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How to debug Axolotl
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<p>This document provides some tips and tricks for debugging Axolotl. It also provides an example configuration for debugging with VSCode. A good debugging setup is essential to understanding how Axolotl code works behind the scenes.</p>
<section id="table-of-contents" class="level2">
<h2 class="anchored" data-anchor-id="table-of-contents">Table of Contents</h2>
<ul>
<li><a href="#general-tips">General Tips</a></li>
<li><a href="#debugging-with-vscode">Debugging with VSCode</a>
<ul>
<li><a href="#background">Background</a></li>
<li><a href="#configuration">Configuration</a></li>
<li><a href="#customizing-your-debugger">Customizing your debugger</a></li>
<li><a href="#video-tutorial">Video Tutorial</a></li>
</ul></li>
<li><a href="#debugging-with-docker">Debugging With Docker</a>
<ul>
<li><a href="#setup">Setup</a></li>
<li><a href="#attach-to-container">Attach To Container</a></li>
<li><a href="#video---attaching-to-docker-on-remote-host">Video - Attaching To Docker On Remote Host</a></li>
</ul></li>
</ul>
</section>
<section id="general-tips" class="level2">
<h2 class="anchored" data-anchor-id="general-tips">General Tips</h2>
<p>While debugging its helpful to simplify your test scenario as much as possible. Here are some tips for doing so:</p>
<blockquote class="blockquote">
<p>[!Important] All of these tips are incorporated into the <a href="#configuration">example configuration</a> for debugging with VSCode below.</p>
</blockquote>
<ol type="1">
<li><strong>Make sure you are using the latest version of axolotl</strong>: This project changes often and bugs get fixed fast. Check your git branch and make sure you have pulled the latest changes from <code>main</code>.</li>
<li><strong>Eliminate concurrency</strong>: Restrict the number of processes to 1 for both training and data preprocessing:
<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>
</ul></li>
<li><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 dont 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): <code>yaml dataset: ... shards: 20</code></li>
<li><strong>Use a small model</strong>: A good example of a small model is <a href="https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0">TinyLlama/TinyLlama-1.1B-Chat-v1.0</a>.</li>
<li><strong>Minimize iteration time</strong>: Make sure the training loop finishes as fast as possible, with these settings.
<ul>
<li><code>micro_batch_size: 1</code></li>
<li><code>max_steps: 1</code></li>
<li><code>val_set_size: 0</code></li>
</ul></li>
<li><strong>Clear Caches:</strong> Axolotl caches certain steps and so does the underlying HuggingFace trainer. You may want to clear some of these caches when debugging.
<ul>
<li>Data preprocessing: When debugging data preprocessing, which includes prompt template formation, you may want to delete the directory set in <code>dataset_prepared_path:</code> in your axolotl config. If you didnt set this value, the default is <code>last_run_prepared</code>.</li>
<li>HF Hub: If you are debugging data preprocessing, you should clear the relevant HF cache <a href="https://huggingface.co/docs/datasets/cache">HuggingFace cache</a>, by deleting the appropriate <code>~/.cache/huggingface/datasets/...</code> folder(s).</li>
<li><strong>The recommended approach is to redirect all outputs and caches to a temporary folder and delete selected subfolders before each run. This is demonstrated in the example configuration below.</strong></li>
</ul></li>
</ol>
</section>
<section id="debugging-with-vscode" class="level2">
<h2 class="anchored" data-anchor-id="debugging-with-vscode">Debugging with VSCode</h2>
<section id="background" class="level3">
<h3 class="anchored" data-anchor-id="background">Background</h3>
<p>The below example shows how to configure VSCode to debug data preprocessing of the <code>sharegpt</code> format. This is the format used when you have the following in your axolotl config:</p>
<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>
<span id="cb1-2"><a href="#cb1-2" 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"> &lt;path to your sharegpt formatted dataset&gt;</span><span class="co"> # example on HF Hub: philschmid/guanaco-sharegpt-style</span></span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">type</span><span class="kw">:</span><span class="at"> sharegpt</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<blockquote class="blockquote">
<p>[!Important] If you are already familiar with advanced VSCode debugging, you can skip the below explanation and look at the files <a href="../.vscode/launch.json">.vscode/launch.json</a> and <a href="../.vscode/tasks.json">.vscode/tasks.json</a> for an example configuration.</p>
</blockquote>
<blockquote class="blockquote">
<p>[!Tip] If you prefer to watch a video, rather than read, you can skip to the <a href="#video-tutorial">video tutorial</a> below (but doing both is recommended).</p>
</blockquote>
</section>
<section id="setup" class="level3">
<h3 class="anchored" data-anchor-id="setup">Setup</h3>
<p>Make sure you have an <a href="https://setuptools.pypa.io/en/latest/userguide/development_mode.html">editable install</a> of Axolotl, which ensures that changes you make to the code are reflected at runtime. Run the following commands from the root of this project:</p>
<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> install packaging</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="ex">pip3</span> install <span class="at">-e</span> <span class="st">'.[flash-attn,deepspeed]'</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<section id="remote-hosts" class="level4">
<h4 class="anchored" data-anchor-id="remote-hosts">Remote Hosts</h4>
<p>If you developing on a remote host, you can easily use VSCode to debug remotely. To do so, you will need to follow this <a href="https://code.visualstudio.com/docs/remote/ssh">remote - SSH guide</a>. You can also see the video below on <a href="#video---attaching-to-docker-on-remote-host">Docker and Remote SSH debugging</a>.</p>
</section>
</section>
<section id="configuration" class="level3">
<h3 class="anchored" data-anchor-id="configuration">Configuration</h3>
<p>The easiest way to get started is to modify the <a href="../.vscode/launch.json">.vscode/launch.json</a> file in this project. This is just an example configuration, so you may need to modify or copy it to suit your needs.</p>
<p>For example, to mimic the command <code>cd devtools &amp;&amp; CUDA_VISIBLE_DEVICES=0 accelerate launch -m axolotl.cli.train dev_sharegpt.yml</code>, you would use the below configuration<a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a>. Note that we add additional flags that override the axolotl config and incorporate the tips above (see the comments). We also set the working directory to <code>devtools</code> and set the <code>env</code> variable <code>HF_HOME</code> to a temporary folder that is later partially deleted. This is because we want to delete the HF dataset cache before each run in order to ensure that the data preprocessing code is run from scratch.</p>
<pre class="jsonc"><code>// .vscode/launch.json
{
"version": "0.2.0",
"configurations": [
{
"name": "Debug axolotl prompt - sharegpt",
"type": "python",
"module": "accelerate.commands.launch",
"request": "launch",
"args": [
"-m", "axolotl.cli.train", "dev_sharegpt.yml",
// The flags below simplify debugging by overriding the axolotl config
// with the debugging tips above. Modify as needed.
