Buckets:

hf-doc-build
/

doc-dev

Files

xet

hf-doc-build/doc-dev / accelerate /pr_4021 /en /usage_guides /training_zoo.html

HuggingFaceDocBuilder

about 1 month ago

download

raw

52.4 kB

	<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{"title":"Example Zoo","local":"example-zoo","sections":[{"title":"Official Accelerate Examples:","local":"official-accelerate-examples","sections":[{"title":"Basic Examples","local":"basic-examples","sections":[],"depth":3},{"title":"Feature Specific Examples","local":"feature-specific-examples","sections":[],"depth":3},{"title":"Full Examples","local":"full-examples","sections":[],"depth":3}],"depth":2},{"title":"Integration Examples","local":"integration-examples","sections":[{"title":"Amphion","local":"amphion","sections":[],"depth":3},{"title":"Catalyst","local":"catalyst","sections":[],"depth":3},{"title":"DALLE2-pytorch","local":"dalle2-pytorch","sections":[],"depth":3},{"title":"Diffusers","local":"diffusers","sections":[],"depth":3},{"title":"fastai","local":"fastai","sections":[],"depth":3},{"title":"GradsFlow","local":"gradsflow","sections":[],"depth":3},{"title":"imagen-pytorch","local":"imagen-pytorch","sections":[],"depth":3},{"title":"Kornia","local":"kornia","sections":[],"depth":3},{"title":"PyTorch Accelerated","local":"pytorch-accelerated","sections":[],"depth":3},{"title":"PyTorch3D","local":"pytorch3d","sections":[],"depth":3},{"title":"Stable-Dreamfusion","local":"stable-dreamfusion","sections":[],"depth":3},{"title":"Tez","local":"tez","sections":[],"depth":3},{"title":"trlx","local":"trlx","sections":[],"depth":3},{"title":"Comfy-UI","local":"comfy-ui","sections":[],"depth":3}],"depth":2},{"title":"In Science","local":"in-science","sections":[],"depth":2}],"depth":1}">
	<link href="/docs/accelerate/pr_4021/en/_app/immutable/assets/0.e3b0c442.css" rel="modulepreload">
	<link rel="modulepreload" href="/docs/accelerate/pr_4021/en/_app/immutable/entry/start.8a49e72b.js">
	<link rel="modulepreload" href="/docs/accelerate/pr_4021/en/_app/immutable/chunks/scheduler.b9285784.js">
	<link rel="modulepreload" href="/docs/accelerate/pr_4021/en/_app/immutable/chunks/singletons.7547c222.js">
	<link rel="modulepreload" href="/docs/accelerate/pr_4021/en/_app/immutable/chunks/index.6d423e5c.js">
	<link rel="modulepreload" href="/docs/accelerate/pr_4021/en/_app/immutable/chunks/paths.d42c9205.js">
	<link rel="modulepreload" href="/docs/accelerate/pr_4021/en/_app/immutable/entry/app.1df4d18e.js">
	<link rel="modulepreload" href="/docs/accelerate/pr_4021/en/_app/immutable/chunks/preload-helper.b0bd19d1.js">
	<link rel="modulepreload" href="/docs/accelerate/pr_4021/en/_app/immutable/chunks/index.26bc89a1.js">
	<link rel="modulepreload" href="/docs/accelerate/pr_4021/en/_app/immutable/nodes/0.0e7c56e8.js">
	<link rel="modulepreload" href="/docs/accelerate/pr_4021/en/_app/immutable/chunks/each.e59479a4.js">
	<link rel="modulepreload" href="/docs/accelerate/pr_4021/en/_app/immutable/nodes/59.590babbe.js">
	<link rel="modulepreload" href="/docs/accelerate/pr_4021/en/_app/immutable/chunks/MermaidChart.svelte_svelte_type_style_lang.7a0ae628.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"Example Zoo","local":"example-zoo","sections":[{"title":"Official Accelerate Examples:","local":"official-accelerate-examples","sections":[{"title":"Basic Examples","local":"basic-examples","sections":[],"depth":3},{"title":"Feature Specific Examples","local":"feature-specific-examples","sections":[],"depth":3},{"title":"Full Examples","local":"full-examples","sections":[],"depth":3}],"depth":2},{"title":"Integration Examples","local":"integration-examples","sections":[{"title":"Amphion","local":"amphion","sections":[],"depth":3},{"title":"Catalyst","local":"catalyst","sections":[],"depth":3},{"title":"DALLE2-pytorch","local":"dalle2-pytorch","sections":[],"depth":3},{"title":"Diffusers","local":"diffusers","sections":[],"depth":3},{"title":"fastai","local":"fastai","sections":[],"depth":3},{"title":"GradsFlow","local":"gradsflow","sections":[],"depth":3},{"title":"imagen-pytorch","local":"imagen-pytorch","sections":[],"depth":3},{"title":"Kornia","local":"kornia","sections":[],"depth":3},{"title":"PyTorch