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merge","isExpanded":true,"id":"package_reference/merge_utils","url":"/docs/peft/package_reference/merge_utils"},{"title":"Helpers","isExpanded":true,"id":"package_reference/helpers","url":"/docs/peft/package_reference/helpers"}]}]}],"chapterId":"quicktour","docType":"docs","isLoggedIn":false,"lang":"en","langs":["en"],"library":"peft","theme":"light","version":"v0.13.0","versions":[{"version":"main"},{"version":"v0.13.0"},{"version":"v0.12.0"},{"version":"v0.11.0"},{"version":"v0.10.0"},{"version":"v0.9.0"},{"version":"v0.8.2"},{"version":"v0.8.1"},{"version":"v0.8.0"},{"version":"v0.7.1"},{"version":"v0.7.0"},{"version":"v0.6.2"},{"version":"v0.6.1"},{"version":"v0.6.0"}],"title":"Quicktour"}"> <div class="z-2 w-full flex-none lg:flex lg:h-dvh lg:w-[270px] lg:flex-col 2xl:w-[300px] false"><div class="shadow-alternate flex h-auto w-full items-center rounded-b-xl border-b bg-white py-2 text-lg leading-tight lg:hidden"> <div class="flex flex-1 cursor-pointer flex-col justify-center 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dark:hover:text-gray-300 ml-2" href="/docs/peft/index" id="index">🤗 PEFT </a><a class="rounded-xl bg-gradient-to-br from-black to-gray-900 py-1 pl-2 pr-2 text-white first:mt-1 last:mb-4 dark:from-gray-800 dark:to-gray-900 ml-2" href="/docs/peft/quicktour" id="quicktour">Quicktour </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/docs/peft/install" id="install">Installation </a> </div> <div class="group flex cursor-pointer items-center pl-2 text-[0.8rem] font-semibold uppercase leading-9 hover:text-gray-700 dark:hover:text-gray-300 ml-0"><div class="flex after:absolute after:right-4 after:text-gray-500 group-hover:after:content-['▶'] after:rotate-90 after:transform"><span><span class="inline-block space-x-1 leading-5"><span>Tutorial</span> </span></span> </div></div> <div class="flex flex-col"><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px 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href="/docs/peft/task_guides/prompt_based_methods" id="task_guides/prompt_based_methods">Prompt-based methods </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/docs/peft/task_guides/lora_based_methods" id="task_guides/lora_based_methods">LoRA methods </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/docs/peft/task_guides/ia3" id="task_guides/ia3">IA3 </a> </div> <div class="group flex cursor-pointer items-center pl-2 text-[0.8rem] font-semibold uppercase leading-9 hover:text-gray-700 dark:hover:text-gray-300 ml-0"><div class="flex after:absolute after:right-4 after:text-gray-500 group-hover:after:content-['▶'] after:rotate-90 after:transform"><span><span class="inline-block space-x-1 leading-5"><span>Developer guides</span> </span></span> </div></div> <div class="flex flex-col"><a 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last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/docs/peft/developer_guides/low_level_api" id="developer_guides/low_level_api">Adapter injection </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/docs/peft/developer_guides/mixed_models" id="developer_guides/mixed_models">Mixed adapter types </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/docs/peft/developer_guides/torch_compile" id="developer_guides/torch_compile">torch.compile </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/docs/peft/developer_guides/contributing" id="developer_guides/contributing">Contribute to PEFT </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 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ml-2" href="/docs/peft/accelerate/deepspeed" id="accelerate/deepspeed">DeepSpeed </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/docs/peft/accelerate/fsdp" id="accelerate/fsdp">Fully Sharded Data Parallel </a> </div> <div class="group flex cursor-pointer items-center pl-2 text-[0.8rem] font-semibold uppercase leading-9 hover:text-gray-700 dark:hover:text-gray-300 ml-0"><div class="flex after:absolute after:right-4 after:text-gray-500 group-hover:after:content-['▶'] after:rotate-90 after:transform"><span><span class="inline-block space-x-1 leading-5"><span>Conceptual guides</span> </span></span> </div></div> <div class="flex flex-col"><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/docs/peft/conceptual_guides/adapter" id="conceptual_guides/adapter">Adapters </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/docs/peft/conceptual_guides/prompting" id="conceptual_guides/prompting">Soft prompts </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/docs/peft/conceptual_guides/ia3" id="conceptual_guides/ia3">IA3 </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/docs/peft/conceptual_guides/oft" id="conceptual_guides/oft">OFT/BOFT </a> </div> <div class="group flex cursor-pointer items-center pl-2 text-[0.8rem] font-semibold uppercase leading-9 hover:text-gray-700 dark:hover:text-gray-300 ml-0"><div class="flex after:absolute after:right-4 after:text-gray-500 group-hover:after:content-['▶'] after:rotate-90 after:transform"><span><span class="inline-block space-x-1 leading-5"><span>API reference</span> </span></span> </div></div> <div class="flex flex-col"> <div class="group flex cursor-pointer items-center pl-2 text-[0.8rem] font-semibold uppercase leading-9 hover:text-gray-700 dark:hover:text-gray-300 ml-2"><div class="flex after:absolute after:right-4 after:text-gray-500 group-hover:after:content-['▶'] after:rotate-90 after:transform"><span><span class="inline-block space-x-1 leading-5"><span>Main classes</span> </span></span> </div></div> <div class="flex flex-col"><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/auto_class" id="package_reference/auto_class">AutoPeftModel </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/peft_model" id="package_reference/peft_model">PEFT model </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/peft_types" id="package_reference/peft_types">PEFT types </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/config" id="package_reference/config">Configuration </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/tuners" id="package_reference/tuners">Tuner </a> </div> <div class="group flex cursor-pointer items-center pl-2 text-[0.8rem] font-semibold uppercase leading-9 hover:text-gray-700 dark:hover:text-gray-300 ml-2"><div class="flex after:absolute after:right-4 after:text-gray-500 group-hover:after:content-['▶'] after:rotate-90 after:transform"><span><span class="inline-block space-x-1 leading-5"><span>Adapters</span> </span></span> </div></div> <div class="flex flex-col"><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/adalora" id="package_reference/adalora">AdaLoRA </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/ia3" id="package_reference/ia3">IA3 </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/llama_adapter" id="package_reference/llama_adapter">Llama-Adapter </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/loha" id="package_reference/loha">LoHa </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/lokr" id="package_reference/lokr">LoKr </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/lora" id="package_reference/lora">LoRA </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/xlora" id="package_reference/xlora">X-LoRA </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/adapter_utils" id="package_reference/adapter_utils">LyCORIS </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/multitask_prompt_tuning" id="package_reference/multitask_prompt_tuning">Multitask Prompt Tuning </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/oft" id="package_reference/oft">OFT </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/boft" id="package_reference/boft">BOFT </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/poly" id="package_reference/poly">Polytropon </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/p_tuning" id="package_reference/p_tuning">P-tuning </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/prefix_tuning" id="package_reference/prefix_tuning">Prefix tuning </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/prompt_tuning" id="package_reference/prompt_tuning">Prompt tuning </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/layernorm_tuning" id="package_reference/layernorm_tuning">Layernorm tuning </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/vera" id="package_reference/vera">VeRA </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/fourierft" id="package_reference/fourierft">FourierFT </a><a class="transform py-1 pl-2 pr-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-4" href="/docs/peft/package_reference/vblora" id="package_reference/vblora">VB-LoRA </a> </div> <div class="group flex cursor-pointer items-center pl-2 text-[0.8rem] font-semibold uppercase leading-9 hover:text-gray-700 dark:hover:text-gray-300 ml-2"><div class="flex after:absolute after:right-4 after:text-gray-500 group-hover:after:content-['▶'] after:rotate-90 after:transform"><span><span class="inline-block space-x-1 leading-5"><span>Utilities</span> </span></span> </div></div> <div