"--dataset_processes=1", // limits data preprocessing to one process
"--max_steps=1", // limits training to just one step
"--batch_size=1", // minimizes batch size
"--micro_batch_size=1", // minimizes batch size
"--val_set_size=0", // disables validation
"--sample_packing=False", // disables sample packing which is necessary for small datasets
"--eval_sample_packing=False",// disables sample packing on eval set
"--dataset_prepared_path=temp_debug/axolotl_outputs/data", // send data outputs to a temp folder
"--output_dir=temp_debug/axolotl_outputs/model" // send model outputs to a temp folder
],
"console": "integratedTerminal", // show output in the integrated terminal
"cwd": "${workspaceFolder}/devtools", // set working directory to devtools from the root of the project
"justMyCode": true, // step through only axolotl code
"env": {"CUDA_VISIBLE_DEVICES": "0", // Since we aren't doing distributed training, we need to limit to one GPU
"HF_HOME": "${workspaceFolder}/devtools/temp_debug/.hf-cache"}, // send HF cache to a temp folder
"preLaunchTask": "cleanup-for-dataprep", // delete temp folders (see below)
}
]
}</code></pre>
<p><strong>Additional notes about this configuration:</strong></p>
<ul>
<li>The argument <code>justMyCode</code> is set to <code>true</code> such that you step through only the axolotl code. If you want to step into dependencies, set this to <code>false</code>.</li>
<li>The <code>preLaunchTask</code>: <code>cleanup-for-dataprep</code> is defined in <a href="../.vscode/tasks.json">.vscode/tasks.json</a> and is used to delete the following folders before debugging, which is essential to ensure that the data pre-processing code is run from scratch:
<ul>
<li><code>./devtools/temp_debug/axolotl_outputs</code></li>
<li><code>./devtools/temp_debug/.hf-cache/datasets</code></li>
</ul></li>
</ul>
<blockquote class="blockquote">
<p>[!Tip] You may not want to delete these folders. For example, if you are debugging model training instead of data pre-processing, you may NOT want to delete the cache or output folders. You may also need to add additional tasks to the <code>tasks.json</code> file depending on your use case.</p>
</blockquote>
<p>Below is the <a href="../.vscode/tasks.json">./vscode/tasks.json</a> file that defines the <code>cleanup-for-dataprep</code> task. This task is run before each debugging session when you use the above configuration. Note how there are two tasks that delete the two folders mentioned above. The third task <code>cleanup-for-dataprep</code> is a composite task that combines the two tasks. A composite task is necessary because VSCode does not allow you to specify multiple tasks in the <code>preLaunchTask</code> argument of the <code>launch.json</code> file.</p>
<pre class="jsonc"><code>// .vscode/tasks.json
// this file is used by launch.json
{
"version": "2.0.0",
"tasks": [
// this task changes into the devtools directory and deletes the temp_debug/axolotl_outputs folder
{
"label": "delete-outputs",
"type": "shell",
"command": "rm -rf temp_debug/axolotl_outputs",
"options":{ "cwd": "${workspaceFolder}/devtools"},
"problemMatcher": []
},
// this task changes into the devtools directory and deletes the `temp_debug/.hf-cache/datasets` folder
{
"label": "delete-temp-hf-dataset-cache",
"type": "shell",
"command": "rm -rf temp_debug/.hf-cache/datasets",
"options":{ "cwd": "${workspaceFolder}/devtools"},
"problemMatcher": []
},
// this task combines the two tasks above
{
"label": "cleanup-for-dataprep",
"dependsOn": ["delete-outputs", "delete-temp-hf-dataset-cache"],
}
]
}</code></pre>
</section>
<section id="customizing-your-debugger" class="level3">
<h3 class="anchored" data-anchor-id="customizing-your-debugger">Customizing your debugger</h3>
<p>Your debugging use case may differ from the example above. The easiest thing to do is to put your own axolotl config in the <code>devtools</code> folder and modify the <code>launch.json</code> file to use your config. You may also want to modify the <code>preLaunchTask</code> to delete different folders or not delete anything at all.</p>
</section>
<section id="video-tutorial" class="level3">
<h3 class="anchored" data-anchor-id="video-tutorial">Video Tutorial</h3>
<p>The following video tutorial walks through the above configuration and demonstrates how to debug with VSCode, (click the image below to watch):</p>
<div style="text-align: center; line-height: 0;">
<p><a href="https://youtu.be/xUUB11yeMmc" target="_blank" title="How to debug Axolotl (for fine tuning LLMs)"><img src="https://i.ytimg.com/vi/xUUB11yeMmc/maxresdefault.jpg" style="border-radius: 10px; display: block; margin: auto;" width="560" height="315"></a></p>
<figcaption style="font-size: smaller;">
<a href="https://hamel.dev">Hamel Husains</a> tutorial: <a href="https://www.youtube.com/watch?v=xUUB11yeMmc">Debugging Axolotl w/VSCode</a>
</figcaption>
</div>
<p><br></p>
</section>
</section>
<section id="debugging-with-docker" class="level2">
<h2 class="anchored" data-anchor-id="debugging-with-docker">Debugging With Docker</h2>
<p>Using <a href="https://hub.docker.com/r/winglian/axolotl/tags">official Axolotl Docker images</a> is a great way to debug your code, and is a very popular way to use Axolotl. Attaching VSCode to Docker takes a few more steps.</p>
<section id="setup-1" class="level3">
<h3 class="anchored" data-anchor-id="setup-1">Setup</h3>
<p>On the host that is running axolotl (ex: if you are using a remote host), clone the axolotl repo and change your current directory to the root:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="fu">git</span> clone https://github.com/OpenAccess-AI-Collective/axolotl</span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="bu">cd</span> axolotl</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<blockquote class="blockquote">
<p>[!Tip] If you already have axolotl cloned on your host, make sure you have the latest changes and change into the root of the project.</p>
</blockquote>
<p>Next, run the desired docker image and mount the current directory. Below is a docker command you can run to do this:<a href="#fn2" class="footnote-ref" id="fnref2" role="doc-noteref"><sup>2</sup></a></p>
<div class="sourceCode" id="cb6"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="ex">docker</span> run <span class="at">--privileged</span> <span class="at">--gpus</span> <span class="st">'"all"'</span> <span class="at">--shm-size</span> 10g <span class="at">--rm</span> <span class="at">-it</span> <span class="at">--name</span> axolotl <span class="at">--ipc</span><span class="op">=</span>host <span class="at">--ulimit</span> memlock=-1 <span class="at">--ulimit</span> stack=67108864 <span class="at">--mount</span> type=bind,src=<span class="st">"</span><span class="va">${PWD}</span><span class="st">"</span>,target=/workspace/axolotl <span class="at">-v</span> <span class="va">${HOME}</span>/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<blockquote class="blockquote">
