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n=t.child;n!==null;){if(n.tag===5||n.tag===6)e.appendChild(n.stateNode);else if(n.tag!==4&&n.child!==null){n.child.return=n,n=n.child;continue}if(n===t)break;for(;n.sibling===null;){if(n.return===null||n.return===t)return;n=n.return}n.sibling.return=n.return,n=n.sibling}};Vs=function(){};ap=function(e,t,n,i){var r=e.memoizedProps;if(r!==i){e=t.stateNode,On(Pt.current);var a=null;switch(n){case"input":r=ds(e,r),i=ds(e,i),a=[];break;case"select":r=ye({},r,{value:void 0}),i=ye({},i,{value:void 0}),a=[];break;case"textarea":r=ms(e,r),i=ms(e,i),a=[];break;default:typeof r.onClick!="function"&&typeof i.onClick=="function"&&(e.onclick=Da)}gs(n,i);var o;n=null;for(u in r)if(!i.hasOwnProperty(u)&&r.hasOwnProperty(u)&&r[u]!=null)if(u==="style"){var s=r[u];for(o in s)s.hasOwnProperty(o)&&(n||(n={}),n[o]="")}else 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t.flags&128?(t.lanes=n,t):(i=i!==null,i!==(e!==null&&e.memoizedState!==null)&&i&&(t.child.flags|=8192,t.mode&1&&(e===null||he.current&1?Le===0&&(Le=3):Ql())),t.updateQueue!==null&&(t.flags|=4),ze(t),null);case 4:return Ii(),Vs(e,t),e===null&&br(t.stateNode.containerInfo),ze(t),null;case 10:return Ll(t.type._context),ze(t),null;case 17:return et(t.type)&&Na(),ze(t),null;case 19:if(de(he),a=t.memoizedState,a===null)return ze(t),null;if(i=(t.flags&128)!==0,o=a.rendering,o===null)if(i)Ki(a,!1);else{if(Le!==0||e!==null&&e.flags&128)for(e=t.child;e!==null;){if(o=Ba(e),o!==null){for(t.flags|=128,Ki(a,!1),i=o.updateQueue,i!==null&&(t.updateQueue=i,t.flags|=4),t.subtreeFlags=0,i=n,n=t.child;n!==null;)a=n,e=i,a.flags&=14680066,o=a.alternate,o===null?(a.childLanes=0,a.lanes=e,a.child=null,a.subtreeFlags=0,a.memoizedProps=null,a.memoizedState=null,a.updateQueue=null,a.dependencies=null,a.stateNode=null):(a.childLanes=o.childLanes,a.lanes=o.lanes,a.child=o.child,a.subtreeFlags=0,a.deletions=null,a.memoizedProps=o.memoizedProps,a.memoizedState=o.memoizedState,a.updateQueue=o.updateQueue,a.type=o.type,e=o.dependencies,a.dependencies=e===null?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return 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 *
 * @license MIT
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 * React Router DOM v6.28.0
 *
 * Copyright (c) Remix Software Inc.
 *
 * This source code is licensed under the MIT license found in the
 * LICENSE.md file in the root directory of this source tree.
 *
 * @license MIT
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This task has multiple variants such as instance segmentation, panoptic segmentation and semantic segmentation.",widgetModels:["nvidia/segformer-b0-finetuned-ade-512-512"],youtubeId:"dKE8SIt9C-w"},Z0=J0,G0={datasets:[],demo:{inputs:[{filename:"mask-generation-input.png",type:"img"}],outputs:[{filename:"mask-generation-output.png",type:"img"}]},metrics:[],models:[{description:"Small yet powerful mask generation model.",id:"Zigeng/SlimSAM-uniform-50"},{description:"Very strong mask generation model.",id:"facebook/sam2-hiera-large"}],spaces:[{description:"An application that combines a mask generation model with a zero-shot object detection model for text-guided image segmentation.",id:"merve/OWLSAM2"},{description:"An application that compares the performance of a large and a small mask generation model.",id:"merve/slimsam"},{description:"An application based on an improved mask generation model.",id:"SkalskiP/segment-anything-model-2"},{description:"An application to remove objects from videos using mask generation models.",id:"SkalskiP/SAM_and_ProPainter"}],summary:"Mask generation is the task of generating masks that identify a specific object or region of interest in a given image. 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It is calculated for each class separately",id:"Average Precision"},{description:"The Mean Average Precision (mAP) metric is the overall average of the AP values",id:"Mean Average Precision"},{description:"The APα metric is the Average Precision at the IoU threshold of a α value, for example, AP50 and AP75",id:"APα"}],models:[{description:"Solid object detection model pre-trained on the COCO 2017 dataset.",id:"facebook/detr-resnet-50"},{description:"Real-time and accurate object detection model.",id:"jameslahm/yolov10x"},{description:"Fast and accurate object detection model trained on COCO and Object365 datasets.",id:"PekingU/rtdetr_r18vd_coco_o365"}],spaces:[{description:"Leaderboard to compare various object detection models across several metrics.",id:"hf-vision/object_detection_leaderboard"},{description:"An application that contains various object detection models to try from.",id:"Gradio-Blocks/Object-Detection-With-DETR-and-YOLOS"},{description:"An application that shows multiple cutting edge techniques for object detection and tracking.",id:"kadirnar/torchyolo"},{description:"An object tracking, segmentation and inpainting application.",id:"VIPLab/Track-Anything"},{description:"Very fast object tracking application based on object detection.",id:"merve/RT-DETR-tracking-coco"}],summary:"Object Detection models allow users to identify objects of certain defined classes. Object detection models receive an image as input and output the images with bounding boxes and labels on detected objects.",widgetModels:["facebook/detr-resnet-50"],youtubeId:"WdAeKSOpxhw"},nw=tw,iw={datasets:[{description:"NYU Depth V2 Dataset: Video dataset containing both RGB and depth sensor data.",id:"sayakpaul/nyu_depth_v2"},{description:"Monocular depth estimation benchmark based without noise and errors.",id:"depth-anything/DA-2K"}],demo:{inputs:[{filename:"depth-estimation-input.jpg",type:"img"}],outputs:[{filename:"depth-estimation-output.png",type:"img"}]},metrics:[],models:[{description:"Cutting-edge depth estimation model.",id:"depth-anything/Depth-Anything-V2-Large"},{description:"A strong monocular depth estimation model.",id:"jingheya/lotus-depth-g-v1-0"},{description:"A depth estimation model that predicts depth in videos.",id:"tencent/DepthCrafter"},{description:"A robust depth estimation model.",id:"apple/DepthPro"}],spaces:[{description:"An application that predicts the depth of an image and then reconstruct the 3D model as voxels.",id:"radames/dpt-depth-estimation-3d-voxels"},{description:"An application for bleeding-edge depth estimation.",id:"akhaliq/depth-pro"},{description:"An application on cutting-edge depth estimation in videos.",id:"tencent/DepthCrafter"},{description:"A human-centric depth estimation application.",id:"facebook/sapiens-depth"}],summary:"Depth estimation is the task of predicting depth of the objects present in an image.",widgetModels:[""],youtubeId:""},rw=iw,aw={datasets:[],demo:{inputs:[],outputs:[]},isPlaceholder:!0,metrics:[],models:[],spaces:[],summary:"",widgetModels:[],youtubeId:void 0,canonicalId:void 0},zp=aw,ow={datasets:[{description:"A curation of widely used datasets for Data Driven Deep Reinforcement Learning (D4RL)",id:"edbeeching/decision_transformer_gym_replay"}],demo:{inputs:[{label:"State",content:"Red traffic light, pedestrians are about to pass.",type:"text"}],outputs:[{label:"Action",content:"Stop the car.",type:"text"},{label:"Next State",content:"Yellow light, pedestrians have crossed.",type:"text"}]},metrics:[{description:"Accumulated reward across