Accelerated","local":"pytorch-accelerated","sections":[],"depth":3},{"title":"PyTorch3D","local":"pytorch3d","sections":[],"depth":3},{"title":"Stable-Dreamfusion","local":"stable-dreamfusion","sections":[],"depth":3},{"title":"Tez","local":"tez","sections":[],"depth":3},{"title":"trlx","local":"trlx","sections":[],"depth":3},{"title":"Comfy-UI","local":"comfy-ui","sections":[],"depth":3}],"depth":2},{"title":"In Science","local":"in-science","sections":[],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <div class="items-center shrink-0 min-w-[100px] max-sm:min-w-[50px] justify-end ml-auto flex" style="float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"><div class="inline-flex rounded-md max-sm:rounded-sm"><button class="inline-flex items-center gap-1 h-7 max-sm:h-7 px-2 max-sm:px-1.5 text-sm font-medium text-gray-800 border border-r-0 rounded-l-md max-sm:rounded-l-sm border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-live="polite"><span class="inline-flex items-center justify-center rounded-md p-0.5 max-sm:p-0 hover:text-gray-800 dark:hover:text-gray-200"><svg class="sm:size-3.5 size-3" 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></span> <span>Copy page</span></button> <button class="inline-flex items-center justify-center w-6 max-sm:w-5 h-7 max-sm:h-7 disabled:pointer-events-none text-sm text-gray-500 hover:text-gray-700 dark:hover:text-white rounded-r-md max-sm:rounded-r-sm border border-l transition border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-haspopup="menu" aria-expanded="false" aria-label="Open copy menu"><svg class="transition-transform text-gray-400 overflow-visible sm:size-3.5 size-3 rotate-0" width="1em" height="1em" viewBox="0 0 12 7" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1 1L6 6L11 1" stroke="currentColor"></path></svg></button></div> </div> <h1 class="relative group"><a id="example-zoo" 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="#example-zoo"><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>Example Zoo</span></h1> <p data-svelte-h="svelte-i6wx37">Below contains a non-exhaustive list of tutorials and scripts showcasing Accelerate.</p> <h2 class="relative group"><a id="official-accelerate-examples" 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="#official-accelerate-examples"><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>Official Accelerate Examples:</span></h2> <h3 class="relative group"><a id="basic-examples" 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="#basic-examples"><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>Basic Examples</span></h3> <p data-svelte-h="svelte-15aiqp">These examples showcase the base features of Accelerate and are a great starting point</p> <ul data-svelte-h="svelte-14t37ux"><li><a href="https://github.com/huggingface/accelerate/blob/main/examples/nlp_example.py" rel="nofollow">Barebones NLP example</a></li> <li><a href="https://github.com/huggingface/notebooks/blob/main/examples/accelerate_examples/simple_nlp_example.ipynb" rel="nofollow">Barebones distributed NLP example in a Jupyter Notebook</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/cv_example.py" rel="nofollow">Barebones computer vision example</a></li> <li><a href="https://github.com/huggingface/notebooks/blob/main/examples/accelerate_examples/simple_cv_example.ipynb" rel="nofollow">Barebones distributed computer vision example in a Jupyter Notebook</a></li> <li><a href="https://www.kaggle.com/code/muellerzr/multi-gpu-and-accelerate" rel="nofollow">Using Accelerate in Kaggle</a></li></ul> <h3 class="relative group"><a id="feature-specific-examples" 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="#feature-specific-examples"><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>Feature Specific Examples</span></h3> <p data-svelte-h="svelte-1jl7f7y">These examples showcase specific features that the Accelerate framework offers</p> <ul data-svelte-h="svelte-p2tbx3"><li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/automatic_gradient_accumulation.py" rel="nofollow">Automatic memory-aware gradient accumulation</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/checkpointing.py" rel="nofollow">Checkpointing states</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/cross_validation.py" rel="nofollow">Cross