class="flex flex-col"><a class="transform py-1 pl-2 pr-2 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relative mx-auto max-w-4xl break-words"> <link href="/docs/peft/v0.13.0/en/_app/immutable/assets/0.e3b0c442.css" rel="modulepreload"> <link rel="modulepreload" href="/docs/peft/v0.13.0/en/_app/immutable/entry/start.c3c2c93b.js"> <link rel="modulepreload" href="/docs/peft/v0.13.0/en/_app/immutable/chunks/scheduler.c57aa7ef.js"> <link rel="modulepreload" href="/docs/peft/v0.13.0/en/_app/immutable/chunks/singletons.b80004a5.js"> <link rel="modulepreload" href="/docs/peft/v0.13.0/en/_app/immutable/chunks/index.c4ac7298.js"> <link rel="modulepreload" href="/docs/peft/v0.13.0/en/_app/immutable/chunks/paths.dbbd692a.js"> <link rel="modulepreload" href="/docs/peft/v0.13.0/en/_app/immutable/entry/app.c13afa6b.js"> <link rel="modulepreload" href="/docs/peft/v0.13.0/en/_app/immutable/chunks/index.c50cb18e.js"> <link rel="modulepreload" href="/docs/peft/v0.13.0/en/_app/immutable/nodes/0.cbd3b982.js"> <link rel="modulepreload" href="/docs/peft/v0.13.0/en/_app/immutable/chunks/each.e59479a4.js"> <link rel="modulepreload" href="/docs/peft/v0.13.0/en/_app/immutable/nodes/46.385c32b6.js"> <link rel="modulepreload" href="/docs/peft/v0.13.0/en/_app/immutable/chunks/Tip.9268b0ca.js"> <link rel="modulepreload" href="/docs/peft/v0.13.0/en/_app/immutable/chunks/CodeBlock.34f0a53d.js"> <link rel="modulepreload" href="/docs/peft/v0.13.0/en/_app/immutable/chunks/EditOnGithub.958a8a49.js"><meta name="hf:doc:metadata" content="{"title":"Quicktour","local":"quicktour","sections":[{"title":"Train","local":"train","sections":[{"title":"Save model","local":"save-model","sections":[],"depth":3}],"depth":2},{"title":"Inference","local":"inference","sections":[],"depth":2},{"title":"Next steps","local":"next-steps","sections":[],"depth":2}],"depth":1}"> <p></p> <h1 class="relative group"><a id="quicktour" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#quicktour"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Quicktour</span></h1> <p data-svelte-h="svelte-hgsptz">PEFT offers parameter-efficient methods for finetuning large pretrained models. The traditional paradigm is to finetune all of a model’s parameters for each downstream task, but this is becoming exceedingly costly and impractical because of the enormous number of parameters in models today. Instead, it is more efficient to train a smaller number of prompt parameters or use a reparametrization method like low-rank adaptation (LoRA) to reduce the number of trainable parameters.</p> <p data-svelte-h="svelte-1t6b3ei">This quicktour will show you PEFT’s main features and how you can train or run inference on large models that would typically be inaccessible on consumer devices.</p> <h2 class="relative group"><a id="train" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#train"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Train</span></h2> <p data-svelte-h="svelte-1n4wot8">Each PEFT method is defined by a <a href="/docs/peft/v0.13.0/en/package_reference/config#peft.PeftConfig">PeftConfig</a> class that stores all the important parameters for building a <a href="/docs/peft/v0.13.0/en/package_reference/peft_model#peft.PeftModel">PeftModel</a>. For example, to train with LoRA, load and create a <a href="/docs/peft/v0.13.0/en/package_reference/lora#peft.LoraConfig">LoraConfig</a> class and specify the following parameters:</p> <ul data-svelte-h="svelte-kw75sh"><li><code>task_type</code>: the task to train for (sequence-to-sequence language modeling in this case)</li> <li><code>inference_mode</code>: whether you’re using the model for inference or not</li> <li><code>r</code>: the dimension of the low-rank matrices</li> <li><code>lora_alpha</code>: the scaling factor for the low-rank matrices</li> <li><code>lora_dropout</code>: the dropout probability of the LoRA layers</li></ul> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><span class="hljs-keyword">from</span> peft <span class="hljs-keyword">import</span> LoraConfig, TaskType peft_config = LoraConfig(task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=<span class="hljs-literal">False</span>, r=<span class="hljs-number">8</span>, lora_alpha=<span class="hljs-number">32</span>, lora_dropout=<span class="hljs-number">0.1</span>)</pre></div> <div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p data-svelte-h="svelte-1srntze">See the <a href="/docs/peft/v0.13.0/en/package_reference/lora#peft.LoraConfig">LoraConfig</a> reference for more details about other parameters you can adjust, such as the modules to target or the bias type.