all time steps discounted by a factor that ranges between 0 and 1 and determines how much the agent optimizes for future relative to immediate rewards. Measures how good is the policy ultimately found by a given algorithm considering uncertainty over the future.",id:"Discounted Total Reward"},{description:"Average return obtained after running the policy for a certain number of evaluation episodes. As opposed to total reward, mean reward considers how much reward a given algorithm receives while learning.",id:"Mean Reward"},{description:"Measures how good a given algorithm is after a predefined time. Some algorithms may be guaranteed to converge to optimal behavior across many time steps. However, an agent that reaches an acceptable level of optimality after a given time horizon may be preferable to one that ultimately reaches optimality but takes a long time.",id:"Level of Performance After Some Time"}],models:[{description:"A Reinforcement Learning model trained on expert data from the Gym Hopper environment",id:"edbeeching/decision-transformer-gym-hopper-expert"},{description:"A PPO agent playing seals/CartPole-v0 using the stable-baselines3 library and the RL Zoo.",id:"HumanCompatibleAI/ppo-seals-CartPole-v0"}],spaces:[{description:"An application for a cute puppy agent learning to catch a stick.",id:"ThomasSimonini/Huggy"},{description:"An application to play Snowball Fight with a reinforcement learning agent.",id:"ThomasSimonini/SnowballFight"}],summary:"Reinforcement learning is the computational approach of learning from action by interacting with an environment through trial and error and receiving rewards (negative or positive) as feedback",widgetModels:[],youtubeId:"q0BiUn5LiBc"},sw=ow,lw={datasets:[{description:"A famous question answering dataset based on English articles from Wikipedia.",id:"squad_v2"},{description:"A dataset of aggregated anonymized actual queries issued to the Google search engine.",id:"natural_questions"}],demo:{inputs:[{label:"Question",content:"Which name is also used to describe the Amazon rainforest in English?",type:"text"},{label:"Context",content:"The Amazon rainforest, also known in English as Amazonia or the Amazon Jungle",type:"text"}],outputs:[{label:"Answer",content:"Amazonia",type:"text"}]},metrics:[{description:"Exact Match is a metric based on the strict character match of the predicted answer and the right answer. For answers predicted correctly, the Exact Match will be 1. Even if only one character is different, Exact Match will be 0",id:"exact-match"},{description:" The F1-Score metric is useful if we value both false positives and false negatives equally. The F1-Score is calculated on each word in the predicted sequence against the correct answer",id:"f1"}],models:[{description:"A robust baseline model for most question answering domains.",id:"deepset/roberta-base-squad2"},{description:"Small yet robust model that can answer questions.",id:"distilbert/distilbert-base-cased-distilled-squad"},{description:"A special model that can answer questions from tables.",id:"google/tapas-base-finetuned-wtq"}],spaces:[{description:"An application that can answer a long question from Wikipedia.",id:"deepset/wikipedia-assistant"}],summary:"Question Answering models can retrieve the answer to a question from a given text, which is useful for searching for an answer in a document. Some question answering models can generate answers without context!",widgetModels:["deepset/roberta-base-squad2"],youtubeId:"ajPx5LwJD-I"},uw=lw,cw={datasets:[{description:"Bing queries with relevant passages from various web sources.",id:"ms_marco"}],demo:{inputs:[{label:"Source sentence",content:"Machine learning is so easy.",type:"text"},{label:"Sentences to compare to",content:"Deep learning is so straightforward.",type:"text"},{label:"",content:"This is so difficult, like rocket science.",type:"text"},{label:"",content:"I can't believe how much I struggled with this.",type:"text"}],outputs:[{type:"chart",data:[{label:"Deep learning is so straightforward.",score:.623},{label:"This is so difficult, like rocket science.",score:.413},{label:"I can't believe how much I struggled with this.",score:.256}]}]},metrics:[{description:"Reciprocal Rank is a measure used to rank the relevancy of documents given a set of documents. Reciprocal Rank is the reciprocal of the rank of the document retrieved, meaning, if the rank is 3, the Reciprocal Rank is 0.33. If the rank is 1, the Reciprocal Rank is 1",id:"Mean Reciprocal Rank"},{description:"The similarity of the embeddings is evaluated mainly on cosine similarity. It is calculated as the cosine of the angle between two vectors. It is particularly useful when your texts are not the same length",id:"Cosine Similarity"}],models:[{description:"This model works well for sentences and paragraphs and can be used for clustering/grouping and semantic searches.",id:"sentence-transformers/all-mpnet-base-v2"},{description:"A multilingual robust sentence similarity model..",id:"BAAI/bge-m3"}],spaces:[{description:"An application that leverages sentence similarity to answer questions from YouTube videos.",id:"Gradio-Blocks/Ask_Questions_To_YouTube_Videos"},{description:"An application that retrieves relevant PubMed abstracts for a given online article which can be used as further references.",id:"Gradio-Blocks/pubmed-abstract-retriever"},{description:"An application that leverages sentence similarity to summarize text.",id:"nickmuchi/article-text-summarizer"},{description:"A guide that explains how Sentence Transformers can be used for semantic search.",id:"sentence-transformers/Sentence_Transformers_for_semantic_search"}],summary:"Sentence Similarity is the task of determining how similar two texts are. Sentence similarity models convert input texts into vectors (embeddings) that capture semantic information and calculate how close (similar) they are between them. This task is particularly useful for information retrieval and clustering/grouping.",widgetModels:["BAAI/bge-small-en-v1.5"],youtubeId:"VCZq5AkbNEU"},dw=cw,fw={canonicalId:"text2text-generation",datasets:[{description:"News articles in five different languages along with their summaries. Widely used for benchmarking multilingual summarization models.",id:"mlsum"},{description:"English conversations and their summaries. Useful for benchmarking conversational agents.",id:"samsum"}],demo:{inputs:[{label:"Input",content:"The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. It was the first structure to reach a height of 300 metres. Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.",type:"text"}],outputs:[{label:"Output",content:"The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building. It was the first structure to reach a height of 300 metres.",type:"text"}]},metrics:[{description:"The generated sequence is compared against its summary, and the overlap of tokens are counted. ROUGE-N refers to overlap of N subsequent tokens, ROUGE-1 refers to overlap of single tokens and ROUGE-2 is the overlap of two subsequent tokens.",id:"rouge"}],models:[{description:"A strong summarization model trained on English news articles. Excels at generating factual summaries.",id:"facebook/bart-large-cnn"},{description:"A summarization model trained on medical articles.",id:"Falconsai/medical_summarization"}],spaces:[{description:"An application that can summarize long paragraphs.",id:"pszemraj/summarize-long-text"},{description:"A much needed summarization application for terms and conditions.",id:"ml6team/distilbart-tos-summarizer-tosdr"},{description:"An application that summarizes long documents.",id:"pszemraj/document-summarization"},{description:"An application that can detect errors in abstractive summarization.",id:"ml6team/post-processing-summarization"}],summary:"Summarization is the task of producing a shorter version of a document while preserving its important information. Some models can extract text from the original input, while other models can generate entirely new text.",widgetModels:["facebook/bart-large-cnn"],youtubeId:"yHnr5Dk2zCI"},pw=fw,mw={datasets:[{description:"The WikiTableQuestions dataset is a large-scale dataset for the task of question answering on semi-structured tables.",id:"wikitablequestions"},{description:"WikiSQL is a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables from Wikipedia.",id:"wikisql"}],demo:{inputs:[{table:[["Rank","Name","No.of reigns","Combined days"],["1","lou Thesz","3","3749"],["2","Ric Flair","8","3103"],["3","Harley Race","7","1799"]],type:"tabular"},{label:"Question",content:"What is the number of reigns for Harley Race?",type:"text"}],outputs:[{label:"Result",content:"7",type:"text"}]},metrics:[{description:"Checks whether the predicted answer(s) is the same as the ground-truth answer(s).",id:"Denotation Accuracy"}],models:[{description:"A table question answering model that is capable of neural SQL execution, i.e., employ TAPEX to execute a SQL query on a given table.",id:"microsoft/tapex-base"},{description:"A robust table question answering model.",id:"google/tapas-base-finetuned-wtq"}],spaces:[{description:"An application that answers questions based on table CSV files.",id:"katanaml/table-query"}],summary:"Table Question Answering (Table QA) is the answering a question about an information on a given table.",widgetModels:["google/tapas-base-finetuned-wtq"]},hw=mw,gw={datasets:[{description:"A comprehensive curation of datasets covering all benchmarks.",id:"inria-soda/tabular-benchmark"}],demo:{inputs:[{table:[["Glucose","Blood Pressure ","Skin Thickness","Insulin","BMI"],["148","72","35","0","33.6"],["150","50","30","0","35.1"],["141","60","29","1","39.2"]],type:"tabular"}],outputs:[{table:[["Diabetes"],["1"],["1"],["0"]],type:"tabular"}]},metrics:[{description:"",id:"accuracy"},{description:"",id:"recall"},{description:"",id:"precision"},{description:"",id:"f1"}],models:[{description:"Breast cancer prediction model based on decision trees.",id:"scikit-learn/cancer-prediction-trees"}],spaces:[{description:"An application that can predict defective products on a production line.",id:"scikit-learn/tabular-playground"},{description:"An application that compares various tabular classification techniques on different datasets.",id:"scikit-learn/classification"}],summary:"Tabular classification is the task of classifying a target category (a group) based on set of attributes.",widgetModels:["scikit-learn/tabular-playground"],youtubeId:""},yw=gw,vw={datasets:[{description:"A comprehensive curation of datasets covering all benchmarks.",id:"inria-soda/tabular-benchmark"}],demo:{inputs:[{table:[["Car Name","Horsepower","Weight"],["ford torino","140","3,449"],["amc hornet","97","2,774"],["toyota corolla","65","1,773"]],type:"tabular"}],outputs:[{table:[["MPG (miles per gallon)"],["17"],["18"],["31"]],type:"tabular"}]},metrics:[{description:"",id:"mse"},{description:"Coefficient of determination (or R-squared) is a measure of how well the model fits the data. Higher R-squared is considered a better fit.",id:"r-squared"}],models:[{description:"Fish weight prediction based on length measurements and species.",id:"scikit-learn/Fish-Weight"}],spaces:[{description:"An application that can predict weight of a fish based on set of attributes.",id:"scikit-learn/fish-weight-prediction"}],summary:"Tabular regression is the task of predicting a numerical value given a set of attributes.",widgetModels:["scikit-learn/Fish-Weight"],youtubeId:""},ww=vw,xw={datasets:[{description:"RedCaps is a large-scale dataset of 12M image-text pairs collected from Reddit.",id:"red_caps"},{description:"Conceptual Captions is a dataset consisting of ~3.3M images annotated with captions.",id:"conceptual_captions"}],demo:{inputs:[{label:"Input",content:"A city above clouds, pastel colors, Victorian style",type:"text"}],outputs:[{filename:"image.jpeg",type:"img"}]},metrics:[{description:"The Inception Score (IS) measure assesses diversity and meaningfulness. It uses a generated image sample to predict its label. A higher score signifies more diverse and meaningful images.",id:"IS"},{description:"The Fréchet Inception Distance (FID) calculates the distance between distributions between synthetic and real samples. A lower FID score indicates better similarity between the distributions of real and generated images.",id:"FID"},{description:"R-precision assesses how the generated image aligns with the provided text description. It uses the generated images as queries to retrieve relevant text descriptions. The top 'r' relevant descriptions are selected and used to calculate R-precision as r/R, where 'R' is the number of ground truth descriptions associated with the generated images. A higher R-precision value indicates a better model.",id:"R-Precision"}],models:[{description:"One of the most powerful image generation models that can generate realistic outputs.",id:"black-forest-labs/FLUX.1-dev"},{description:"A powerful yet fast image generation model.",id:"latent-consistency/lcm-lora-sdxl"},{description:"Text-to-image model for photorealistic generation.",id:"Kwai-Kolors/Kolors"},{description:"A powerful text-to-image model.",id:"stabilityai/stable-diffusion-3-medium-diffusers"}],spaces:[{description:"A powerful text-to-image application.",id:"stabilityai/stable-diffusion-3-medium"},{description:"A text-to-image application to generate comics.",id:"jbilcke-hf/ai-comic-factory"},{description:"An application to match multiple custom image generation models.",id:"multimodalart/flux-lora-lab"},{description:"A powerful yet very fast image generation application.",id:"latent-consistency/lcm-lora-for-sdxl"},{description:"A gallery to explore various text-to-image models.",id:"multimodalart/LoraTheExplorer"},{description:"An application for `text-to-image`, `image-to-image` and image inpainting.",id:"ArtGAN/Stable-Diffusion-ControlNet-WebUI"},{description:"An application to generate realistic images given photos of a person and a prompt.",id:"InstantX/InstantID"}],summary:"Text-to-image is the task of generating images from input text. These pipelines can also be used to modify and edit images based on text prompts.",widgetModels:["black-forest-labs/FLUX.1-dev"],youtubeId:""},bw=xw,kw={canonicalId:"text-to-audio",datasets:[{description:"10K hours of multi-speaker English dataset.",id:"parler-tts/mls_eng_10k"},{description:"Multi-speaker English dataset.",id:"mythicinfinity/libritts_r"}],demo:{inputs:[{label:"Input",content:"I love audio models on the Hub!",type:"text"}],outputs:[{filename:"audio.wav",type:"audio"}]},metrics:[{description:"The Mel Cepstral Distortion (MCD) metric is used to calculate the quality of generated speech.",id:"mel cepstral distortion"}],models:[{description:"A powerful TTS model.",id:"parler-tts/parler-tts-large-v1"},{description:"A massively multi-lingual TTS model.",id:"coqui/XTTS-v2"},{description:"Robust TTS model.",id:"metavoiceio/metavoice-1B-v0.1"},{description:"A prompt based, powerful TTS model.",id:"parler-tts/parler_tts_mini_v0.1"}],spaces:[{description:"An application for generate highly realistic, multilingual speech.",id:"suno/bark"},{description:"An application on XTTS, a voice generation model that lets you clone voices into different languages.",id:"coqui/xtts"},{description:"An application that generates speech in different styles in English and Chinese.",id:"mrfakename/E2-F5-TTS"},{description:"An application that synthesizes speech for diverse speaker prompts.",id:"parler-tts/parler_tts_mini"}],summary:"Text-to-Speech (TTS) is the task of generating natural sounding speech given text input. TTS models can be extended to have a single model that generates speech for multiple speakers and multiple languages.",widgetModels:["suno/bark"],youtubeId:"NW62DpzJ274"},_w=kw,Sw={datasets:[{description:"A widely used dataset useful to benchmark named entity recognition models.",id:"eriktks/conll2003"},{description:"A multilingual dataset of Wikipedia articles annotated for named entity recognition in over 150 different languages.",id:"unimelb-nlp/wikiann"}],demo:{inputs:[{label:"Input",content:"My name is Omar and I live in Zürich.",type:"text"}],outputs:[{text:"My name is Omar and I live in Zürich.",tokens:[{type:"PERSON",start:11,end:15},{type:"GPE",start:30,end:36}],type:"text-with-tokens"}]},metrics:[{description:"",id:"accuracy"},{description:"",id:"recall"},{description:"",id:"precision"},{description:"",id:"f1"}],models:[{description:"A robust performance model to identify people, locations, organizations and names of miscellaneous entities.",id:"dslim/bert-base-NER"},{description:"A strong model to identify people, locations, organizations and names in multiple languages.",id:"FacebookAI/xlm-roberta-large-finetuned-conll03-english"},{description:"A token classification model specialized on medical entity recognition.",id:"blaze999/Medical-NER"},{description:"Flair models are typically the state of the art in named entity recognition tasks.",id:"flair/ner-english"}],spaces:[{description:"An application that can recognizes entities, extracts noun chunks and recognizes various linguistic features of each token.",id:"spacy/gradio_pipeline_visualizer"}],summary:"Token classification is a natural language understanding task in which a label is assigned to some tokens in a text. Some popular token classification subtasks are Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging. NER models could be trained to identify specific entities in a text, such as dates, individuals and places; and PoS tagging would identify, for example, which words in a text are verbs, nouns, and punctuation marks.",widgetModels:["FacebookAI/xlm-roberta-large-finetuned-conll03-english"],youtubeId:"wVHdVlPScxA"},Ew=Sw,Tw={canonicalId:"text2text-generation",datasets:[{description:"A dataset of copyright-free books translated into 16 different languages.",id:"Helsinki-NLP/opus_books"},{description:"An example of translation between programming languages. This dataset consists of functions in Java and C#.",id:"google/code_x_glue_cc_code_to_code_trans"}],demo:{inputs:[{label:"Input",content:"My name is Omar and I live in Zürich.",type:"text"}],outputs:[{label:"Output",content:"Mein Name ist Omar und ich wohne in Zürich.",type:"text"}]},metrics:[{description:"BLEU score is calculated by counting the number of shared single or subsequent tokens between the generated sequence and the reference. Subsequent n tokens are called “n-grams”. Unigram refers to a single token while bi-gram refers to token pairs and n-grams refer to n subsequent tokens. The score ranges from 0 to 1, where 1 means the translation perfectly matched and 0 did not match at all",id:"bleu"},{description:"",id:"sacrebleu"}],models:[{description:"Very powerful model that can translate many languages between each other, especially low-resource languages.",id:"facebook/nllb-200-1.3B"},{description:"A general-purpose Transformer that can be used to translate from English to German, French, or Romanian.",id:"google-t5/t5-base"}],spaces:[{description:"An application that can translate between 100 languages.",id:"Iker/Translate-100-languages"},{description:"An application that can translate between many languages.",id:"Geonmo/nllb-translation-demo"}],summary:"Translation is the task of converting text from one language to another.",widgetModels:["facebook/mbart-large-50-many-to-many-mmt"],youtubeId:"1JvfrvZgi6c"},Cw=Tw,Aw={datasets:[{description:"A widely used dataset used to benchmark multiple variants of text classification.",id:"nyu-mll/glue"},{description:"A text classification dataset used to benchmark natural language inference models",id:"stanfordnlp/snli"}],demo:{inputs:[{label:"Input",content:"I love Hugging Face!",type:"text"}],outputs:[{type:"chart",data:[{label:"POSITIVE",score:.9},{label:"NEUTRAL",score:.1},{label:"NEGATIVE",score:0}]}]},metrics:[{description:"",id:"accuracy"},{description:"",id:"recall"},{description:"",id:"precision"},{description:"The F1 metric is the harmonic mean of the precision and recall. It can be calculated as: F1 = 2 * (precision * recall) / (precision + recall)",id:"f1"}],models:[{description:"A robust model trained for sentiment analysis.",id:"distilbert/distilbert-base-uncased-finetuned-sst-2-english"},{description:"A sentiment analysis model specialized in financial sentiment.",id:"ProsusAI/finbert"},{description:"A sentiment analysis model specialized in analyzing tweets.",id:"cardiffnlp/twitter-roberta-base-sentiment-latest"},{description:"A model that can classify languages.",id:"papluca/xlm-roberta-base-language-detection"},{description:"A model that can classify text generation attacks.",id:"meta-llama/Prompt-Guard-86M"}],spaces:[{description:"An application that can classify financial sentiment.",id:"IoannisTr/Tech_Stocks_Trading_Assistant"},{description:"A dashboard that contains various text classification tasks.",id:"miesnerjacob/Multi-task-NLP"},{description:"An application that analyzes user reviews in healthcare.",id:"spacy/healthsea-demo"}],summary:"Text Classification is the task of assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness.",widgetModels:["distilbert/distilbert-base-uncased-finetuned-sst-2-english"],youtubeId:"leNG9fN9FQU"},Iw=Aw,jw={datasets:[{description:"A large multilingual dataset of text crawled from the web.",id:"mc4"},{description:"Diverse open-source data consisting of 22 smaller high-quality datasets. It was used to train GPT-Neo.",id:"the_pile"},{description:"Truly open-source, curated and cleaned dialogue dataset.",id:"HuggingFaceH4/ultrachat_200k"},{description:"An instruction dataset with preference ratings on responses.",id:"openbmb/UltraFeedback"},{description:"A large synthetic dataset for alignment of text generation models.",id:"argilla/magpie-ultra-v0.1"}],demo:{inputs:[{label:"Input",content:"Once upon a time,",type:"text"}],outputs:[{label:"Output",content:"Once upon a time, we knew that