<p>[!Tip] To understand which containers are available, see the <a href="../README.md#docker">Docker section of the README</a> and the <a href="https://hub.docker.com/r/winglian/axolotl/tags">DockerHub repo</a>. For details of how the Docker containers are built, see axolotls <a href="../.github/workflows/main.yml">Docker CI builds</a>.</p>
</blockquote>
<p>You will now be in the container. Next, perform an editable install of Axolotl:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="ex">pip3</span> install packaging</span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="ex">pip3</span> install <span class="at">-e</span> <span class="st">'.[flash-attn,deepspeed]'</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</section>
<section id="attach-to-container" class="level3">
<h3 class="anchored" data-anchor-id="attach-to-container">Attach To Container</h3>
<p>Next, if you are using a remote host, <a href="https://code.visualstudio.com/docs/remote/ssh">Remote into this host with VSCode</a>. If you are using a local host, you can skip this step.</p>
<p>Next, select <code>Dev Containers: Attach to Running Container...</code> using the command palette (<code>CMD + SHIFT + P</code>) in VSCode. You will be prompted to select a container to attach to. Select the container you just created. You will now be in the container with a working directory that is at the root of the project. Any changes you make to the code will be reflected both in the container and on the host.</p>
<p>Now you are ready to debug as described above (see <a href="#debugging-with-vscode">Debugging with VSCode</a>).</p>
</section>
<section id="video---attaching-to-docker-on-remote-host" class="level3">
<h3 class="anchored" data-anchor-id="video---attaching-to-docker-on-remote-host">Video - Attaching To Docker On Remote Host</h3>
<p>Here is a short video that demonstrates how to attach to a Docker container on a remote host:</p>
<div style="text-align: center; line-height: 0;">
<p><a href="https://youtu.be/0AuoR7QnHR0" target="_blank" title="Debugging Axolotl Part 2: Attaching to Docker on a Remote Host"><img src="https://i.ytimg.com/vi/0AuoR7QnHR0/hqdefault.jpg" style="border-radius: 10px; display: block; margin: auto;" width="560" height="315"></a></p>
<figcaption style="font-size: smaller;">
<a href="https://hamel.dev">Hamel Husains</a> tutorial: <a href="https://youtu.be/0AuoR7QnHR0">Debugging Axolotl Part 2: Attaching to Docker on a Remote Host </a>
</figcaption>
</div>
<p><br></p>
</section>
</section>
<div id="quarto-appendix" class="default"><section id="footnotes" class="footnotes footnotes-end-of-document" role="doc-endnotes"><h2 class="anchored quarto-appendix-heading">Footnotes</h2>
<ol>
<li id="fn1"><p>The config actually mimics the command <code>CUDA_VISIBLE_DEVICES=0 python -m accelerate.commands.launch -m axolotl.cli.train devtools/sharegpt.yml</code>, but this is the same thing.<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn2"><p>Many of the below flags are recommended best practices by Nvidia when using nvidia-container-toolkit. You can read more about these flags <a href="https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html">here</a>.<a href="#fnref2" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
</ol>
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<h1 class="title">FDSP + QLoRA</h1>
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Use FSDP with QLoRA to fine-tune large LLMs on consumer GPUs.
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<h2 class="anchored" data-anchor-id="background">Background</h2>
<p>Using FSDP with QLoRA is essential for <strong>fine-tuning larger (70b+ parameter) LLMs on consumer GPUs.</strong> For example, you can use FSDP + QLoRA to train a 70b model on two 24GB GPUs<a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a>.</p>
<p>Below, we describe how to use this feature in Axolotl.</p>
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<p>To enable <code>QLoRA</code> with <code>FSDP</code>, you need to perform the following steps:</p>
<blockquote class="blockquote">
<p>![Tip] See the <a href="#example-config">example config</a> file in addition to reading these instructions.</p>
</blockquote>
<ol type="1">
<li>Set <code>adapter: qlora</code> in your axolotl config file.</li>
<li>Enable FSDP in your axolotl config, as <a href="https://github.com/OpenAccess-AI-Collective/axolotl?tab=readme-ov-file#fsdp">described here</a>.</li>
<li>Use one of the supported model types: <code>llama</code>, <code>mistral</code> or <code>mixtral</code>.</li>
</ol>
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<h2 class="anchored" data-anchor-id="example-config">Example Config</h2>
<p><a href="../examples/llama-2/qlora-fsdp.yml">examples/llama-2/qlora-fsdp.yml</a> contains an example of how to enable QLoRA + FSDP in axolotl.</p>
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<h2 class="anchored" data-anchor-id="references">References</h2>
<ul>
<li><a href="https://github.com/OpenAccess-AI-Collective/axolotl/pull/1378">PR #1378</a> enabling QLoRA in FSDP in Axolotl.</li>
<li><a href="https://www.answer.ai/posts/2024-03-06-fsdp-qlora.html">Blog Post</a> from the <a href="https://www.answer.ai/">Answer.AI</a> team describing the work that enabled QLoRA in FSDP.</li>
<li>Related HuggingFace PRs Enabling FDSP + QLoRA:
<ul>
<li>Accelerate <a href="https://github.com/huggingface/accelerate/pull/2544">PR#2544</a></li>
<li>Transformers <a href="https://github.com/huggingface/transformers/pull/29587">PR#29587</a></li>
<li>TRL <a href="https://github.com/huggingface/trl/pull/1416">PR#1416</a></li>
<li>PEFT <a href="https://github.com/huggingface/peft/pull/1550">PR#1550</a></li>
</ul></li>
</ul>
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<li id="fn1"><p>This was enabled by <a href="https://www.answer.ai/posts/2024-03-06-fsdp-qlora.html">this work</a> from the Answer.AI team.<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
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<h1 class="title">FDSP + QLoRA</h1>
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<div>
<div class="description">
Use FSDP with QLoRA to fine-tune large LLMs on consumer GPUs.