</p></div> <p data-svelte-h="svelte-wg55ww">Once the <a href="/docs/peft/v0.13.0/en/package_reference/lora#peft.LoraConfig">LoraConfig</a> is setup, create a <a href="/docs/peft/v0.13.0/en/package_reference/peft_model#peft.PeftModel">PeftModel</a> with the <a href="/docs/peft/v0.13.0/en/package_reference/peft_model#peft.get_peft_model">get_peft_model()</a> function. It takes a base model - which you can load from the Transformers library - and the <a href="/docs/peft/v0.13.0/en/package_reference/lora#peft.LoraConfig">LoraConfig</a> containing the parameters for how to configure a model for training with LoRA.</p> <p data-svelte-h="svelte-toa5ud">Load the base model you want to finetune.</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"bigscience/mt0-large"</span>)</pre></div> <p data-svelte-h="svelte-1xhlky3">Wrap the base model and <code>peft_config</code> with the <a href="/docs/peft/v0.13.0/en/package_reference/peft_model#peft.get_peft_model">get_peft_model()</a> function to create a <a href="/docs/peft/v0.13.0/en/package_reference/peft_model#peft.PeftModel">PeftModel</a>. To get a sense of the number of trainable parameters in your model, use the <code>print_trainable_parameters</code> method.</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><span class="hljs-keyword">from</span> peft <span class="hljs-keyword">import</span> get_peft_model model = get_peft_model(model, peft_config) model.print_trainable_parameters() <span class="hljs-string">"output: trainable params: 2359296 || all params: 1231940608 || trainable%: 0.19151053100118282"</span></pre></div> <p data-svelte-h="svelte-y8cetx">Out of <a href="https://huggingface.co/bigscience/mt0-large" rel="nofollow">bigscience/mt0-large’s</a> 1.2B parameters, you’re only training 0.19% of them!</p> <p data-svelte-h="svelte-1554878">That is it 🎉! Now you can train the model with the Transformers <a href="https://huggingface.co/docs/transformers/v4.44.2/en/main_classes/trainer#transformers.Trainer" rel="nofollow">Trainer</a>, Accelerate, or any custom PyTorch training loop.</p> <p data-svelte-h="svelte-smig4h">For example, to train with the <a href="https://huggingface.co/docs/transformers/v4.44.2/en/main_classes/trainer#transformers.Trainer" rel="nofollow">Trainer</a> class, setup a <a href="https://huggingface.co/docs/transformers/v4.44.2/en/main_classes/trainer#transformers.TrainingArguments" rel="nofollow">TrainingArguments</a> class with some training hyperparameters.</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="">training_args = TrainingArguments( output_dir=<span class="hljs-string">"your-name/bigscience/mt0-large-lora"</span>, learning_rate=<span class="hljs-number">1e-3</span>, per_device_train_batch_size=<span class="hljs-number">32</span>, per_device_eval_batch_size=<span class="hljs-number">32</span>, num_train_epochs=<span class="hljs-number">2</span>, weight_decay=<span class="hljs-number">0.01</span>, eval_strategy=<span class="hljs-string">"epoch"</span>, save_strategy=<span class="hljs-string">"epoch"</span>, load_best_model_at_end=<span class="hljs-literal">True</span>, )</pre></div> <p data-svelte-h="svelte-1jara0a">Pass the model, training arguments, dataset, tokenizer, and any other necessary component to the <a href="https://huggingface.co/docs/transformers/v4.44.2/en/main_classes/trainer#transformers.Trainer" rel="nofollow">Trainer</a>, and call <a href="https://huggingface.co/docs/transformers/v4.44.2/en/main_classes/trainer#transformers.Trainer.train" rel="nofollow">train</a> to start training.</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="">trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_datasets[<span class="hljs-string">"train"</span>], eval_dataset=tokenized_datasets[<span class="hljs-string">"test"</span>], tokenizer=tokenizer, data_collator=data_collator, compute_metrics=compute_metrics, ) trainer.train()</pre></div> <h3 class="relative group"><a id="save-model" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#save-model"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Save model</span></h3> <p data-svelte-h="svelte-okvo85">After your model is finished training, you can save your model to a directory using the <a href="https://huggingface.co/docs/transformers/v4.44.2/en/main_classes/model#transformers.PreTrainedModel.save_pretrained" rel="nofollow">save_pretrained</a> function.