our ancestors were on the verge of extinction. The great explorers and poets of the Old World, from Alexander the Great to Chaucer, are dead and gone. A good many of our ancient explorers and poets have",type:"text"}]},metrics:[{description:"Cross Entropy is a metric that calculates the difference between two probability distributions. Each probability distribution is the distribution of predicted words",id:"Cross Entropy"},{description:"The Perplexity metric is the exponential of the cross-entropy loss. It evaluates the probabilities assigned to the next word by the model. Lower perplexity indicates better performance",id:"Perplexity"}],models:[{description:"A text-generation model trained to follow instructions.",id:"google/gemma-2-2b-it"},{description:"Very powerful text generation model trained to follow instructions.",id:"meta-llama/Meta-Llama-3.1-8B-Instruct"},{description:"Small yet powerful text generation model.",id:"microsoft/Phi-3-mini-4k-instruct"},{description:"A very powerful model that can solve mathematical problems.",id:"AI-MO/NuminaMath-7B-TIR"},{description:"Strong text generation model to follow instructions.",id:"Qwen/Qwen2.5-7B-Instruct"},{description:"Very strong open-source large language model.",id:"nvidia/Llama-3.1-Nemotron-70B-Instruct"}],spaces:[{description:"A leaderboard to compare different open-source text generation models based on various benchmarks.",id:"open-llm-leaderboard/open_llm_leaderboard"},{description:"A leaderboard for comparing chain-of-thought performance of models.",id:"logikon/open_cot_leaderboard"},{description:"An text generation based application based on a very powerful LLaMA2 model.",id:"ysharma/Explore_llamav2_with_TGI"},{description:"An text generation based application to converse with Zephyr model.",id:"HuggingFaceH4/zephyr-chat"},{description:"A leaderboard that ranks text generation models based on blind votes from people.",id:"lmsys/chatbot-arena-leaderboard"},{description:"An chatbot to converse with a very powerful text generation model.",id:"mlabonne/phixtral-chat"}],summary:"Generating text is the task of generating new text given another text. These models can, for example, fill in incomplete text or paraphrase.",widgetModels:["mistralai/Mistral-Nemo-Instruct-2407"],youtubeId:"e9gNEAlsOvU"},Lw=jw,Pw={datasets:[{description:"Microsoft Research Video to Text is a large-scale dataset for open domain video captioning",id:"iejMac/CLIP-MSR-VTT"},{description:"UCF101 Human Actions dataset consists of 13,320 video clips from YouTube, with 101 classes.",id:"quchenyuan/UCF101-ZIP"},{description:"A high-quality dataset for human action recognition in YouTube videos.",id:"nateraw/kinetics"},{description:"A dataset of video clips of humans performing pre-defined basic actions with everyday objects.",id:"HuggingFaceM4/something_something_v2"},{description:"This dataset consists of text-video pairs and contains noisy samples with irrelevant video descriptions",id:"HuggingFaceM4/webvid"},{description:"A dataset of short Flickr videos for the temporal localization of events with descriptions.",id:"iejMac/CLIP-DiDeMo"}],demo:{inputs:[{label:"Input",content:"Darth Vader is surfing on the waves.",type:"text"}],outputs:[{filename:"text-to-video-output.gif",type:"img"}]},metrics:[{description:"Inception Score uses an image classification model that predicts class labels and evaluates how distinct and diverse the images are. A higher score indicates better video generation.",id:"is"},{description:"Frechet Inception Distance uses an image classification model to obtain image embeddings. The metric compares mean and standard deviation of the embeddings of real and generated images. A smaller score indicates better video generation.",id:"fid"},{description:"Frechet Video Distance uses a model that captures coherence for changes in frames and the quality of each frame. A smaller score indicates better video generation.",id:"fvd"},{description:"CLIPSIM measures similarity between video frames and text using an image-text similarity model. A higher score indicates better video generation.",id:"clipsim"}],models:[{description:"A strong model for consistent video generation.",id:"rain1011/pyramid-flow-sd3"},{description:"A robust model for text-to-video generation.",id:"VideoCrafter/VideoCrafter2"},{description:"A cutting-edge text-to-video generation model.",id:"TIGER-Lab/T2V-Turbo-V2"}],spaces:[{description:"An application that generates video from text.",id:"VideoCrafter/VideoCrafter"},{description:"Consistent video generation application.",id:"TIGER-Lab/T2V-Turbo-V2"},{description:"A cutting edge video generation application.",id:"Pyramid-Flow/pyramid-flow"}],summary:"Text-to-video models can be used in any application that requires generating consistent sequence of images from text. ",widgetModels:[],youtubeId:void 0},Rw=Pw,Dw={datasets:[{description:"The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images per class.",id:"cifar100"},{description:"Multiple images of celebrities, used for facial expression translation.",id:"CelebA"}],demo:{inputs:[{label:"Seed",content:"42",type:"text"},{label:"Number of images to generate:",content:"4",type:"text"}],outputs:[{filename:"unconditional-image-generation-output.jpeg",type:"img"}]},metrics:[{description:"The inception score (IS) evaluates the quality of generated images. It measures the diversity of the generated images (the model predictions are evenly distributed across all possible labels) and their 'distinction' or 'sharpness' (the model confidently predicts a single label for each image).",id:"Inception score (IS)"},{description:"The Fréchet Inception Distance (FID) evaluates the quality of images created by a generative model by calculating the distance between feature vectors for real and generated images.",id:"Frećhet Inception Distance (FID)"}],models:[{description:"High-quality image generation model trained on the CIFAR-10 dataset. It synthesizes images of the ten classes presented in the dataset using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics.",id:"google/ddpm-cifar10-32"},{description:"High-quality image generation model trained on the 256x256 CelebA-HQ dataset. It synthesizes images of faces using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics.",id:"google/ddpm-celebahq-256"}],spaces:[{description:"An application that can generate realistic faces.",id:"CompVis/celeba-latent-diffusion"}],summary:"Unconditional image generation is the task of generating images with no condition in any context (like a prompt text or another image). Once trained, the model will create images that resemble its training data distribution.",widgetModels:[""],youtubeId:""},Nw=Dw,Mw={datasets:[{description:"Benchmark dataset used for video classification with videos that belong to 400 classes.",id:"kinetics400"}],demo:{inputs:[{filename:"video-classification-input.gif",type:"img"}],outputs:[{type:"chart",data:[{label:"Playing Guitar",score:.514},{label:"Playing Tennis",score:.193},{label:"Cooking",score:.068}]}]},metrics:[{description:"",id:"accuracy"},{description:"",id:"recall"},{description:"",id:"precision"},{description:"",id:"f1"}],models:[{description:"Strong Video Classification model trained on the Kinetics 400 dataset.",id:"google/vivit-b-16x2-kinetics400"},{description:"Strong