validation</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/deepspeed_with_config_support.py" rel="nofollow">DeepSpeed</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/fsdp_with_peak_mem_tracking.py" rel="nofollow">Fully Sharded Data Parallelism</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/gradient_accumulation.py" rel="nofollow">Gradient accumulation</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/memory.py" rel="nofollow">Memory-aware batch size finder</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/multi_process_metrics.py" rel="nofollow">Metric Computation</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/tracking.py" rel="nofollow">Using Trackers</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/megatron_lm_gpt_pretraining.py" rel="nofollow">Using Megatron-LM</a></li></ul> <h3 class="relative group"><a id="full-examples" 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="#full-examples"><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>Full Examples</span></h3> <p data-svelte-h="svelte-1s449lo">These examples showcase every feature in Accelerate at once that was shown in “Feature Specific Examples”</p> <ul data-svelte-h="svelte-kaelad"><li><a href="https://github.com/huggingface/accelerate/blob/main/examples/complete_nlp_example.py" rel="nofollow">Complete NLP example</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/complete_cv_example.py" rel="nofollow">Complete computer vision example</a></li> <li><a href="https://github.com/yuvalkirstain/PickScore" rel="nofollow">Very complete and extensible vision example showcasing SLURM, hydra, and a very extensible usage of the framework</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm_no_trainer.py" rel="nofollow">Causal language model fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm_no_trainer.py" rel="nofollow">Masked language model fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py" rel="nofollow">Speech pretraining example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/translation/run_translation_no_trainer.py" rel="nofollow">Translation fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue_no_trainer.py" rel="nofollow">Text classification fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py" rel="nofollow">Semantic segmentation fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa_no_trainer.py" rel="nofollow">Question answering fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py" rel="nofollow">Beam search question answering fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/multiple-choice/run_swag_no_trainer.py" rel="nofollow">Multiple choice question answering fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/token-classification/run_ner_no_trainer.py" rel="nofollow">Named entity recognition fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/image-classification/run_image_classification_no_trainer.py" rel="nofollow">Image classification fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/summarization/run_summarization_no_trainer.py" rel="nofollow">Summarization fine-tuning example</a></li> <li><a href="https://github.com/huggingface/notebooks/blob/main/sagemaker/22_accelerate_sagemaker_examples/README.md" rel="nofollow">End-to-end examples on how to use AWS SageMaker integration of Accelerate</a></li> <li><a href="https://github.com/pacman100/accelerate-megatron-test" rel="nofollow">Megatron-LM examples for various NLp tasks</a></li></ul> <h2 class="relative group"><a id="integration-examples" 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="#integration-examples"><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>Integration Examples</span></h2> <p data-svelte-h="svelte-1qkx6ia">These are tutorials from libraries that integrate with Accelerate:</p> <blockquote data-svelte-h="svelte-1a5tgms"><p>Don’t find your integration here? Make a PR to include it!