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<h2 class="anchored" data-anchor-id="background">Background</h2>
<p>Using FSDP with QLoRA is essential for <strong>fine-tuning larger (70b+ parameter) LLMs on consumer GPUs.</strong> For example, you can use FSDP + QLoRA to train a 70b model on two 24GB GPUs<a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a>.</p>
<p>Below, we describe how to use this feature in Axolotl.</p>
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<h2 class="anchored" data-anchor-id="usage">Usage</h2>
<p>To enable <code>QLoRA</code> with <code>FSDP</code>, you need to perform the following steps:</p>
<blockquote class="blockquote">
<p>![Tip] See the <a href="#example-config">example config</a> file in addition to reading these instructions.</p>
</blockquote>
<ol type="1">
<li>Set <code>adapter: qlora</code> in your axolotl config file.</li>
<li>Enable FSDP in your axolotl config, as <a href="https://github.com/OpenAccess-AI-Collective/axolotl?tab=readme-ov-file#fsdp">described here</a>.</li>
<li>Use one of the supported model types: <code>llama</code>, <code>mistral</code> or <code>mixtral</code>.</li>
</ol>
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<section id="example-config" class="level2">
<h2 class="anchored" data-anchor-id="example-config">Example Config</h2>
<p><a href="../examples/llama-2/qlora-fsdp.yml">examples/llama-2/qlora-fsdp.yml</a> contains an example of how to enable QLoRA + FSDP in axolotl.</p>
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<section id="references" class="level2">
<h2 class="anchored" data-anchor-id="references">References</h2>
<ul>
<li><a href="https://github.com/OpenAccess-AI-Collective/axolotl/pull/1378">PR #1378</a> enabling QLoRA in FSDP in Axolotl.</li>
<li><a href="https://www.answer.ai/posts/2024-03-06-fsdp-qlora.html">Blog Post</a> from the <a href="https://www.answer.ai/">Answer.AI</a> team describing the work that enabled QLoRA in FSDP.</li>
<li>Related HuggingFace PRs Enabling FDSP + QLoRA:
<ul>
<li>Accelerate <a href="https://github.com/huggingface/accelerate/pull/2544">PR#2544</a></li>
<li>Transformers <a href="https://github.com/huggingface/transformers/pull/29587">PR#29587</a></li>
<li>TRL <a href="https://github.com/huggingface/trl/pull/1416">PR#1416</a></li>
<li>PEFT <a href="https://github.com/huggingface/peft/pull/1550">PR#1550</a></li>
</ul></li>
</ul>
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<div id="quarto-appendix" class="default"><section id="footnotes" class="footnotes footnotes-end-of-document" role="doc-endnotes"><h2 class="anchored quarto-appendix-heading">Footnotes</h2>
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<li id="fn1"><p>This was enabled by <a href="https://www.answer.ai/posts/2024-03-06-fsdp-qlora.html">this work</a> from the Answer.AI team.<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
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<li><a href="#masking-inputs" id="toc-masking-inputs" class="nav-link" data-scroll-target="#masking-inputs">Masking Inputs</a></li>
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<li><a href="#the-input_output-format" id="toc-the-input_output-format" class="nav-link" data-scroll-target="#the-input_output-format">The <code>input_output</code> format</a></li>
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<li><a href="#prepare-data" id="toc-prepare-data" class="nav-link" data-scroll-target="#prepare-data">1. Prepare Data</a></li>
<li><a href="#use-type-input_output" id="toc-use-type-input_output" class="nav-link" data-scroll-target="#use-type-input_output">2. Use <code>type: input_output</code></a></li>
<li><a href="#check-the-prompts" id="toc-check-the-prompts" class="nav-link" data-scroll-target="#check-the-prompts">3. Check the prompts</a></li>
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<h1 class="title">Template-free prompt construction</h1>
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Template-free prompt construction with the <code>input_output</code> format
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<ul>
<li><a href="#background">Background</a>
<ul>
<li><a href="#masking-inputs">Masking Inputs</a></li>
<li><a href="#you-may-not-want-prompt-templates">You may not want prompt templates</a></li>
<li><a href="#the-input_output-format">The <code>input_output</code> format</a></li>
</ul></li>
<li><a href="#usage">Usage</a>
<ul>
<li><a href="#1-prepare-data">1. Prepare Data</a></li>
<li><a href="#2-use-type-input_output">2. Use <code>type: input_output</code></a></li>
<li><a href="#3-check-the-prompts">3. Check the prompts</a></li>
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<p><a id="markdown-background" name="background"></a></p>
<section id="background" class="level2">
<h2 class="anchored" data-anchor-id="background">Background</h2>
<p><a id="markdown-masking-inputs" name="masking-inputs"></a></p>
<section id="masking-inputs" class="level3">
<h3 class="anchored" data-anchor-id="masking-inputs">Masking Inputs</h3>
<p>One of the most popular features of <a href="https://github.com/OpenAccess-AI-Collective/axolotl">axolotl</a> is setting the following configuration value:</p>
<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">train_on_inputs</span><span class="kw">:</span><span class="at"> </span><span class="ch">false</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p>If you declare a <a href="https://github.com/OpenAccess-AI-Collective/axolotl?tab=readme-ov-file#dataset">dataset formats</a> such as <code>alpaca</code> or <code>chatml</code>, axolotl knows what is an input (i.e.&nbsp;human) vs.&nbsp;an output (i.e.&nbsp;the assistant) and masks the input labels so that your model can focus on predicting the outputs only.</p>
<p><a id="markdown-you-may-not-want-prompt-templates" name="you-may-not-want-prompt-templates"></a></p>
</section>
<section id="you-may-not-want-prompt-templates" class="level3">
<h3 class="anchored" data-anchor-id="you-may-not-want-prompt-templates">You may not want prompt templates</h3>
<p>However, there are many situations where you dont want to use one of these formats or templates (I usually dont!). This is because they can:</p>
<ul>
<li>Add unnecessary boilerplate to your prompts.</li>
<li>Create artifacts like special delimiters <code>&lt;|im_start|&gt;</code> that can quickly become footguns if you dont include them correctly at inference time.</li>
<li>Enforce a <em>chat</em> interface when you do not want one. Sometimes you just want to fine-tune a model to a very specific task and do NOT want multi-turn conversations, roles, etc.</li>
<li>Limit you to only certain roles that the template allows.</li>
</ul>
<p><a id="markdown-the-inputoutput-format" name="the-inputoutput-format"></a></p>
</section>
<section id="the-input_output-format" class="level3">
<h3 class="anchored" data-anchor-id="the-input_output-format">The <code>input_output</code> format</h3>
<p>You can construct your prompts without a template by using the <code>input_output</code> format, by setting <code>type: input_output</code> in your configuration file like this:</p>
<p><strong>config.yml</strong></p>
<div class="sourceCode" id="cb2"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">train_on_inputs</span><span class="kw">:</span><span class="at"> </span><span class="ch">false</span><span class="co"> # Mask segments of your data</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">datasets</span><span class="kw">:</span></span>
<span id="cb2-3"><a href="#cb2-3" 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"> output.jsonl</span></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">type</span><span class="kw">:</span><span class="at"> input_output</span><span class="co"> # use template free prompt construction</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p>Unlike <code>type: completion</code>, which is also template-free, <code>type: input_output</code> allows you to mask segments of your text. More details on how this works are described below.</p>
<p><a id="markdown-usage" name="usage"></a></p>
</section>
</section>
<section id="usage" class="level2">
<h2 class="anchored" data-anchor-id="usage">Usage</h2>
<p>This is how you can use the <code>input_output</code> format:</p>
<p><a id="markdown-1-prepare-data" name="1-prepare-data"></a></p>
<section id="prepare-data" class="level3">