Video Classification model trained on the Kinetics 400 dataset.",id:"microsoft/xclip-base-patch32"}],spaces:[{description:"An application that classifies video at different timestamps.",id:"nateraw/lavila"},{description:"An application that classifies video.",id:"fcakyon/video-classification"}],summary:"Video classification is the task of assigning a label or class to an entire video. Videos are expected to have only one class for each video. Video classification models take a video as input and return a prediction about which class the video belongs to.",widgetModels:[],youtubeId:""},Ow=Mw,Fw={datasets:[{description:"A widely used dataset containing questions (with answers) about images.",id:"Graphcore/vqa"},{description:"A dataset to benchmark visual reasoning based on text in images.",id:"facebook/textvqa"}],demo:{inputs:[{filename:"elephant.jpeg",type:"img"},{label:"Question",content:"What is in this image?",type:"text"}],outputs:[{type:"chart",data:[{label:"elephant",score:.97},{label:"elephants",score:.06},{label:"animal",score:.003}]}]},isPlaceholder:!1,metrics:[{description:"",id:"accuracy"},{description:"Measures how much a predicted answer differs from the ground truth based on the difference in their semantic meaning.",id:"wu-palmer similarity"}],models:[{description:"A visual question answering model trained to convert charts and plots to text.",id:"google/deplot"},{description:"A visual question answering model trained for mathematical reasoning and chart derendering from images.",id:"google/matcha-base"},{description:"A strong visual question answering that answers questions from book covers.",id:"google/pix2struct-ocrvqa-large"}],spaces:[{description:"An application that compares visual question answering models across different tasks.",id:"merve/pix2struct"},{description:"An application that can answer questions based on images.",id:"nielsr/vilt-vqa"},{description:"An application that can caption images and answer questions about a given image. ",id:"Salesforce/BLIP"},{description:"An application that can caption images and answer questions about a given image. ",id:"vumichien/Img2Prompt"}],summary:"Visual Question Answering is the task of answering open-ended questions based on an image. They output natural language responses to natural language questions.",widgetModels:["dandelin/vilt-b32-finetuned-vqa"],youtubeId:""},Uw=Fw,zw={datasets:[{description:"A widely used dataset used to benchmark multiple variants of text classification.",id:"nyu-mll/glue"},{description:"The Multi-Genre Natural Language Inference (MultiNLI) corpus is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information.",id:"nyu-mll/multi_nli"},{description:"FEVER is a publicly available dataset for fact extraction and verification against textual sources.",id:"fever/fever"}],demo:{inputs:[{label:"Text Input",content:"Dune is the best movie ever.",type:"text"},{label:"Candidate Labels",content:"CINEMA, ART, MUSIC",type:"text"}],outputs:[{type:"chart",data:[{label:"CINEMA",score:.9},{label:"ART",score:.1},{label:"MUSIC",score:0}]}]},metrics:[],models:[{description:"Powerful zero-shot text classification model.",id:"facebook/bart-large-mnli"},{description:"Powerful zero-shot multilingual text classification model that can accomplish multiple tasks.",id:"MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7"}],spaces:[],summary:"Zero-shot text classification is a task in natural language processing where a model is trained on a set of labeled examples but is then able to classify new examples from previously unseen classes.",widgetModels:["facebook/bart-large-mnli"]},$w=zw,Bw={datasets:[{description:"",id:""}],demo:{inputs:[{filename:"image-classification-input.jpeg",type:"img"},{label:"Classes",content:"cat, dog, bird",type:"text"}],outputs:[{type:"chart",data:[{label:"Cat",score:.664},{label:"Dog",score:.329},{label:"Bird",score:.008}]}]},metrics:[{description:"Computes the number of times the correct label appears in top K labels predicted",id:"top-K accuracy"}],models:[{description:"Robust image classification model trained on publicly available image-caption data.",id:"openai/clip-vit-base-patch16"},{description:"Strong zero-shot image classification model.",id:"google/siglip-so400m-patch14-224"},{description:"Small yet powerful zero-shot image classification model that can run on edge devices.",id:"apple/MobileCLIP-S1-OpenCLIP"},{description:"Strong image classification model for biomedical domain.",id:"microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224"}],spaces:[{description:"An application that leverages zero-shot image classification to find best captions to generate an image. ",id:"pharma/CLIP-Interrogator"},{description:"An application to compare different zero-shot image classification models. ",id:"merve/compare_clip_siglip"}],summary:"Zero-shot image classification is the task of classifying previously unseen classes during training of a model.",widgetModels:["google/siglip-so400m-patch14-224"],youtubeId:""},Vw=Bw,Hw={datasets:[],demo:{inputs:[{filename:"zero-shot-object-detection-input.jpg",type:"img"},{label:"Classes",content:"cat, dog, bird",type:"text"}],outputs:[{filename:"zero-shot-object-detection-output.jpg",type:"img"}]},metrics:[{description:"The Average Precision (AP) metric is the Area Under the PR Curve (AUC-PR). It is calculated for each class separately",id:"Average Precision"},{description:"The Mean Average Precision (mAP) metric is the overall average of the AP values",id:"Mean Average Precision"},{description:"The APα metric is the Average Precision at the IoU threshold of a α value, for example, AP50 and AP75",id:"APα"}],models:[{description:"Solid zero-shot object detection model.",id:"IDEA-Research/grounding-dino-base"},{description:"Cutting-edge zero-shot object detection model.",id:"google/owlv2-base-patch16-ensemble"}],spaces:[{description:"A demo to try the state-of-the-art zero-shot object detection model, OWLv2.",id:"merve/owlv2"},{description:"A demo that combines a zero-shot object detection and mask generation model for zero-shot segmentation.",id:"merve/OWLSAM"}],summary:"Zero-shot object detection is a computer vision task to detect objects and their classes in images, without any prior training or knowledge of the classes. Zero-shot object detection models receive an image as input, as well as a list of candidate classes, and output the bounding boxes and labels where the objects have been detected.",widgetModels:[],youtubeId:""},qw=Hw,Ww={datasets:[{description:"A large dataset of over 10 million 3D objects.",id:"allenai/objaverse-xl"},{description:"A dataset of isolated object images for evaluating image-to-3D models.",id:"dylanebert/iso3d"}],demo:{inputs:[{filename:"image-to-3d-image-input.png",type:"img"}],outputs:[{label:"Result",content:"image-to-3d-3d-output-filename.glb",type:"text"}]},metrics:[],models:[{description:"Fast image-to-3D mesh model by Tencent.",id:"TencentARC/InstantMesh"},{description:"Fast image-to-3D mesh model by StabilityAI",id:"stabilityai/TripoSR"},{description:"A scaled up image-to-3D mesh model derived from TripoSR.",id:"hwjiang/Real3D"},{description:"Generative 3D gaussian splatting model.",id:"ashawkey/LGM"}],spaces:[{description:"Leaderboard to evaluate image-to-3D models.",id:"dylanebert/3d-arena"},{description:"Image-to-3D