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<ul>
<li>If you are installing from pip</li>
</ul>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb2"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="ex">pip3</span> uninstall <span class="at">-y</span> cut-cross-entropy <span class="kw">&amp;&amp;</span> <span class="ex">pip3</span> install <span class="st">"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@49f3308"</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb2"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="ex">pip3</span> uninstall <span class="at">-y</span> cut-cross-entropy <span class="kw">&amp;&amp;</span> <span class="ex">pip3</span> install <span class="st">"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@8a1a0ec"</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</section>
<section id="usage" class="level3">
<h3 class="anchored" data-anchor-id="usage">Usage</h3>

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<li><p><strong>Eliminate concurrency</strong>: Restrict the number of processes to 1 for both training and data preprocessing:</p>
<ul>
<li>Set <code>CUDA_VISIBLE_DEVICES</code> to a single GPU, ex: <code>export CUDA_VISIBLE_DEVICES=0</code>.</li>
<li>Set <code>dataset_processes: 1</code> in your axolotl config or run the training command with <code>--dataset_processes=1</code>.</li>
<li>Set <code>dataset_num_proc: 1</code> in your axolotl config or run the training command with <code>--dataset_num_proc=1</code>.</li>
</ul></li>
<li><p><strong>Use a small dataset</strong>: Construct or use a small dataset from HF Hub. When using a small dataset, you will often have to make sure <code>sample_packing: False</code> and <code>eval_sample_packing: False</code> to avoid errors. If you are in a pinch and don’t have time to construct a small dataset but want to use from the HF Hub, you can shard the data (this will still tokenize the entire dataset, but will only use a fraction of the data for training. For example, to shard the dataset into 20 pieces, add the following to your axolotl config):</p>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb1"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">datasets</span><span class="kw">:</span></span>
@@ -653,7 +653,7 @@ If you prefer to watch a video, rather than read, you can skip to the <a href="#
<span id="cb4-11"><a href="#cb4-11" aria-hidden="true" tabindex="-1"></a> <span class="st">"-m"</span><span class="ot">,</span> <span class="st">"axolotl.cli.train"</span><span class="ot">,</span> <span class="st">"dev_chat_template.yml"</span><span class="ot">,</span></span>
<span id="cb4-12"><a href="#cb4-12" aria-hidden="true" tabindex="-1"></a> <span class="er">//</span> <span class="er">The</span> <span class="er">flags</span> <span class="er">below</span> <span class="er">simplify</span> <span class="er">debugging</span> <span class="er">by</span> <span class="er">overriding</span> <span class="er">the</span> <span class="er">axolotl</span> <span class="er">config</span></span>
<span id="cb4-13"><a href="#cb4-13" aria-hidden="true" tabindex="-1"></a> <span class="er">//</span> <span class="er">with</span> <span class="er">the</span> <span class="er">debugging</span> <span class="er">tips</span> <span class="er">above.</span> <span class="er">Modify</span> <span class="er">as</span> <span class="er">needed.</span></span>
<span id="cb4-14"><a href="#cb4-14" aria-hidden="true" tabindex="-1"></a> <span class="st">"--dataset_processes=1"</span><span class="ot">,</span> <span class="er">//</span> <span class="er">limits</span> <span class="er">data</span> <span class="er">preprocessing</span> <span class="er">to</span> <span class="er">one</span> <span class="er">process</span></span>
<span id="cb4-14"><a href="#cb4-14" aria-hidden="true" tabindex="-1"></a> <span class="st">"--dataset_num_proc=1"</span><span class="ot">,</span> <span class="er">//</span> <span class="er">limits</span> <span class="er">data</span> <span class="er">preprocessing</span> <span class="er">to</span> <span class="er">one</span> <span class="er">process</span></span>
<span id="cb4-15"><a href="#cb4-15" aria-hidden="true" tabindex="-1"></a> <span class="st">"--max_steps=1"</span><span class="ot">,</span> <span class="er">//</span> <span class="er">limits</span> <span class="er">training</span> <span class="er">to</span> <span class="er">just</span> <span class="er">one</span> <span class="er">step</span></span>
<span id="cb4-16"><a href="#cb4-16" aria-hidden="true" tabindex="-1"></a> <span class="st">"--batch_size=1"</span><span class="ot">,</span> <span class="er">//</span> <span class="er">minimizes</span> <span class="er">batch</span> <span class="er">size</span></span>
<span id="cb4-17"><a href="#cb4-17" aria-hidden="true" tabindex="-1"></a> <span class="st">"--micro_batch_size=1"</span><span class="ot">,</span> <span class="er">//</span> <span class="er">minimizes</span> <span class="er">batch</span> <span class="er">size</span></span>

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<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">&gt;=</span><span class="fl">0.9.1</span></span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install <span class="st">"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@49f3308"</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install <span class="st">"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@8a1a0ec"</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>

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