<h3 class="anchored" data-anchor-id="prepare-data">1. Prepare Data</h3>
<p>To use the <code>input_output</code> format, collect your data in the following format into a jsonl file (below is the first row from the file <code>output</code>.jsonl` pretty printed):</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="ex">$</span> head <span class="at">-n1</span> output.jsonl <span class="kw">|</span> <span class="ex">python</span> <span class="at">-m</span> json.tool</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="ex">{.cell-output</span> .cell-output-stdout}</span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a> <span class="kw">{</span></span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a> <span class="st">"segments"</span><span class="ex">:</span> [</span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a> <span class="kw">{</span></span>
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a> <span class="st">"label"</span><span class="ex">:</span> true,</span>
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a> <span class="st">"text"</span><span class="ex">:</span> <span class="st">"&lt;s&gt;Hello\n"</span></span>
<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a> <span class="ex">},</span></span>
<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a> <span class="kw">{</span></span>
<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a> <span class="st">"label"</span><span class="ex">:</span> true,</span>
<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a> <span class="st">"text"</span><span class="ex">:</span> <span class="st">"hi there!. "</span></span>
<span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a> <span class="ex">},</span></span>
<span id="cb3-14"><a href="#cb3-14" aria-hidden="true" tabindex="-1"></a> <span class="kw">{</span></span>
<span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a> <span class="st">"label"</span><span class="ex">:</span> false,</span>
<span id="cb3-16"><a href="#cb3-16" aria-hidden="true" tabindex="-1"></a> <span class="st">"text"</span><span class="ex">:</span> <span class="st">"goodbye "</span></span>
<span id="cb3-17"><a href="#cb3-17" aria-hidden="true" tabindex="-1"></a> <span class="ex">},</span></span>
<span id="cb3-18"><a href="#cb3-18" aria-hidden="true" tabindex="-1"></a> <span class="kw">{</span></span>
<span id="cb3-19"><a href="#cb3-19" aria-hidden="true" tabindex="-1"></a> <span class="st">"label"</span><span class="ex">:</span> true,</span>
<span id="cb3-20"><a href="#cb3-20" aria-hidden="true" tabindex="-1"></a> <span class="st">"text"</span><span class="ex">:</span> <span class="st">"farewell&lt;/s&gt;"</span></span>
<span id="cb3-21"><a href="#cb3-21" aria-hidden="true" tabindex="-1"></a> <span class="kw">}</span></span>
<span id="cb3-22"><a href="#cb3-22" aria-hidden="true" tabindex="-1"></a> <span class="ex">]</span></span>
<span id="cb3-23"><a href="#cb3-23" aria-hidden="true" tabindex="-1"></a> <span class="kw">}</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p>Set <code>label:false</code> when you want to mask a segment of text so that the model isnt trained on it. Some things to keep in mind:</p>
<blockquote class="blockquote">
<p>[!IMPORTANT] 1. <strong>EOS, BOS, spaces, newlines etc. are entirely up to you. Axolotl concatenates all the segments as-is.</strong> The tokenizer doesnt add anything additional. Notice how I added spaces, newlines, <code>&lt;s&gt;</code> (BOS), and <code>&lt;/s&gt;</code> (EOS) myself. 2. Make sure you check the materialized output to validate that the prompt is getting assembled how you like.</p>
</blockquote>
<p><a id="markdown-2-use-type-inputoutput" name="2-use-type-inputoutput"></a></p>
</section>
<section id="use-type-input_output" class="level3">
<h3 class="anchored" data-anchor-id="use-type-input_output">2. Use <code>type: input_output</code></h3>
<p>Lets materialize data with our <code>output.jsonl</code> file by setting <code>type: input_output</code> in our axolotl config:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co"># training_config.yaml</span></span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="fu">base_model</span><span class="kw">:</span><span class="at"> mistralai/Mistral-7B-v0.1</span></span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a><span class="fu">data_seed</span><span class="kw">:</span><span class="at"> </span><span class="dv">49</span></span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a><span class="fu">seed</span><span class="kw">:</span><span class="at"> </span><span class="dv">49</span></span>
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a><span class="fu">datasets</span><span class="kw">:</span></span>
<span id="cb4-7"><a href="#cb4-7" 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"> output.jsonl</span></span>
<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">type</span><span class="kw">:</span><span class="at"> input_output</span></span>
<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a><span class="fu">val_set_size</span><span class="kw">:</span><span class="at"> </span><span class="fl">0.1</span></span>
<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb4-11"><a href="#cb4-11" aria-hidden="true" tabindex="-1"></a><span class="fu">sequence_len</span><span class="kw">:</span><span class="at"> </span><span class="dv">896</span></span>
<span id="cb4-12"><a href="#cb4-12" aria-hidden="true" tabindex="-1"></a><span class="fu">sample_packing</span><span class="kw">:</span><span class="at"> </span><span class="ch">false</span></span>
<span id="cb4-13"><a href="#cb4-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb4-14"><a href="#cb4-14" aria-hidden="true" tabindex="-1"></a><span class="fu">micro_batch_size</span><span class="kw">:</span><span class="at"> </span><span class="dv">2</span></span>
<span id="cb4-15"><a href="#cb4-15" aria-hidden="true" tabindex="-1"></a><span class="fu">gradient_accumulation_steps</span><span class="kw">:</span><span class="at"> </span><span class="dv">3</span></span>
<span id="cb4-16"><a href="#cb4-16" aria-hidden="true" tabindex="-1"></a><span class="fu">eval_batch_size</span><span class="kw">:</span><span class="at"> </span><span class="dv">2</span></span>
<span id="cb4-17"><a href="#cb4-17" aria-hidden="true" tabindex="-1"></a><span class="fu">num_epochs</span><span class="kw">:</span><span class="at"> </span><span class="dv">1</span></span>
<span id="cb4-18"><a href="#cb4-18" aria-hidden="true" tabindex="-1"></a><span class="fu">learning_rate</span><span class="kw">:</span><span class="at"> </span><span class="fl">0.0002</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><span class="fu">train_on_inputs</span><span class="kw">:</span><span class="at"> </span><span class="ch">false</span></span>
<span id="cb4-21"><a href="#cb4-21" aria-hidden="true" tabindex="-1"></a><span class="fu">special_tokens</span><span class="kw">:</span></span>
<span id="cb4-22"><a href="#cb4-22" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">bos_token</span><span class="kw">:</span><span class="at"> </span><span class="st">"&lt;s&gt;"</span></span>
<span id="cb4-23"><a href="#cb4-23" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">eos_token</span><span class="kw">:</span><span class="at"> </span><span class="st">"&lt;/s&gt;"</span></span>
