
||
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|
||
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|
||
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|
||
<div class="cell-output cell-output-stdout">
|
||
<pre><code>[2025-05-07 22:09:49,798] [INFO] [accelerate.utils.modeling.get_balanced_memory:990] [PID:1336] We will use 90% of the memory on device 0 for storing the model, and 10% for the buffer to avoid OOM. You can set `max_memory` in to a higher value to use more memory (at your own risk).</code></pre>
|
||
</div>
|
||
<div class="cell-output cell-output-display">
|
||
<script type="application/vnd.jupyter.widget-view+json">
|
||
{"model_id":"2860e3bb3baf4f7da058465850e800c5","version_major":2,"version_minor":0,"quarto_mimetype":"application/vnd.jupyter.widget-view+json"}
|
||
</script>
|
||
</div>
|
||
<div class="cell-output cell-output-display">
|
||
<script type="application/vnd.jupyter.widget-view+json">
|
||
{"model_id":"fea1b70fb46745feb5111b3929175b5d","version_major":2,"version_minor":0,"quarto_mimetype":"application/vnd.jupyter.widget-view+json"}
|
||
</script>
|
||
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|
||
<div class="cell-output cell-output-stdout">
|
||
<div class="ansi-escaped-output">
|
||
<pre>[2025-05-07 22:11:37,521] [INFO] [axolotl.utils.models.load_model:1302] [PID:1336] [RANK:0] cuda memory usage after model load: 9.264GB (+1.721GB cache, +0.375GB misc)
|
||
|
||
[2025-05-07 22:11:37,532] [INFO] [axolotl.utils.models.prepare_model:1205] [PID:1336] [RANK:0] converting PEFT model w/ prepare_model_for_kbit_training
|
||
|
||
[2025-05-07 22:11:37,537] [INFO] [axolotl.utils.models.load_model:1341] [PID:1336] [RANK:0] Converting modules to torch.float16
|
||
|
||
trainable params: 128,450,560 || all params: 14,896,757,760 || trainable%: 0.8623
|
||
|
||
[2025-05-07 22:11:40,170] [INFO] [axolotl.utils.models.load_model:1402] [PID:1336] [RANK:0] cuda memory usage after adapters: 9.743GB (+1.476GB cache, +0.375GB misc)
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
<div class="cell-output cell-output-stderr">
|
||
<pre><code>/usr/local/lib/python3.11/dist-packages/axolotl/core/trainers/base.py:64: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `AxolotlTrainer.__init__`. Use `processing_class` instead.
|
||
super().__init__(*_args, **kwargs)
|
||
No label_names provided for model class `PeftModelForCausalLM`. Since `PeftModel` hides base models input arguments, if label_names is not given, label_names can't be set automatically within `Trainer`. Note that empty label_names list will be used instead.</code></pre>
|
||
</div>
|
||
<div class="cell-output cell-output-stdout">
|
||
<div class="ansi-escaped-output">
|
||
<pre>[2025-05-07 22:11:41,755] [INFO] [axolotl.train.save_initial_configs:359] [PID:1336] [RANK:0] Pre-saving adapter config to ./outputs/qwen-sft-pirate-rrr...
|
||
|
||
[2025-05-07 22:11:41,756] [INFO] [axolotl.train.save_initial_configs:363] [PID:1336] [RANK:0] Pre-saving tokenizer to ./outputs/qwen-sft-pirate-rrr...
|
||
|
||
[2025-05-07 22:11:41,974] [INFO] [axolotl.train.save_initial_configs:366] [PID:1336] [RANK:0] Pre-saving model config to ./outputs/qwen-sft-pirate-rrr...
|
||
|
||
[2025-05-07 22:11:41,982] [INFO] [axolotl.train.execute_training:211] [PID:1336] [RANK:0] Starting trainer...