</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="">model.save_pretrained(<span class="hljs-string">"output_dir"</span>)</pre></div> <p data-svelte-h="svelte-c8xvod">You can also save your model to the Hub (make sure you’re logged in to your Hugging Face account first) with the <a href="https://huggingface.co/docs/transformers/v4.44.2/en/main_classes/model#transformers.PreTrainedModel.push_to_hub" rel="nofollow">push_to_hub</a> function.</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login notebook_login() model.push_to_hub(<span class="hljs-string">"your-name/bigscience/mt0-large-lora"</span>)</pre></div> <p data-svelte-h="svelte-lvoeik">Both methods only save the extra PEFT weights that were trained, meaning it is super efficient to store, transfer, and load. For example, this <a href="https://huggingface.co/ybelkada/opt-350m-lora" rel="nofollow">facebook/opt-350m</a> model trained with LoRA only contains two files: <code>adapter_config.json</code> and <code>adapter_model.safetensors</code>. The <code>adapter_model.safetensors</code> file is just 6.3MB!</p> <div class="flex flex-col justify-center" data-svelte-h="svelte-1nqpszv"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/PEFT-hub-screenshot.png"> <figcaption class="text-center">The adapter weights for a opt-350m model stored on the Hub are only ~6MB compared to the full size of the model weights, which can be ~700MB.</figcaption></div> <h2 class="relative group"><a id="inference" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#inference"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Inference</span></h2> <div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p data-svelte-h="svelte-bec5ju">Take a look at the <a href="package_reference/auto_class">AutoPeftModel</a> API reference for a complete list of available <code>AutoPeftModel</code> classes.</p></div> <p data-svelte-h="svelte-tt9tuw">Easily load any PEFT-trained model for inference with the <a href="/docs/peft/v0.13.0/en/package_reference/auto_class#peft.AutoPeftModel">AutoPeftModel</a> class and the <a href="https://huggingface.co/docs/transformers/v4.44.2/en/main_classes/model#transformers.PreTrainedModel.from_pretrained" rel="nofollow">from_pretrained</a> method:</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><span class="hljs-keyword">from</span> peft <span class="hljs-keyword">import</span> AutoPeftModelForCausalLM <span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer <span class="hljs-keyword">import</span> torch model = AutoPeftModelForCausalLM.from_pretrained(<span class="hljs-string">"ybelkada/opt-350m-lora"</span>) tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"facebook/opt-350m"</span>) model = model.to(<span class="hljs-string">"cuda"</span>) model.<span class="hljs-built_in">eval</span>() inputs = tokenizer(<span class="hljs-string">"Preheat the oven to 350 degrees and place the cookie dough"</span>, return_tensors=<span class="hljs-string">"pt"</span>) outputs = model.generate(input_ids=inputs[<span class="hljs-string">"input_ids"</span>].to(<span class="hljs-string">"cuda"</span>), max_new_tokens=<span class="hljs-number">50</span>) <span class="hljs-built_in">print</span>(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>]) <span class="hljs-string">"Preheat the oven to 350 degrees and place the cookie dough in the center of the oven. In a large bowl, combine the flour, baking powder, baking soda, salt, and cinnamon. In a separate bowl, combine the egg yolks, sugar, and vanilla."</span></pre></div> <p data-svelte-h="svelte-l2sl5z">For other tasks that aren’t explicitly supported with an <code>AutoPeftModelFor</code> class - such as automatic speech recognition - you can still use the base <a href="/docs/peft/v0.13.0/en/package_reference/auto_class#peft.AutoPeftModel">AutoPeftModel</a> class to load a model for the task.</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><span class="hljs-keyword">from</span> peft <span class="hljs-keyword">import</span> AutoPeftModel model = AutoPeftModel.from_pretrained(<span class="hljs-string">"smangrul/openai-whisper-large-v2-LORA-colab"</span>)</pre></div> <h2 class="relative group"><a id="next-steps" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#next-steps"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Next steps</span></h2> <p data-svelte-h="svelte-zexwzv">Now that you’ve seen how to train a model with one of the PEFT methods, we encourage you to try out some of the other methods like prompt tuning. The steps are very similar to the ones shown in the quicktour:</p> <ol data-svelte-h="svelte-faajtu"><li>prepare a <a href="/docs/peft/v0.13.0/en/package_reference/config#peft.PeftConfig">PeftConfig</a> for a PEFT method</li> <li>use the <a href="/docs/peft/v0.13.0/en/package_reference/peft_model#peft.get_peft_model">get_peft_model()</a> method to create a <a href="/docs/peft/v0.13.0/en/package_reference/peft_model#peft.PeftModel">PeftModel</a> from the configuration and base model</li></ol> <p data-svelte-h="svelte-13ytqkh">Then you can train it however you like! To load a PEFT model for inference, you can use the <a href="/docs/peft/v0.13.0/en/package_reference/auto_class#peft.AutoPeftModel">AutoPeftModel</a> class.