</p></blockquote> <h3 class="relative group"><a id="amphion" 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="#amphion"><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>Amphion</span></h3> <ul data-svelte-h="svelte-m2whwv"><li><a href="https://github.com/open-mmlab/Amphion/blob/main/egs/tts/README.md" rel="nofollow">Training Text-to-Speech Models with Amphion</a></li> <li><a href="https://github.com/open-mmlab/Amphion/blob/main/egs/svc/README.md" rel="nofollow">Training Singing Voice Conversion Models with Amphion</a></li> <li><a href="https://github.com/open-mmlab/Amphion/blob/main/egs/vocoder/README.md" rel="nofollow">Training Vocoders with Amphion</a></li></ul> <h3 class="relative group"><a id="catalyst" 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="#catalyst"><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>Catalyst</span></h3> <ul data-svelte-h="svelte-ddv6p9"><li><a href="https://catalyst-team.github.io/catalyst/tutorials/ddp.html" rel="nofollow">Distributed training tutorial with Catalyst</a></li></ul> <h3 class="relative group"><a id="dalle2-pytorch" 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="#dalle2-pytorch"><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>DALLE2-pytorch</span></h3> <ul data-svelte-h="svelte-1lx7ztj"><li><a href="https://github.com/lucidrains/DALLE2-pytorch#usage" rel="nofollow">Fine-tuning DALLE2</a></li></ul> <h3 class="relative group"><a id="diffusers" 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="#diffusers"><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>Diffusers</span></h3> <ul data-svelte-h="svelte-18qz8iq"><li><a href="https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion" rel="nofollow">Performing textual inversion with diffusers</a></li> <li><a href="https://github.com/huggingface/diffusers/tree/main/examples/dreambooth" rel="nofollow">Training DreamBooth with diffusers</a></li></ul> <h3 class="relative group"><a id="fastai" 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="#fastai"><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>fastai</span></h3> <ul data-svelte-h="svelte-z25h44"><li><a href="https://docs.fast.ai/tutorial.distributed.html" rel="nofollow">Distributed training from Jupyter Notebooks with fastai</a></li> <li><a href="https://docs.fast.ai/examples/distributed_app_examples.html" rel="nofollow">Basic distributed training examples with fastai</a></li></ul> <h3 class="relative group"><a id="gradsflow" 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="#gradsflow"><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>GradsFlow</span></h3> <ul data-svelte-h="svelte-ytpfrs"><li><a href="https://docs.gradsflow.com/en/latest/examples/nbs/01-ImageClassification/" rel="nofollow">Auto Image Classification with GradsFlow</a></li></ul> <h3 class="relative group"><a id="imagen-pytorch" 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="#imagen-pytorch"><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>imagen-pytorch</span></h3> <ul data-svelte-h="svelte-ebz57v"><li><a href="https://github.com/lucidrains/imagen-pytorch#usage" rel="nofollow">Fine-tuning Imagen</a></li></ul> <h3 class="relative group"><a id="kornia" 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="#kornia"><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>Kornia</span></h3> <ul data-svelte-h="svelte-6mcze3"><li><a href="https://kornia.readthedocs.io/en/latest/get-started/training.html" rel="nofollow">Fine-tuning vision models with Kornia’s Trainer</a></li></ul> <h3 class="relative group"><a id="pytorch-accelerated" 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="#pytorch-accelerated"><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>PyTorch Accelerated</span></h3> <ul data-svelte-h="svelte-188zfrf"><li><a href="https://pytorch-accelerated.readthedocs.io/en/latest/quickstart.html" rel="nofollow">Quickstart distributed training tutorial with PyTorch Accelerated</a></li></ul> <h3 class="relative group"><a id="pytorch3d" 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="#pytorch3d"><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>PyTorch3D</span></h3> <ul data-svelte-h="svelte-o30kn6"><li><a href="https://pytorch3d.org/tutorials/" rel="nofollow">Perform Deep Learning with 3D data</a></li></ul> <h3 class="relative group"><a id="stable-dreamfusion" 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="#stable-dreamfusion"><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>Stable-Dreamfusion</span></h3> <ul data-svelte-h="svelte-7hmcdx"><li><a