|
||
|
||
[2025-05-07 22:11:45,047] [INFO] [axolotl.utils.samplers.multipack.calc_min_len:411] [PID:1336] [RANK:0] gather_len_batches: [1540]
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
<div class="cell-output cell-output-stderr">
|
||
<pre><code>You're using a Qwen2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
|
||
You're using a Qwen2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.</code></pre>
|
||
</div>
|
||
<div class="cell-output cell-output-display">
|
||
<div>
|
||
|
||
<progress value="25" max="25" style="width:300px; height:20px; vertical-align: middle;"></progress>
|
||
[25/25 09:25, Epoch 0/1]
|
||
</div>
|
||
|
||
<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
|
||
<thead>
|
||
<tr class="header">
|
||
<th data-quarto-table-cell-role="th">Step</th>
|
||
<th data-quarto-table-cell-role="th">Training Loss</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr class="odd">
|
||
<td>1</td>
|
||
<td>1.092300</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>2</td>
|
||
<td>1.554200</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>3</td>
|
||
<td>1.041400</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>4</td>
|
||
<td>1.733800</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>5</td>
|
||
<td>1.430000</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>6</td>
|
||
<td>1.258500</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>7</td>
|
||
<td>1.343600</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>8</td>
|
||
<td>1.101700</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>9</td>
|
||
<td>1.086500</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>10</td>
|
||
<td>0.813200</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>11</td>
|
||
<td>0.689600</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>12</td>
|
||
<td>0.826700</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>13</td>
|
||
<td>1.541800</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>14</td>
|
||
<td>0.948000</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>15</td>
|
||
<td>1.357000</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>16</td>
|
||
<td>1.085800</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>17</td>
|
||
<td>1.516800</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>18</td>
|
||
<td>1.146800</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>19</td>
|
||
<td>0.834800</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>20</td>
|
||
<td>0.968000</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>21</td>
|
||
<td>1.388800</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>22</td>
|
||
<td>1.511500</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>23</td>
|
||
<td>1.338500</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>24</td>
|
||
<td>1.206600</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>25</td>
|
||
<td>1.504600</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>
|
||
</p></div>
|
||
<div class="cell-output cell-output-stdout">
|
||
<div class="ansi-escaped-output">
|
||
<pre>[2025-05-07 22:12:42,746] [INFO] [axolotl.callbacks.on_step_end:128] [PID:1336] [RANK:0] cuda memory usage while training: 9.768GB (+3.287GB cache, +0.646GB misc)
|
||
|
||
[2025-05-07 22:21:46,859] [INFO] [axolotl.train.save_trained_model:231] [PID:1336] [RANK:0] Training completed! Saving pre-trained model to ./outputs/qwen-sft-pirate-rrr.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
<section id="inferencing-the-trained-model" class="level1">
|
||
<h1>Inferencing the trained model</h1>
|
||
<div id="cell-16" class="cell" data-quarto-private-1="{"key":"colab","value":{"base_uri":"https://localhost:8080/"}}" data-outputid="e5050605-f6c9-421c-98f9-bde56a281eae">
|
||
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" 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> transformers <span class="im">import</span> TextStreamer</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>messages <span class="op">=</span> [</span>
|
||
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a> {</span>
|
||
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a> <span class="st">"role"</span>: <span class="st">"user"</span>,</span>
|
||