</p> <p data-svelte-h="svelte-eanbvx">Feel free to also take a look at the task guides if you’re interested in training a model with another PEFT method for a specific task such as semantic segmentation, multilingual automatic speech recognition, DreamBooth, token classification, and more.</p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/peft/blob/main/docs/source/quicktour.md" target="_blank"><span data-svelte-h="svelte-1kd6by1"><</span> <span data-svelte-h="svelte-x0xyl0">></span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a> <p></p> <script> { __sveltekit_6j4juc = { assets: "/docs/peft/v0.13.0/en", base: "/docs/peft/v0.13.0/en", env: {} }; const element = document.currentScript.parentElement; const data = [null,null]; Promise.all([ import("/docs/peft/v0.13.0/en/_app/immutable/entry/start.c3c2c93b.js"), import("/docs/peft/v0.13.0/en/_app/immutable/entry/app.c13afa6b.js") ]).then(([kit, app]) => { kit.start(app, element, { node_ids: [0, 46], data, form: null, error: null }); }); } </script> </div> <div class="SVELTE_HYDRATER contents" data-target="DocFooterNav" data-props="{"classNames":"mx-auto mt-16 flex max-w-4xl items-center pb-8 font-sans font-medium leading-6 xl:mt-32","chapterPrev":{"title":"🤗 PEFT","isExpanded":true,"id":"index","url":"/docs/peft/index"},"chapterNext":{"title":"Installation","isExpanded":true,"id":"install","url":"/docs/peft/install"},"isCourse":false,"isLoggedIn":false}"><div class="mx-auto mt-16 flex max-w-4xl items-center pb-8 font-sans font-medium leading-6 xl:mt-32"><a href="/docs/peft/index" class="mr-8 flex transform items-center text-gray-600 transition-all hover:-translate-x-px hover:text-gray-900 dark:hover:text-gray-300"><span class="mr-2 translate-y-px">←</span>🤗 PEFT</a> <a href="/docs/peft/install" class="ml-auto flex transform items-center text-right text-gray-600 transition-all hover:translate-x-px hover:text-gray-900 dark:hover:text-gray-300">Installation<span class="ml-2 translate-y-px">→</span></a></div></div></div></div> <div class="sticky top-0 self-start"><div class="SVELTE_HYDRATER contents" data-target="SubSideMenu" data-props="{"chapter":{"title":"Quicktour","isExpanded":true,"id":"quicktour","url":"#quicktour","sections":[{"title":"Train","isExpanded":true,"id":"train","url":"#train","sections":[{"title":"Save model","isExpanded":true,"id":"save-model","url":"#save-model","sections":[]}]},{"title":"Inference","isExpanded":true,"id":"inference","url":"#inference","sections":[]},{"title":"Next steps","isExpanded":true,"id":"next-steps","url":"#next-steps","sections":[]}]}}"> <nav class="hidden h-dvh w-[270px] flex-none flex-col space-y-3 overflow-y-auto break-words border-l pb-16 pl-6 pr-10 pt-24 text-sm lg:flex 2xl:w-[305px]"> <a href="#quicktour" class=" text-gray-400 transform hover:translate-x-px hover:text-gray-700 dark:hover:text-gray-300" id="nav-quicktour"><wbr>Quicktour</a> <a href="#train" class="pl-4 text-gray-400 transform hover:translate-x-px hover:text-gray-700 dark:hover:text-gray-300" id="nav-train"><wbr>Train</a> <a href="#save-model" class="pl-8 text-gray-400 transform hover:translate-x-px hover:text-gray-700 dark:hover:text-gray-300" id="nav-save-model"><wbr>Save model</a> <a href="#inference" class="pl-4 text-gray-400 transform hover:translate-x-px hover:text-gray-700 dark:hover:text-gray-300" id="nav-inference"><wbr>Inference</a> <a href="#next-steps" class="pl-4 text-gray-400 transform hover:translate-x-px hover:text-gray-700 dark:hover:text-gray-300" id="nav-next-steps"><wbr>Next steps</a> </nav></div></div></div> <div id="doc-footer"></div></main> </div> <script> import("\/front\/build\/kube-aa0622c\/index.js"); window.moonSha = "kube-aa0622c\/"; window.__hf_deferred = {}; </script>  <script> if (["hf.co", "huggingface.co"].includes(window.location.hostname)) { const script = document.createElement("script"); script.src = "https://js.stripe.com/v3/"; script.async = true; document.head.appendChild(script); } </script> </body> </html>