<span id="cb4-24"><a href="#cb4-24" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">unk_token</span><span class="kw">:</span><span class="at"> </span><span class="st">"&lt;unk&gt;"</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p>You can use the following command to materialize your data. The <code>--debug</code> flag will print the tokens, along with the labels so you can verify that the correct items are being ignored:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="ex">$</span> python <span class="at">-m</span> axolotl.cli.preprocess training_config.yaml <span class="at">--debug</span></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><span class="ex">...</span></span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a><span class="ex">[2024-03-05</span> 23:36:46,969] <span class="pp">[</span><span class="ss">INFO</span><span class="pp">]</span> <span class="pp">[</span><span class="ss">axolotl.check_example_labels:35</span><span class="pp">]</span> <span class="pp">[</span><span class="ss">PID:607731</span><span class="pp">]</span> <span class="pp">[</span><span class="ss">RANK:0</span><span class="pp">]</span> <span class="op">&lt;</span>s<span class="op">&gt;(</span><span class="ex">1,</span> 1<span class="op">)</span> Hello<span class="er">(</span><span class="ex">22557,</span> 22557<span class="kw">)</span></span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a><span class="kw">(</span><span class="ex">13,</span> 13<span class="kw">)</span> <span class="ex">hi</span><span class="er">(</span><span class="ex">12014,</span> 12014<span class="kw">)</span> <span class="ex">there</span><span class="er">(</span><span class="ex">736,</span> 736<span class="kw">)</span> <span class="ex">!</span><span class="er">(</span><span class="ex">28808,</span> 28808<span class="kw">)</span> <span class="bu">.</span><span class="er">(</span><span class="ex">28723,</span> 28723<span class="kw">)</span> <span class="kw">(</span><span class="ex">28705,</span> 28705<span class="kw">)</span> <span class="ex">good</span><span class="er">(</span><span class="ex">-100,</span> 1179<span class="kw">)</span> <span class="ex">bye</span><span class="er">(</span><span class="ex">-100,</span> 17664<span class="kw">)</span> <span class="kw">(</span><span class="ex">-100,</span> 28705<span class="kw">)</span> <span class="ex">fare</span><span class="er">(</span><span class="ex">19111,</span> 19111<span class="kw">)</span> <span class="ex">well</span><span class="er">(</span><span class="ex">5458,</span> 5458<span class="kw">)</span> <span class="op">&lt;</span>/s<span class="op">&gt;(</span><span class="ex">2,</span> 2<span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p>The format is <code>decoded_token</code>(<code>label</code>, <code>token_id</code>), for example, <code>&lt;s&gt;(1, 1)</code> means that the token is <code>&lt;s&gt;</code>, the label is <code>1</code> and the token_id is <code>1</code>. When the label is <code>-100</code> then that token is ignored for training.</p>
<p><a id="markdown-3-check-the-prompts" name="3-check-the-prompts"></a></p>
</section>
<section id="check-the-prompts" class="level3">
<h3 class="anchored" data-anchor-id="check-the-prompts">3. Check the prompts</h3>
<p>Here is another way to check the materialized output:</p>
<div class="sourceCode" 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> transformers <span class="im">import</span> AutoTokenizer</span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> datasets <span class="im">import</span> load_from_disk</span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> yaml</span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>directory <span class="op">=</span> <span class="op">!</span>ls last_run_prepared<span class="op">/</span></span>
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> <span class="bu">open</span>(<span class="st">'training_config.yaml'</span>, <span class="st">'r'</span>) <span class="im">as</span> f:</span>
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a> cfg <span class="op">=</span> yaml.safe_load(f)</span>
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a>model_id <span class="op">=</span> cfg[<span class="st">'base_model'</span>]</span>
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a>tok <span class="op">=</span> AutoTokenizer.from_pretrained(model_id)</span>
<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a>ds <span class="op">=</span> load_from_disk(<span class="ss">f'last_run_prepared/</span><span class="sc">{</span>directory[<span class="dv">0</span>]<span class="sc">}</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="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="op">&gt;&gt;&gt;</span> row <span class="op">=</span> ds[<span class="dv">0</span>]</span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="op">&gt;&gt;&gt;</span> <span class="bu">print</span>(tok.decode(row[<span class="st">'input_ids'</span>]))</span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="op">&lt;</span>s<span class="op">&gt;</span> Hello</span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a> hi there<span class="op">!</span>. goodbye farewell<span class="op">&lt;/</span>s<span class="op">&gt;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p>We can check that the right tokens are ingored by comparing the labels to each token:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>pd.DataFrame([{<span class="st">'token'</span>: tok.decode(i), <span class="st">'label'</span>: l, <span class="st">'id'</span>:i} <span class="cf">for</span> i,l <span class="kw">in</span></span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a> <span class="bu">zip</span>(row[<span class="st">'input_ids'</span>], row[<span class="st">'labels'</span>])])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<table class="table">
<thead>
<tr class="header">
<th>token</th>
<th>label</th>
<th>id</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>0</td>
<td>&lt;s&gt;</td>
<td>1</td>
</tr>
<tr class="even">
<td>1</td>
<td>Hello</td>
<td>22557</td>
</tr>
<tr class="odd">
<td>2</td>
<td>\n</td>
<td>13</td>
</tr>
<tr class="even">
<td>3</td>
<td>hi</td>
<td>12014</td>
</tr>
<tr class="odd">
<td>4</td>
<td>there</td>
<td>736</td>
</tr>
<tr class="even">
<td>5</td>
<td>!</td>
<td>28808</td>
</tr>
<tr class="odd">
<td>6</td>
<td>.</td>
<td>28723</td>
</tr>
<tr class="even">
<td>7</td>
<td></td>
<td>28705</td>
</tr>
<tr class="odd">
<td>8</td>
<td>good</td>
<td>-100</td>
</tr>
<tr class="even">
<td>9</td>
<td>bye</td>
<td>-100</td>
</tr>
<tr class="odd">
<td>10</td>
<td></td>
<td>-100</td>
</tr>
<tr class="even">
<td>11</td>
<td>fare</td>
<td>19111</td>
</tr>
<tr class="odd">
<td>12</td>
<td>well</td>
<td>5458</td>
</tr>
<tr class="even">
<td>13</td>
<td>&lt;/s&gt;</td>
<td>2</td>
</tr>
</tbody>
</table>
<p>If we look at the input data, the above table seems correct! (The jsonl version is repeated below for reference):</p>
<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">$</span> head <span class="at">-n1</span> output.jsonl <span class="kw">|</span> <span class="ex">python</span> <span class="at">-m</span> json.tool</span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a><span class="ex">{.cell-output</span> .cell-output-stdout}</span>
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a> <span class="kw">{</span></span>
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a> <span class="st">"segments"</span><span class="ex">:</span> [</span>
<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a> <span class="kw">{</span></span>
<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a> <span class="st">"label"</span><span class="ex">:</span> true,</span>
<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a> <span class="st">"text"</span><span class="ex">:</span> <span class="st">"&lt;s&gt;Hello\n"</span></span>
<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a> <span class="ex">},</span></span>
<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a> <span class="kw">{</span></span>
<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a> <span class="st">"label"</span><span class="ex">:</span> true,</span>
<span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a> <span class="st">"text"</span><span class="ex">:</span> <span class="st">"hi there!. "</span></span>
<span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a> <span class="ex">},</span></span>
<span id="cb9-14"><a href="#cb9-14" aria-hidden="true" tabindex="-1"></a> <span class="kw">{</span></span>
<span id="cb9-15"><a href="#cb9-15" aria-hidden="true" tabindex="-1"></a> <span class="st">"label"</span><span class="ex">:</span> false,</span>
<span id="cb9-16"><a href="#cb9-16" aria-hidden="true" tabindex="-1"></a> <span class="st">"text"</span><span class="ex">:</span> <span class="st">"goodbye "</span></span>
<span id="cb9-17"><a href="#cb9-17" aria-hidden="true" tabindex="-1"></a> <span class="ex">},</span></span>
<span id="cb9-18"><a href="#cb9-18" aria-hidden="true" tabindex="-1"></a> <span class="kw">{</span></span>
<span id="cb9-19"><a href="#cb9-19" aria-hidden="true" tabindex="-1"></a> <span class="st">"label"</span><span class="ex">:</span> true,</span>
<span id="cb9-20"><a href="#cb9-20" aria-hidden="true" tabindex="-1"></a> <span class="st">"text"</span><span class="ex">:</span> <span class="st">"farewell&lt;/s&gt;"</span></span>
<span id="cb9-21"><a href="#cb9-21" aria-hidden="true" tabindex="-1"></a> <span class="kw">}</span></span>