demo with mesh outputs.",id:"TencentARC/InstantMesh"},{description:"Image-to-3D demo with mesh outputs.",id:"stabilityai/TripoSR"},{description:"Image-to-3D demo with mesh outputs.",id:"hwjiang/Real3D"},{description:"Image-to-3D demo with splat outputs.",id:"dylanebert/LGM-mini"}],summary:"Image-to-3D models take in image input and produce 3D output.",widgetModels:[],youtubeId:""},Kw=Ww,Qw={datasets:[{description:"A large dataset of over 10 million 3D objects.",id:"allenai/objaverse-xl"},{description:"Descriptive captions for 3D objects in Objaverse.",id:"tiange/Cap3D"}],demo:{inputs:[{label:"Prompt",content:"a cat statue",type:"text"}],outputs:[{label:"Result",content:"text-to-3d-3d-output-filename.glb",type:"text"}]},metrics:[],models:[{description:"Text-to-3D mesh model by OpenAI",id:"openai/shap-e"},{description:"Generative 3D gaussian splatting model.",id:"ashawkey/LGM"}],spaces:[{description:"Text-to-3D demo with mesh outputs.",id:"hysts/Shap-E"},{description:"Text/image-to-3D demo with splat outputs.",id:"ashawkey/LGM"}],summary:"Text-to-3D models take in text input and produce 3D output.",widgetModels:[],youtubeId:""},Xw=Qw,Yw={datasets:[{description:"A dataset of hand keypoints of over 500k examples.",id:"Vincent-luo/hagrid-mediapipe-hands"}],demo:{inputs:[{filename:"keypoint-detection-input.png",type:"img"}],outputs:[{filename:"keypoint-detection-output.png",type:"img"}]},metrics:[],models:[{description:"A robust keypoint detection model.",id:"magic-leap-community/superpoint"},{description:"Strong keypoint detection model used to detect human pose.",id:"facebook/sapiens-pose-1b"}],spaces:[{description:"An application that detects hand keypoints in real-time.",id:"datasciencedojo/Hand-Keypoint-Detection-Realtime"},{description:"An application to try a universal keypoint detection model.",id:"merve/SuperPoint"}],summary:"Keypoint detection is the task of identifying meaningful distinctive points or features in an image.",widgetModels:[],youtubeId:""},Jw=Yw,Zw={datasets:[{description:"Multiple-choice questions and answers about videos.",id:"lmms-lab/Video-MME"},{description:"A dataset of instructions and question-answer pairs about videos.",id:"lmms-lab/VideoChatGPT"},{description:"Large video understanding dataset.",id:"HuggingFaceFV/finevideo"}],demo:{inputs:[{filename:"video-text-to-text-input.gif",type:"img"},{label:"Text Prompt",content:"What is happening in this video?",type:"text"}],outputs:[{label:"Answer",content:"The video shows a series of images showing a fountain with water jets and a variety of colorful flowers and butterflies in the background.",type:"text"}]},metrics:[],models:[{description:"A robust video-text-to-text model that can take in image and video inputs.",id:"llava-hf/llava-onevision-qwen2-72b-ov-hf"},{description:"Large and powerful video-text-to-text model that can take in image and video inputs.",id:"llava-hf/LLaVA-NeXT-Video-34B-hf"}],spaces:[{description:"An application to chat with a video-text-to-text model.",id:"llava-hf/video-llava"},{description:"A leaderboard for various video-text-to-text models.",id:"opencompass/openvlm_video_leaderboard"}],summary:"Video-text-to-text models take in a video and a text prompt and output text. These models are also called video-language models.",widgetModels:[""],youtubeId:""},Gw=Zw,ex={"audio-classification":["speechbrain","transformers","transformers.js"],"audio-to-audio":["asteroid","fairseq","speechbrain"],"automatic-speech-recognition":["espnet","nemo","speechbrain","transformers","transformers.js"],"depth-estimation":["transformers","transformers.js"],"document-question-answering":["transformers","transformers.js"],"feature-extraction":["sentence-transformers","transformers","transformers.js"],"fill-mask":["transformers","transformers.js"],"graph-ml":["transformers"],"image-classification":["keras","timm","transformers","transformers.js"],"image-feature-extraction":["timm","transformers"],"image-segmentation":["transformers","transformers.js"],"image-text-to-text":["transformers"],"image-to-image":["diffusers","transformers","transformers.js"],"image-to-text":["transformers","transformers.js"],"image-to-video":["diffusers"],"keypoint-detection":["transformers"],"video-classification":["transformers"],"mask-generation":["transformers"],"multiple-choice":["transformers"],"object-detection":["transformers","transformers.js"],other:[],"question-answering":["adapter-transformers","allennlp","transformers","transformers.js"],robotics:[],"reinforcement-learning":["transformers","stable-baselines3","ml-agents","sample-factory"],"sentence-similarity":["sentence-transformers","spacy","transformers.js"],summarization:["transformers","transformers.js"],"table-question-answering":["transformers"],"table-to-text":["transformers"],"tabular-classification":["sklearn"],"tabular-regression":["sklearn"],"tabular-to-text":["transformers"],"text-classification":["adapter-transformers","setfit","spacy","transformers","transformers.js"],"text-generation":["transformers","transformers.js"],"text-retrieval":[],"text-to-image":["diffusers"],"text-to-speech":["espnet","tensorflowtts","transformers","transformers.js"],"text-to-audio":["transformers","transformers.js"],"text-to-video":["diffusers"],"text2text-generation":["transformers","transformers.js"],"time-series-forecasting":[],"token-classification":["adapter-transformers","flair","spacy","span-marker","stanza","transformers","transformers.js"],translation:["transformers","transformers.js"],"unconditional-image-generation":["diffusers"],"video-text-to-text":["transformers"],"visual-question-answering":["transformers","transformers.js"],"voice-activity-detection":[],"zero-shot-classification":["transformers","transformers.js"],"zero-shot-image-classification":["transformers","transformers.js"],"zero-shot-object-detection":["transformers","transformers.js"],"text-to-3d":["diffusers"],"image-to-3d":["diffusers"],"any-to-any":["transformers"]};function K(e,t=zp){return{...t,id:e,label:su[e].name,libraries:ex[e]}}K("any-to-any",zp),K("audio-classification",j0),K("audio-to-audio",P0),K("automatic-speech-recognition",D0),K("depth-estimation",rw),K("document-question-answering",M0),K("feature-extraction",F0),K("fill-mask",z0),K("image-classification",B0),K("image-feature-extraction",H0),K("image-segmentation",Z0),K("image-to-image",W0),K("image-text-to-text",Y0),K("image-to-text",Q0),K("keypoint-detection",Jw),K("mask-generation",ew),K("object-detection",nw),K("video-classification",Ow),K("question-answering",uw),K("reinforcement-learning",sw),K("sentence-similarity",dw),K("summarization",pw),K("table-question-answering",hw),K("tabular-classification",yw),K("tabular-regression",ww),K("text-classification",Iw),K("text-generation",Lw),K("text-to-image",bw),K("text-to-speech",_w),K("text-to-video",Rw),K("token-classification",Ew),K("translation",Cw),K("unconditional-image-generation",Nw),K("video-text-to-text",Gw),K("visual-question-answering",Uw),K("zero-shot-classification",$w),K("zero-shot-image-classification",Vw),K("zero-shot-object-detection",qw),K("text-to-3d",Xw),K("image-to-3d",Kw);var $p="custom_code";function fn(e){const t=e.split("/");return t.length===1?t[0]:t[1]}var tx=e=>JSON.stringify(e).slice(1,-1),nx=e=>{var t,n;return[`from adapters import AutoAdapterModel