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a> <span class="st">"content"</span>: <span class="st">"Explain the Pythagorean theorem to me."</span>,</span>
|
||
<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a> },</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></span>
|
||
<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a>prompt <span class="op">=</span> tokenizer.apply_chat_template(</span>
|
||
<span id="cb11-11"><a href="#cb11-11" aria-hidden="true" tabindex="-1"></a> messages,</span>
|
||
<span id="cb11-12"><a href="#cb11-12" aria-hidden="true" tabindex="-1"></a> add_generation_prompt<span class="op">=</span><span class="va">True</span>,</span>
|
||
<span id="cb11-13"><a href="#cb11-13" aria-hidden="true" tabindex="-1"></a> tokenize<span class="op">=</span><span class="va">False</span>,</span>
|
||
<span id="cb11-14"><a href="#cb11-14" aria-hidden="true" tabindex="-1"></a> enable_thinking<span class="op">=</span><span class="va">False</span>,</span>
|
||
<span id="cb11-15"><a href="#cb11-15" aria-hidden="true" tabindex="-1"></a>)</span>
|
||
<span id="cb11-16"><a href="#cb11-16" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb11-17"><a href="#cb11-17" aria-hidden="true" tabindex="-1"></a>outputs <span class="op">=</span> model.generate(</span>
|
||
<span id="cb11-18"><a href="#cb11-18" aria-hidden="true" tabindex="-1"></a> <span class="op">**</span>tokenizer(prompt, return_tensors<span class="op">=</span><span class="st">"pt"</span>).to(<span class="st">"cuda"</span>),</span>
|
||
<span id="cb11-19"><a href="#cb11-19" aria-hidden="true" tabindex="-1"></a> max_new_tokens<span class="op">=</span><span class="dv">192</span>,</span>
|
||
<span id="cb11-20"><a href="#cb11-20" aria-hidden="true" tabindex="-1"></a> temperature<span class="op">=</span><span class="fl">1.0</span>,</span>
|
||
<span id="cb11-21"><a href="#cb11-21" aria-hidden="true" tabindex="-1"></a> top_p<span class="op">=</span><span class="fl">0.8</span>,</span>
|
||
<span id="cb11-22"><a href="#cb11-22" aria-hidden="true" tabindex="-1"></a> top_k<span class="op">=</span><span class="dv">32</span>,</span>
|
||
<span id="cb11-23"><a href="#cb11-23" aria-hidden="true" tabindex="-1"></a> streamer<span class="op">=</span>TextStreamer(tokenizer, skip_prompt<span class="op">=</span><span class="va">True</span>),</span>
|
||
<span id="cb11-24"><a href="#cb11-24" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
|
||
<div class="cell-output cell-output-stdout">
|
||
<pre><code>Ahoy there, matey! Shiver me timbers, ye be lookin' for the Pythagorean theorem, eh? Well, hold yer horses and listen up, for I'll be tellin' ye all about it in me own special way.
|
||
|
||
The Pythagorean theorem be a real gem of a mathematical trick that helps ye find the length of a side of a right triangle. Now, a right triangle be a triangle with a right angle, which be that little corner that looks like a square.
|
||
|
||
The theorem be named after a clever fellow named Pythagoras, who be a mathematician from ancient Greece. He discovered that if ye have a right triangle, the square of the length of the hypotenuse (that be the side opposite the right angle) be equal to the sum of the squares of the other two sides.
|
||
|
||
In other words, if ye have a triangle with sides of length a, b, and c (</code></pre>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
<section id="saving-your-trained-model" class="level1">
|
||
<h1>Saving your trained model</h1>
|
||
<p>Axolotl automatically saves checkpoints to the <code>output_dir</code> path.</p>
|
||
<div id="cell-18" class="cell" data-quarto-private-1="{"key":"colab","value":{"base_uri":"https://localhost:8080/"}}" data-outputid="f5e1d913-7d55-42d2-8340-f9f1b0bc2b38">
|
||
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Show the saved checkpoints in the output_dir</span></span>
|
||
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>ls <span class="op">-</span>lh <span class="st">"./outputs/qwen-sft-pirate-rrr"</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
|
||
<div class="cell-output cell-output-stdout">
|
||
<pre><code>total 506M
|
||
-rw-r--r-- 1 root root 845 May 7 22:21 adapter_config.json
|
||
-rw-r--r-- 1 root root 491M May 7 22:21 adapter_model.safetensors
|
||
-rw-r--r-- 1 root root 707 May 7 22:11 added_tokens.json
|
||