<span id="cb9-22"><a href="#cb9-22" aria-hidden="true" tabindex="-1"></a> <span class="ex">]</span></span>
<span id="cb9-23"><a href="#cb9-23" aria-hidden="true" tabindex="-1"></a> <span class="kw">}</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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Mac M-series support
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<p>Currently Axolotl on Mac is partially usable, many of the dependencies of Axolotl including Pytorch do not support MPS or have incomplete support.</p>
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How to use Axolotl on multiple machines
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<p>You will need to create a configuration for accelerate, either by using <code>accelerate config</code> and follow the instructions or you can use one of the preset below:</p>
<p>~/.cache/huggingface/accelerate/default_config.yaml</p>
<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">compute_environment</span><span class="kw">:</span><span class="at"> LOCAL_MACHINE</span></span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">debug</span><span class="kw">:</span><span class="at"> </span><span class="ch">false</span></span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">distributed_type</span><span class="kw">:</span><span class="at"> FSDP</span></span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">downcast_bf16</span><span class="kw">:</span><span class="at"> </span><span class="st">'no'</span></span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu">machine_rank</span><span class="kw">:</span><span class="at"> </span><span class="dv">0</span><span class="co"> # Set to 0 for the main machine, increment by one for other machines</span></span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="fu">main_process_ip</span><span class="kw">:</span><span class="at"> </span><span class="fl">10.0.0.4</span><span class="co"> # Set to main machine's IP</span></span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="fu">main_process_port</span><span class="kw">:</span><span class="at"> </span><span class="dv">5000</span></span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="fu">main_training_function</span><span class="kw">:</span><span class="at"> main</span></span>
<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="fu">mixed_precision</span><span class="kw">:</span><span class="at"> bf16</span></span>
<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="fu">num_machines</span><span class="kw">:</span><span class="at"> </span><span class="dv">2</span><span class="co"> # Change to the number of machines</span></span>
<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="fu">num_processes</span><span class="kw">:</span><span class="at"> </span><span class="dv">4</span><span class="co"> # That's the total number of GPUs, (for example: if you have 2 machines with 4 GPU, put 8)</span></span>
<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="fu">rdzv_backend</span><span class="kw">:</span><span class="at"> static</span></span>
<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="fu">same_network</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a><span class="fu">tpu_env</span><span class="kw">:</span><span class="at"> </span><span class="kw">[]</span></span>
<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a><span class="fu">tpu_use_cluster</span><span class="kw">:</span><span class="at"> </span><span class="ch">false</span></span>
<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a><span class="fu">tpu_use_sudo</span><span class="kw">:</span><span class="at"> </span><span class="ch">false</span></span>
<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a><span class="fu">use_cpu</span><span class="kw">:</span><span class="at"> </span><span class="ch">false</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p>Configure your model to use FSDP with for example:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">fsdp</span><span class="kw">:</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> full_shard</span></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> auto_wrap</span></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="fu">fsdp_config</span><span class="kw">:</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">fsdp_offload_params</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">fsdp_state_dict_type</span><span class="kw">:</span><span class="at"> FULL_STATE_DICT</span></span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">fsdp_transformer_layer_cls_to_wrap</span><span class="kw">:</span><span class="at"> LlamaDecoderLayer</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<section id="machine-configuration" class="level2">
<h2 class="anchored" data-anchor-id="machine-configuration">Machine configuration</h2>
<p>On each machine you need a copy of Axolotl, we suggest using the same commit to ensure compatibility.</p>
<p>You will also need to have the same configuration file for your model on each machine.</p>
<p>On the main machine only, make sure the port you set as <code>main_process_port</code> is open in TCP and reachable by other machines.</p>
<p>All you have to do now is launch using accelerate as you would usually do on each machine and voila, the processes will start once you have launched accelerate on every machine.</p>
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Multipack is a technique to pack multiple sequences into a single batch to increase training throughput.
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<h2 class="anchored" data-anchor-id="visualization-of-multipack-with-flash-attention">Visualization of Multipack with Flash Attention</h2>
<p>Because Flash Attention simply drops the attention mask, we do not need to construct a 4d attention mask. We only need to concatenate the sequences into a single batch and let flash attention know where each new sequence begins.</p>
<p>4k context, bsz =4, each character represents 256 tokens X represents a padding token</p>
<pre><code> 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
[[ A A A A A A A A A A A ]
B B B B B B ]
C C C C C C C ]
D D D D ]]
[[ E E E E E E E E ]
[ F F F F ]
[ G G G ]
[ H H H H ]]
[[ I I I ]
[ J J J ]
[ K K K K K]
[ L L L ]]</code></pre>
<p>after padding to longest input in each step</p>
<pre><code> 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
[[ A A A A A A A A A A A ]
B B B B B B X X X X X X ]
C C C C C C C X X X X ]
D D D D X X X X X X X ]]
[[ E E E E E E E E ]
[ F F F F X X X X ]
[ G G G X X X X X ]
[ H H H H X X X X ]]
[[ I I I X X ]
[ J J J X X ]
[ K K K K K ]
[ L L L X X ]]</code></pre>
<p>w packing ( note its the same effective number of tokens per step, but a true bsz of 1)</p>
<pre><code> 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
[[ A A A A A A A A A A A B B B B B
B C C C C C C C D D D D E E E E
E E E E F F F F F G G G H H H H
I I I J J J J K K K K K L L L X ]]</code></pre>
<p>cu_seqlens: [[ 0, 11, 17, 24, 28, 36, 41 44, 48, 51, 55, 60, 64]]</p>
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<h2 class="anchored" data-anchor-id="multipack-without-flash-attention">Multipack without Flash Attention</h2>
<p>Multipack can still be achieved without Flash attention, but with lower packing efficiency as we are not able to join multiple batches into a single batch due to context length limits without flash attention. We can use either Pytorchs Scaled Dot Product Attention implementation or native Pytorch attention implementation along with <a href="https://github.com/huggingface/transformers/pull/27539">4d attention masks</a> to pack sequences together and avoid cross attention.</p>
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Troubleshooting NCCL issues
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<p>NVIDIA NCCL is a library to facilitate and optimize multi-GPU communication operations, such as broadcast, all-gather, reduce, all-reduce, etc. Broadly, NCCL configuration is highly environment-specific and is configured via several <a href="https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/env.html">environment variables</a>. A common NCCL-related problem occurs when a long-running operation times out causing the training process to abort:</p>
<pre class="text"><code>Watchdog caught collective operation timeout: WorkNCCL(SeqNum=42, OpType=ALLGATHER, Timeout(ms)=1800000) ran for 1806948 milliseconds before timing out.</code></pre>