href="https://colab.research.google.com/drive/1MXT3yfOFvO0ooKEfiUUvTKwUkrrlCHpF?usp=sharing" rel="nofollow">Training with Stable-Dreamfusion to convert text to a 3D model</a></li></ul> <h3 class="relative group"><a id="tez" 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="#tez"><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>Tez</span></h3> <ul data-svelte-h="svelte-1kb8pbu"><li><a href="https://www.kaggle.com/code/abhishek/tez-faster-and-easier-training-for-leaf-detection/notebook" rel="nofollow">Leaf disease detection with Tez and Accelerate</a></li></ul> <h3 class="relative group"><a id="trlx" 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="#trlx"><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>trlx</span></h3> <ul data-svelte-h="svelte-1glwo4f"><li><a href="https://github.com/CarperAI/trlx#example-how-to-add-a-task" rel="nofollow">How to implement a sentiment learning task with trlx</a></li></ul> <h3 class="relative group"><a id="comfy-ui" 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="#comfy-ui"><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>Comfy-UI</span></h3> <ul data-svelte-h="svelte-fpf70h"><li><a href="https://github.com/comfyanonymous/ComfyUI/blob/master/comfy/model_management.py#L291-L296" rel="nofollow">Enabling using large Stable Diffusion Models in low-vram settings using Accelerate</a></li></ul> <h2 class="relative group"><a id="in-science" 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="#in-science"><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>In Science</span></h2> <p data-svelte-h="svelte-1vcat29">Below contains a non-exhaustive list of papers utilizing Accelerate.</p> <blockquote data-svelte-h="svelte-fqfs08"><p>Don’t find your paper here? Make a PR to include it!</p></blockquote> <ul data-svelte-h="svelte-1xumll1"><li>Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, Omer Levy: “Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation”, 2023; <a href="http://huggingface.co/papers/2305.01569" rel="nofollow">arXiv:2305.01569</a>.</li> <li>Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, Ee-Peng Lim: “Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models”, 2023; <a href="http://huggingface.co/papers/2305.04091" rel="nofollow">arXiv:2305.04091</a>.</li> <li>Arthur Câmara, Claudia Hauff: “Moving Stuff Around: A study on efficiency of moving documents into memory for Neural IR models”, 2022; <a href="http://huggingface.co/papers/2205.08343" rel="nofollow">arXiv:2205.08343</a>.</li> <li>Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Daniel Y. Fu, Zhiqiang Xie, Beidi Chen, Clark Barrett, Joseph E. Gonzalez, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang: “High-throughput Generative Inference of Large Language Models with a Single GPU”, 2023; <a href="http://huggingface.co/papers/2303.06865" rel="nofollow">arXiv:2303.06865</a>.</li> <li>Peter Melchior, Yan Liang, ChangHoon Hahn, Andy Goulding: “Autoencoding Galaxy Spectra I: Architecture”, 2022; <a href="http://huggingface.co/papers/2211.07890" rel="nofollow">arXiv:2211.07890</a>.</li> <li>Jiaao Chen, Aston Zhang, Mu Li, Alex Smola, Diyi Yang: “A Cheaper and Better Diffusion Language Model with Soft-Masked Noise”, 2023; <a href="http://huggingface.co/papers/2304.04746" rel="nofollow">arXiv:2304.04746</a>.</li> <li>Ayaan Haque, Matthew Tancik, Alexei A. Efros, Aleksander Holynski, Angjoo Kanazawa: “Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions”, 2023; <a href="http://huggingface.co/papers/2303.12789" rel="nofollow">arXiv:2303.12789</a>.</li> <li>Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, Andrea Vedaldi: “RealFusion: 360° Reconstruction of Any Object from a Single Image”, 2023; <a href="http://huggingface.co/papers/2302.10663" rel="nofollow">arXiv:2302.10663</a>.</li> <li>Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, Hongsheng Li: “Better Aligning Text-to-Image Models with Human Preference”, 2023; <a href="http://huggingface.co/papers/2303.14420" rel="nofollow">arXiv:2303.14420</a>.</li> <li>Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, Yueting Zhuang: “HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace”, 2023; <a href="http://huggingface.co/papers/2303.17580" rel="nofollow">arXiv:2303.17580</a>.