drwxr-xr-x 2 root root 4.0K May 7 22:17 checkpoint-13
|
||
drwxr-xr-x 2 root root 4.0K May 7 22:21 checkpoint-25
|
||
-rw-r--r-- 1 root root 1.2K May 7 22:11 config.json
|
||
-rw-r--r-- 1 root root 1.6M May 7 22:11 merges.txt
|
||
-rw-r--r-- 1 root root 2.6K May 7 22:21 README.md
|
||
-rw-r--r-- 1 root root 613 May 7 22:11 special_tokens_map.json
|
||
-rw-r--r-- 1 root root 9.5K May 7 22:11 tokenizer_config.json
|
||
-rw-r--r-- 1 root root 11M May 7 22:11 tokenizer.json
|
||
-rw-r--r-- 1 root root 2.7M May 7 22:11 vocab.json</code></pre>
|
||
</div>
|
||
</div>
|
||
<p>Setting <code>hub_model_id:</code> in the original config would have automatically uploaded the model to HuggingFace Hub (e.g. <code>hub_model_id: username/model_id</code>)</p>
|
||
<p>If you prefer to manually upload the training artifacts, we can still upload the entire final checkpoint to HuggingFace from the CLI.</p>
|
||
<div id="cell-20" class="cell" data-quarto-private-1="{"key":"colab","value":{"base_uri":"https://localhost:8080/","height":955,"referenced_widgets":["c12ea43372ac4d57bb9605f1a429b397","86816687746246b4a6105e8010384e25","6f05e9bebf7b40c9835808e77de6c236","c7433acd3c4841e6958ae8f7e87b1808","19c1e38389fa46c7b7e2152a56e1df34","0e067d8db8ed48308a718d5f57683fd1","131065f118274a1586ac38e39ed84ef0","8640ac440fbc4644b9a3af7ba3ae7183","5cea7996f02040b187ece0bb2d6a8d1f","2e257c8be2da40b4bb67a9e4ab6811f3","56e3768bef5a4b9db4168c5c17f509c2","62c028fdef904dedb9cdeca2b3bda725","a7cf477e80fc43e0ad82c7997b076dce","835bcc28a5564fb9b3d651bc8e32dc46","9f1c9a0695384bdaa6f8b847ef89bee8","b1bea589efa14258a9982071b87938bf","590eef89881545aa8bbef9a8bbe7fb00","4b1f04ff63d14a118fdd15814dff50e4","39789237703c4a418134243055c9cbf5","a3a945817f684328b34651fe052393ec"]}}" data-outputid="6e489ab2-4abe-4e28-84ca-959f912433a4">
|
||
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> huggingface_hub <span class="im">import</span> notebook_login</span>
|
||
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a><span class="co"># remove the partial epoch checkpoints</span></span>
|
||
<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>rm <span class="op">-</span>rf <span class="st">"./outputs/qwen-sft-pirate-rrr/checkpoint-*"</span></span>
|
||
<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a><span class="co"># HF Notebook login widget</span></span>
|
||
<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a>notebook_login()</span>
|
||
<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a><span class="co"># upload the LoRA adapter for your model to HF, remember to update the username/model-name below</span></span>
|
||
<span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>huggingface<span class="op">-</span>cli upload <span class="op">--</span>repo<span class="op">-</span><span class="bu">type</span><span class="op">=</span>model winglian<span class="op">/</span>pirate<span class="op">-</span>qwen<span class="op">-</span><span class="dv">14</span><span class="er">B</span> <span class="st">"./outputs/qwen-sft-pirate-rrr"</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
|
||
<div class="cell-output cell-output-display">
|
||
<script type="application/vnd.jupyter.widget-view+json">
|
||
{"model_id":"c12ea43372ac4d57bb9605f1a429b397","version_major":2,"version_minor":0,"quarto_mimetype":"application/vnd.jupyter.widget-view+json"}
|
||
</script>
|
||
</div>
|
||
<div class="cell-output cell-output-stdout">
|
||
<pre><code>It seems you are trying to upload a large folder at once. This might take some time and then fail if the folder is too large. For such cases, it is recommended to upload in smaller batches or to use `HfApi().upload_large_folder(...)`/`huggingface-cli upload-large-folder` instead. For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/upload#upload-a-large-folder.
|
||
Start hashing 40 files.
|
||
Finished hashing 40 files.
|
||
Uploading files using Xet Storage..
|
||
Uploading...: 87% 1.82G/2.10G [00:23<00:04, 67.3MB/s]Cancellation requested; stopping current tasks.
|
||
Traceback (most recent call last):
|
||
File "/usr/local/lib/python3.11/dist-packages/huggingface_hub/_commit_api.py", line 598, in _upload_xet_files
|
||
upload_files(
|
||
RuntimeError: Xet Runtime Error: Task cancelled; possible runtime shutdown in progress (task 9 was cancelled).