<p>Often, this timeout will happen after 30 minutes (the default setting) and is accompanied by below-average power consumption with near 100% GPU utilization before the error is raised. Nvidia recommends <a href="https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/troubleshooting.html#pci-access-control-services-acs">disabling PCI access control services (ACS)</a> as a possible solution if this is available to you.</p>
<p>Forcing cross-GPU communication via <a href="https://en.wikipedia.org/wiki/NVLink">NVLink</a> may help without increasing timeouts. To verify that your configuration is leveraging NVLink run the following command:</p>
<pre class="shell"><code>nvidia-smi nvlink --status</code></pre>
<p>To force NCCL to use NVLink, simply set this in the environment:</p>
<pre class="shell"><code>export NCCL_P2P_LEVEL=NVL</code></pre>
<p>If NVLink is not available in your environment there are other options for <code>NCCL_P2P_LEVEL</code> in the table below:</p>
<table class="table">
<colgroup>
<col style="width: 56%">
<col style="width: 44%">
</colgroup>
<thead>
<tr class="header">
<th>NCCL_P2P_LEVEL</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>PIX</td>
<td>P2P data transfers through no more than a single PCIe bridge. Faster data transfer rates vs to paths involving multiple bridges, but slower compared to direct GPU-to-GPU communication.</td>
</tr>
<tr class="even">
<td>PXB</td>
<td>P2P data transfers through multiple PCIe bridges but not going through the PCIe Host Bridge; this path involves a complex routing process, potentially incurring a moderate level of latency.</td>
</tr>
<tr class="odd">
<td>PHB</td>
<td>P2P data transfers occur over the PCIe and through a PCIe Host Bridge, typically involving the CPU, which can facilitate direct memory access but might introduce additional latency compared to more direct paths (ex PIX, NVL)</td>
</tr>
</tbody>
</table>
<p>To validate that acceptable data transfer speeds exist for your training job, running <a href="https://github.com/NVIDIA/nccl-tests/blob/master/README.md">NCCL Tests</a> can help pinpoint bottlenecks, for example:</p>
<pre class="shell"><code>./build/all_reduce_perf -b 8 -e 128M -f 2 -g 3</code></pre>
<p>It can be useful when debugging NCCL communication timeouts to activate additional logging in both PyTorch and NCCL:</p>
<pre class="shell"><code>export NCCL_DEBUG=INFO
export NCCL_DEBUG_SUBSYS=ALL
export TORCH_DISTRIBUTED_DEBUG=INFO
export TORCHELASTIC_ERROR_FILE=/PATH/TO/torcherror.log</code></pre>
<p>Finally, if you believe your training job needs more time you can increase the timeout past 30 minutes by setting the <code>ddp_timeout</code> value in the Axolotl configuration. See <a href="https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group">PyTorch init_process_group</a> for documentation on this value.</p>
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Reinforcement Learning from Human Feedback is a method whereby a language model is optimized from data using human feedback.
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<section id="overview" class="level3">
<h3 class="anchored" data-anchor-id="overview">Overview</h3>
<p>Reinforcement Learning from Human Feedback is a method whereby a language model is optimized from data using human feedback. Various methods include, but not limited to:</p>
<ul>
<li>Proximal Policy Optimization (PPO) (not yet supported in axolotl)</li>
<li>Direct Preference Optimization (DPO)</li>
<li>Identity Preference Optimization (IPO)</li>
</ul>
</section>
<section id="rlhf-using-axolotl" class="level3">
<h3 class="anchored" data-anchor-id="rlhf-using-axolotl">RLHF using Axolotl</h3>
<blockquote class="blockquote">
<p>[!IMPORTANT] This is a BETA feature and many features are not fully implemented. You are encouraged to open new PRs to improve the integration and functionality.</p>
</blockquote>
<p>The various RL training methods are implemented in trl and wrapped via axolotl. Below are various examples with how you can use various preference datasets to train models that use ChatML</p>
<section id="dpo" class="level4">
<h4 class="anchored" data-anchor-id="dpo">DPO</h4>
<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">rl</span><span class="kw">:</span><span class="at"> dpo</span></span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">datasets</span><span class="kw">:</span></span>
<span id="cb1-3"><a href="#cb1-3" 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"> Intel/orca_dpo_pairs</span></span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">split</span><span class="kw">:</span><span class="at"> train</span></span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">type</span><span class="kw">:</span><span class="at"> chatml.intel</span></span>
<span id="cb1-6"><a href="#cb1-6" 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"> argilla/ultrafeedback-binarized-preferences</span></span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">split</span><span class="kw">:</span><span class="at"> train</span></span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">type</span><span class="kw">:</span><span class="at"> chatml.argilla</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</section>
<section id="ipo" class="level4">
<h4 class="anchored" data-anchor-id="ipo">IPO</h4>
<div class="sourceCode" id="cb2"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">rl</span><span class="kw">:</span><span class="at"> ipo</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</section>
<section id="orpo" class="level4">
<h4 class="anchored" data-anchor-id="orpo">ORPO</h4>
<p>Paper: https://arxiv.org/abs/2403.07691</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">rl</span><span class="kw">:</span><span class="at"> orpo</span></span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="fu">orpo_alpha</span><span class="kw">:</span><span class="at"> </span><span class="fl">0.1</span></span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="fu">remove_unused_columns</span><span class="kw">:</span><span class="at"> </span><span class="ch">false</span></span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a><span class="fu">chat_template</span><span class="kw">:</span><span class="at"> chatml</span></span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a><span class="fu">datasets</span><span class="kw">:</span></span>
<span id="cb3-7"><a href="#cb3-7" 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"> argilla/ultrafeedback-binarized-preferences-cleaned</span></span>
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">type</span><span class="kw">:</span><span class="at"> orpo.chat_template</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</section>
<section id="using-local-dataset-files" class="level4">
<h4 class="anchored" data-anchor-id="using-local-dataset-files">Using local dataset files</h4>
<div class="sourceCode" id="cb4"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">datasets</span><span class="kw">:</span></span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> </span><span class="fu">ds_type</span><span class="kw">:</span><span class="at"> json</span></span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">data_files</span><span class="kw">:</span></span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="kw">-</span><span class="at"> orca_rlhf.jsonl</span></span>
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">split</span><span class="kw">:</span><span class="at"> train</span></span>
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">type</span><span class="kw">:</span><span class="at"> chatml.intel</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</section>
<section id="trl-autounwrap-for-peft" class="level4">
<h4 class="anchored" data-anchor-id="trl-autounwrap-for-peft">Trl autounwrap for peft</h4>
<p>Trl supports autounwrapping peft models, so that a ref model does not need to be additionally loaded, leading to less VRAM needed. This is on by default. To turn it off, pass the following config.</p>
<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="co"># load ref model when adapter training.</span></span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="fu">rl_adapter_ref_model</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>
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/* css styles */