</li> <li>Yue Yang, Wenlin Yao, Hongming Zhang, Xiaoyang Wang, Dong Yu, Jianshu Chen: “Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination”, 2022; <a href="http://huggingface.co/papers/2210.12261" rel="nofollow">arXiv:2210.12261</a>.</li> <li>Sheng-Yen Chou, Pin-Yu Chen, Tsung-Yi Ho: “How to Backdoor Diffusion Models?”, 2022; <a href="http://huggingface.co/papers/2212.05400" rel="nofollow">arXiv:2212.05400</a>.</li> <li>Junyoung Seo, Wooseok Jang, Min-Seop Kwak, Jaehoon Ko, Hyeonsu Kim, Junho Kim, Jin-Hwa Kim, Jiyoung Lee, Seungryong Kim: “Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation”, 2023; <a href="http://huggingface.co/papers/2303.07937" rel="nofollow">arXiv:2303.07937</a>.</li> <li>Or Patashnik, Daniel Garibi, Idan Azuri, Hadar Averbuch-Elor, Daniel Cohen-Or: “Localizing Object-level Shape Variations with Text-to-Image Diffusion Models”, 2023; <a href="http://huggingface.co/papers/2303.11306" rel="nofollow">arXiv:2303.11306</a>.</li> <li>Dídac Surís, Sachit Menon, Carl Vondrick: “ViperGPT: Visual Inference via Python Execution for Reasoning”, 2023; <a href="http://huggingface.co/papers/2303.08128" rel="nofollow">arXiv:2303.08128</a>.</li> <li>Chenyang Qi, Xiaodong Cun, Yong Zhang, Chenyang Lei, Xintao Wang, Ying Shan, Qifeng Chen: “FateZero: Fusing Attentions for Zero-shot Text-based Video Editing”, 2023; <a href="http://huggingface.co/papers/2303.09535" rel="nofollow">arXiv:2303.09535</a>.</li> <li>Sean Welleck, Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi, Yejin Choi: “NaturalProver: Grounded Mathematical Proof Generation with Language Models”, 2022; <a href="http://huggingface.co/papers/2205.12910" rel="nofollow">arXiv:2205.12910</a>.</li> <li>Elad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes, Daniel Cohen-Or: “TEXTure: Text-Guided Texturing of 3D Shapes”, 2023; <a href="http://huggingface.co/papers/2302.01721" rel="nofollow">arXiv:2302.01721</a>.</li> <li>Puijin Cheng, Li Lin, Yijin Huang, Huaqing He, Wenhan Luo, Xiaoying Tang: “Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement”, 2023; <a href="http://huggingface.co/papers/2303.04603" rel="nofollow">arXiv:2303.04603</a>.</li> <li>Shun Shao, Yftah Ziser, Shay Cohen: “Erasure of Unaligned Attributes from Neural Representations”, 2023; <a href="http://huggingface.co/papers/2302.02997" rel="nofollow">arXiv:2302.02997</a>.</li> <li>Seonghyeon Ye, Hyeonbin Hwang, Sohee Yang, Hyeongu Yun, Yireun Kim, Minjoon Seo: “In-Context Instruction Learning”, 2023; <a href="http://huggingface.co/papers/2302.14691" rel="nofollow">arXiv:2302.14691</a>.</li> <li>Shikun Liu, Linxi Fan, Edward Johns, Zhiding Yu, Chaowei Xiao, Anima Anandkumar: “Prismer: A Vision-Language Model with An Ensemble of Experts”, 2023; <a href="http://huggingface.co/papers/2303.02506" rel="nofollow">arXiv:2303.02506</a>.</li> <li>Haoyu Chen, Zhihua Wang, Yang Yang, Qilin Sun, Kede Ma: “Learning a Deep Color Difference Metric for Photographic Images”, 2023; <a href="http://huggingface.co/papers/2303.14964" rel="nofollow">arXiv:2303.14964</a>.</li> <li>Van-Hoang Le, Hongyu Zhang: “Log Parsing with Prompt-based Few-shot Learning”, 2023; <a href="http://huggingface.co/papers/2302.07435" rel="nofollow">arXiv:2302.07435</a>.</li> <li>Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi, Ana Brassard, Masashi Yoshikawa, Keisuke Sakaguchi, Kentaro Inui: “Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?”, 2023; <a href="http://huggingface.co/papers/2302.07866" rel="nofollow">arXiv:2302.07866</a>.</li> <li>Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, Prithviraj Ammanabrolu: “Behavior Cloned Transformers are Neurosymbolic Reasoners”, 2022; <a href="http://huggingface.co/papers/2210.07382" rel="nofollow">arXiv:2210.07382</a>.</li> <li>Martin Wessel, Tomáš Horych, Terry Ruas, Akiko Aizawa, Bela Gipp, Timo Spinde: “Introducing MBIB — the first Media Bias Identification Benchmark Task and Dataset Collection”, 2023; <a href="http://huggingface.co/papers/2304.13148" rel="nofollow">arXiv:2304.13148</a>. DOI: [https://dx.doi.org/10.1145/3539618.3591882 10.1145/3539618.3591882].</li> <li>Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, Daniel Cohen-Or: “Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models”, 2023; <a href="http://huggingface.co/papers/2301.13826" rel="nofollow">arXiv:2301.13826</a>.