|
||
|
||
During handling of the above exception, another exception occurred:
|
||
|
||
Traceback (most recent call last):
|
||
File "/usr/local/bin/huggingface-cli", line 8, in <module>
|
||
sys.exit(main())
|
||
^^^^^^
|
||
File "/usr/local/lib/python3.11/dist-packages/huggingface_hub/commands/huggingface_cli.py", line 57, in main
|
||
service.run()
|
||
File "/usr/local/lib/python3.11/dist-packages/huggingface_hub/commands/upload.py", line 207, in run
|
||
print(self._upload())
|
||
^^^^^^^^^^^^^^
|
||
File "/usr/local/lib/python3.11/dist-packages/huggingface_hub/commands/upload.py", line 302, in _upload
|
||
return self.api.upload_folder(
|
||
^^^^^^^^^^^^^^^^^^^^^^^
|
||
File "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
|
||
return fn(*args, **kwargs)
|
||
^^^^^^^^^^^^^^^^^^^
|
||
File "/usr/local/lib/python3.11/dist-packages/huggingface_hub/hf_api.py", line 1633, in _inner
|
||
return fn(self, *args, **kwargs)
|
||
^^^^^^^^^^^^^^^^^^^^^^^^^
|
||
File "/usr/local/lib/python3.11/dist-packages/huggingface_hub/hf_api.py", line 4942, in upload_folder
|
||
commit_info = self.create_commit(
|
||
^^^^^^^^^^^^^^^^^^^
|
||
File "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
|
||
return fn(*args, **kwargs)
|
||
^^^^^^^^^^^^^^^^^^^
|
||
File "/usr/local/lib/python3.11/dist-packages/huggingface_hub/hf_api.py", line 1633, in _inner
|
||
return fn(self, *args, **kwargs)
|
||
^^^^^^^^^^^^^^^^^^^^^^^^^
|
||
File "/usr/local/lib/python3.11/dist-packages/huggingface_hub/hf_api.py", line 4202, in create_commit
|
||
self.preupload_lfs_files(
|
||
File "/usr/local/lib/python3.11/dist-packages/huggingface_hub/hf_api.py", line 4483, in preupload_lfs_files
|
||
_upload_xet_files(**upload_kwargs, create_pr=create_pr) # type: ignore [arg-type]
|
||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||
File "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
|
||
return fn(*args, **kwargs)
|
||
^^^^^^^^^^^^^^^^^^^
|
||
File "/usr/local/lib/python3.11/dist-packages/huggingface_hub/_commit_api.py", line 592, in _upload_xet_files
|
||
with progress_cm as progress:
|
||
File "/usr/local/lib/python3.11/dist-packages/tqdm/std.py", line 1138, in __exit__
|
||
def __exit__(self, exc_type, exc_value, traceback):
|
||
|
||
KeyboardInterrupt
|
||
^C</code></pre>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
</section>
|
||
|
||
</main> <!-- /main -->
|
||
<script type="application/vnd.jupyter.widget-state+json">
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const html = processXRef(null, note);
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||
instance.setContent(html);
|
||
}
|
||
}).finally(() => {
|
||
instance.enable();
|
||
instance.show();
|
||
});
|
||
}
|
||
}, function(instance) {
|
||
});
|
||
}
|
||
let selectedAnnoteEl;
|
||
const selectorForAnnotation = ( cell, annotation) => {
|
||
let cellAttr = 'data-code-cell="' + cell + '"';
|
||
let lineAttr = 'data-code-annotation="' + annotation + '"';
|
||
const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
|
||
return selector;
|
||
}
|
||
const selectCodeLines = (annoteEl) => {
|
||
const doc = window.document;
|
||
const targetCell = annoteEl.getAttribute("data-target-cell");
|
||
const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
|
||
const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
|
||
const lines = annoteSpan.getAttribute("data-code-lines").split(",");
|
||
const lineIds = lines.map((line) => {
|
||
return targetCell + "-" + line;
|
||
})
|
||
let top = null;
|
||
let height = null;
|
||
let parent = null;
|
||
if (lineIds.length > 0) {
|
||
//compute the position of the single el (top and bottom and make a div)
|
||
const el = window.document.getElementById(lineIds[0]);
|
||
top = el.offsetTop;
|
||
height = el.offsetHeight;
|
||
parent = el.parentElement.parentElement;
|
||
if (lineIds.length > 1) {
|
||
const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
|
||
const bottom = lastEl.offsetTop + lastEl.offsetHeight;
|
||
height = bottom - top;
|
||
}
|
||
if (top !== null && height !== null && parent !== null) {
|
||
// cook up a div (if necessary) and position it
|
||
let div = window.document.getElementById("code-annotation-line-highlight");
|
||
if (div === null) {
|
||
div = window.document.createElement("div");
|
||
div.setAttribute("id", "code-annotation-line-highlight");
|
||
div.style.position = 'absolute';
|
||
parent.appendChild(div);
|
||
}
|
||
div.style.top = top - 2 + "px";
|
||
div.style.height = height + 4 + "px";
|
||
div.style.left = 0;
|
||
let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
|
||
if (gutterDiv === null) {
|
||
gutterDiv = window.document.createElement("div");
|
||
gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
|
||
gutterDiv.style.position = 'absolute';
|
||
const codeCell = window.document.getElementById(targetCell);
|
||
const gutter = codeCell.querySelector('.code-annotation-gutter');
|
||
gutter.appendChild(gutterDiv);
|
||
}
|
||
gutterDiv.style.top = top - 2 + "px";
|
||
gutterDiv.style.height = height + 4 + "px";
|
||
}
|
||
selectedAnnoteEl = annoteEl;
|
||
}
|
||
};
|
||
const unselectCodeLines = () => {
|
||
const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
|
||
elementsIds.forEach((elId) => {
|
||
const div = window.document.getElementById(elId);
|
||
if (div) {
|
||
div.remove();
|
||
}
|
||
});
|
||
selectedAnnoteEl = undefined;
|
||
};
|
||
// Handle positioning of the toggle
|
||
window.addEventListener(
|
||
"resize",
|
||
throttle(() => {
|
||
elRect = undefined;
|
||
if (selectedAnnoteEl) {
|
||
selectCodeLines(selectedAnnoteEl);
|
||
}
|
||
}, 10)
|
||
);
|
||
function throttle(fn, ms) {
|
||
let throttle = false;
|
||
let timer;
|
||
return (...args) => {
|
||
if(!throttle) { // first call gets through
|
||
fn.apply(this, args);
|
||
throttle = true;
|
||
} else { // all the others get throttled
|
||
if(timer) clearTimeout(timer); // cancel #2
|
||
timer = setTimeout(() => {
|
||
fn.apply(this, args);
|
||
timer = throttle = false;
|
||
}, ms);
|
||
}
|
||
};
|
||
}
|
||
// Attach click handler to the DT
|
||
const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
|
||
for (const annoteDlNode of annoteDls) {
|
||
annoteDlNode.addEventListener('click', (event) => {
|
||
const clickedEl = event.target;
|
||
if (clickedEl !== selectedAnnoteEl) {
|
||
unselectCodeLines();
|
||
const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
|
||
if (activeEl) {
|
||
activeEl.classList.remove('code-annotation-active');
|
||
}
|
||
selectCodeLines(clickedEl);
|
||
clickedEl.classList.add('code-annotation-active');
|
||
} else {
|
||
// Unselect the line
|
||
unselectCodeLines();
|
||
clickedEl.classList.remove('code-annotation-active');
|
||
}
|
||
});
|
||
}
|
||
const findCites = (el) => {
|
||
const parentEl = el.parentElement;
|
||
if (parentEl) {
|
||
const cites = parentEl.dataset.cites;
|
||
if (cites) {
|
||
return {
|
||
el,
|
||
cites: cites.split(' ')
|
||
};
|
||
} else {
|
||
return findCites(el.parentElement)
|
||
}
|
||
} else {
|
||
return undefined;
|
||
}
|
||
};
|
||
var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
|
||
for (var i=0; i<bibliorefs.length; i++) {
|
||
const ref = bibliorefs[i];
|
||
const citeInfo = findCites(ref);
|
||
if (citeInfo) {
|
||
tippyHover(citeInfo.el, function() {
|
||
var popup = window.document.createElement('div');
|
||
citeInfo.cites.forEach(function(cite) {
|
||
var citeDiv = window.document.createElement('div');
|
||
citeDiv.classList.add('hanging-indent');
|
||
citeDiv.classList.add('csl-entry');
|
||
var biblioDiv = window.document.getElementById('ref-' + cite);
|
||
if (biblioDiv) {
|
||
citeDiv.innerHTML = biblioDiv.innerHTML;
|
||
}
|
||
popup.appendChild(citeDiv);
|
||
});
|
||
return popup.innerHTML;
|
||
});
|
||
}
|
||
}
|
||
});
|
||
</script>
|
||
</div> <!-- /content -->
|
||
|
||
|
||
|
||
|
||
</body></html> |