</li> <li>Marcio Fonseca, Yftah Ziser, Shay B. Cohen: “Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents”, 2022; <a href="http://huggingface.co/papers/2205.12486" rel="nofollow">arXiv:2205.12486</a>.</li> <li>Elad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes, Daniel Cohen-Or: “TEXTure: Text-Guided Texturing of 3D Shapes”, 2023; <a href="http://huggingface.co/papers/2302.01721" rel="nofollow">arXiv:2302.01721</a>.</li> <li>Tianxing He, Jingyu Zhang, Tianle Wang, Sachin Kumar, Kyunghyun Cho, James Glass, Yulia Tsvetkov: “On the Blind Spots of Model-Based Evaluation Metrics for Text Generation”, 2022; <a href="http://huggingface.co/papers/2212.10020" rel="nofollow">arXiv:2212.10020</a>.</li> <li>Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham: “In-Context Retrieval-Augmented Language Models”, 2023; <a href="http://huggingface.co/papers/2302.00083" rel="nofollow">arXiv:2302.00083</a>.</li> <li>Dacheng Li, Rulin Shao, Hongyi Wang, Han Guo, Eric P. Xing, Hao Zhang: “MPCFormer: fast, performant and private Transformer inference with MPC”, 2022; <a href="http://huggingface.co/papers/2211.01452" rel="nofollow">arXiv:2211.01452</a>.</li> <li>Baolin Peng, Michel Galley, Pengcheng He, Chris Brockett, Lars Liden, Elnaz Nouri, Zhou Yu, Bill Dolan, Jianfeng Gao: “GODEL: Large-Scale Pre-Training for Goal-Directed Dialog”, 2022; <a href="http://huggingface.co/papers/2206.11309" rel="nofollow">arXiv:2206.11309</a>.</li> <li>Egil Rønningstad, Erik Velldal, Lilja Øvrelid: “Entity-Level Sentiment Analysis (ELSA): An exploratory task survey”, 2023, Proceedings of the 29th International Conference on Computational Linguistics, 2022, pages 6773-6783; <a href="http://huggingface.co/papers/2304.14241" rel="nofollow">arXiv:2304.14241</a>.</li> <li>Charlie Snell, Ilya Kostrikov, Yi Su, Mengjiao Yang, Sergey Levine: “Offline RL for Natural Language Generation with Implicit Language Q Learning”, 2022; <a href="http://huggingface.co/papers/2206.11871" rel="nofollow">arXiv:2206.11871</a>.</li> <li>Zhiruo Wang, Shuyan Zhou, Daniel Fried, Graham Neubig: “Execution-Based Evaluation for Open-Domain Code Generation”, 2022; <a href="http://huggingface.co/papers/2212.10481" rel="nofollow">arXiv:2212.10481</a>.</li> <li>Minh-Long Luu, Zeyi Huang, Eric P. Xing, Yong Jae Lee, Haohan Wang: “Expeditious Saliency-guided Mix-up through Random Gradient Thresholding”, 2022; <a href="http://huggingface.co/papers/2212.04875" rel="nofollow">arXiv:2212.04875</a>.</li> <li>Jun Hao Liew, Hanshu Yan, Daquan Zhou, Jiashi Feng: “MagicMix: Semantic Mixing with Diffusion Models”, 2022; <a href="http://huggingface.co/papers/2210.16056" rel="nofollow">arXiv:2210.16056</a>.</li> <li>Yaqing Wang, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Hassan Awadallah, Jianfeng Gao: “LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learners”, 2021; <a href="http://huggingface.co/papers/2110.06274" rel="nofollow">arXiv:2110.06274</a>.</li></ul> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/accelerate/blob/main/docs/source/usage_guides/training_zoo.md" target="_blank"><svg class="mr-1" 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="M31,16l-7,7l-1.41-1.41L28.17,16l-5.58-5.59L24,9l7,7z"></path><path d="M1,16l7-7l1.41,1.41L3.83,16l5.58,5.59L8,23l-7-7z"></path><path d="M12.419,25.484L17.639,6.552l1.932,0.518L14.351,26.002z"></path></svg> <span data-svelte-h="svelte-zjs2n5"><span class="underline">Update</span> on GitHub</span></a> <p></p>

	<script>
	{
	__sveltekit_1q7nz6m = {
	assets: "/docs/accelerate/pr_4021/en",
	base: "/docs/accelerate/pr_4021/en",
	env: {}
	};

	const element = document.currentScript.parentElement;

	const data = [null,null];

	Promise.all([
	import("/docs/accelerate/pr_4021/en/_app/immutable/entry/start.8a49e72b.js"),
	import("/docs/accelerate/pr_4021/en/_app/immutable/entry/app.1df4d18e.js")
	]).then(([kit, app]) => {
	kit.start(app, element, {
	node_ids: [0, 59],
	data,
	form: null,
	error: null
	});
	});
	}
	</script>

Xet Storage Details

Size:: 52.4 kB
Xet hash:: bdf9aec537f4cc2075927cffcfd4ac51c7feccdef2c637ecd123e9e65bd054e2

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.