model = AutoAdapterModel.from_pretrained("${(n=(t=e.config)==null?void 0:t.adapter_transformers)==null?void 0:n.model_name}")
model.load_adapter("${e.id}", set_active=True)`]},ix=e=>[`import allennlp_models
from allennlp.predictors.predictor import Predictor

predictor = Predictor.from_path("hf://${e.id}")`],rx=e=>[`import allennlp_models
from allennlp.predictors.predictor import Predictor

predictor = Predictor.from_path("hf://${e.id}")
predictor_input = {"passage": "My name is Wolfgang and I live in Berlin", "question": "Where do I live?"}
predictions = predictor.predict_json(predictor_input)`],ax=e=>e.tags.includes("question-answering")?rx(e):ix(e),ox=e=>[`from asteroid.models import BaseModel

model = BaseModel.from_pretrained("${e.id}")`],sx=e=>{const t=`# Watermark Generator
from audioseal import AudioSeal

model = AudioSeal.load_generator("${e.id}")
# pass a tensor (tensor_wav) of shape (batch, channels, samples) and a sample rate
wav, sr = tensor_wav, 16000
	
watermark = model.get_watermark(wav, sr)
watermarked_audio = wav + watermark`,n=`# Watermark Detector
from audioseal import AudioSeal

detector = AudioSeal.load_detector("${e.id}")
	
result, message = detector.detect_watermark(watermarked_audio, sr)`;return[t,n]};function lu(e){var t,n;return((n=(t=e.cardData)==null?void 0:t.base_model)==null?void 0:n.toString())??"fill-in-base-model"}function Bp(e){var n,i,r;const t=((i=(n=e.widgetData)==null?void 0:n[0])==null?void 0:i.text)??((r=e.cardData)==null?void 0:r.instance_prompt);if(t)return tx(t)}var lx=e=>[`from bertopic import BERTopic

model = BERTopic.load("${e.id}")`],ux=e=>[`from bm25s.hf import BM25HF

retriever = BM25HF.load_from_hub("${e.id}")`],cx=e=>{let t,n,i;return t="<ENCODER>",n="<NUMBER_OF_FEATURES>",i="<OUT_CHANNELS>",e.id==="depth-anything/Depth-Anything-V2-Small"?(t="vits",n="64",i="[48, 96, 192, 384]"):e.id==="depth-anything/Depth-Anything-V2-Base"?(t="vitb",n="128",i="[96, 192, 384, 768]"):e.id==="depth-anything/Depth-Anything-V2-Large"&&(t="vitl",n="256",i="[256, 512, 1024, 1024"),[`
# Install from https://github.com/DepthAnything/Depth-Anything-V2

# Load the model and infer depth from an image
import cv2
import torch

from depth_anything_v2.dpt import DepthAnythingV2

# instantiate the model
model = DepthAnythingV2(encoder="${t}", features=${n}, out_channels=${i})

# load the weights
filepath = hf_hub_download(repo_id="${e.id}", filename="depth_anything_v2_${t}.pth", repo_type="model")
state_dict = torch.load(filepath, map_location="cpu")
model.load_state_dict(state_dict).eval()

raw_img = cv2.imread("your/image/path")
depth = model.infer_image(raw_img) # HxW raw depth map in numpy
    `]},dx=e=>[`# Download checkpoint
pip install huggingface-hub
huggingface-cli download --local-dir checkpoints ${e.id}`,`import depth_pro

# Load model and preprocessing transform
model, transform = depth_pro.create_model_and_transforms()
model.eval()

# Load and preprocess an image.
image, _, f_px = depth_pro.load_rgb("example.png")
image = transform(image)

# Run inference.
prediction = model.infer(image, f_px=f_px)

# Results: 1. Depth in meters
depth = prediction["depth"]
# Results: 2. Focal length in pixels
focallength_px = prediction["focallength_px"]`],Vp="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",fx=e=>[`from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained("${e.id}")

prompt = "${Bp(e)??Vp}"
image = pipe(prompt).images[0]`],px=e=>[`from diffusers import ControlNetModel, StableDiffusionControlNetPipeline

controlnet = ControlNetModel.from_pretrained("${e.id}")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"${lu(e)}", controlnet=controlnet
)`],mx=e=>[`from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained("${lu(e)}")
pipe.load_lora_weights("${e.id}")

prompt = "${Bp(e)??Vp}"
image = pipe(prompt).images[0]`],hx=e=>[`from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained("${lu(e)}")
pipe.load_textual_inversion("${e.id}")`],gx=e=>e.tags.includes("controlnet")?px(e):e.tags.includes("lora")?mx(e):e.tags.includes("textual_inversion")?hx(e):fx(e),yx=e=>{const t=`# Pipeline for Stable Diffusion 3
from diffusionkit.mlx import DiffusionPipeline

pipeline = DiffusionPipeline(
	shift=3.0,
	use_t5=False,
	model_version=${e.id},
	low_memory_mode=True,
	a16=True,
	w16=True,
)`,n=`# Pipeline for Flux
from diffusionkit.mlx import FluxPipeline

pipeline = FluxPipeline(
  shift=1.0,
  model_version=${e.id},
  low_memory_mode=True,
  a16=True,
  w16=True,
)`,i=`# Image Generation
HEIGHT = 512
WIDTH = 512
NUM_STEPS = ${e.tags.includes("flux")?4:50}
CFG_WEIGHT = ${e.tags.includes("flux")?0:5}

image, _ = pipeline.generate_image(
  "a photo of a cat",
  cfg_weight=CFG_WEIGHT,
  num_steps=NUM_STEPS,
  latent_size=(HEIGHT // 8, WIDTH // 8),
)`;return[e.tags.includes("flux")?n:t,i]},vx=e=>[`# pip install --no-binary :all: cartesia-pytorch
from cartesia_pytorch import ReneLMHeadModel
from transformers import AutoTokenizer

model = ReneLMHeadModel.from_pretrained("${e.id}")
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-1B-hf")

in_message = ["Rene Descartes was"]
inputs = tokenizer(in_message, return_tensors="pt")

outputs = model.generate(inputs.input_ids, max_length=50, top_k=100, top_p=0.99)
out_message = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]

print(out_message)
)`],wx=e=>[`import mlx.core as mx
import cartesia_mlx as cmx

model = cmx.from_pretrained("${e.id}")
model.set_dtype(mx.float32)   

prompt = "Rene Descartes was"

for text in model.generate(
    prompt,
    max_tokens=500,
    eval_every_n=5,
    verbose=True,
    top_p=0.99,
    temperature=0.85,
):
    print(text, end="", flush=True)
`],xx=e=>{const t=fn(e.id).replaceAll("-","_");return[`# Load it from the Hub directly
import edsnlp
nlp = edsnlp.load("${e.id}")
`,`# Or install it as a package
!pip install git+https://huggingface.co/${e.id}

# and import it as a module
import ${t}

nlp = ${t}.load()  # or edsnlp.load("${t}")
`]},bx=e=>[`from espnet2.bin.tts_inference import Text2Speech

model = Text2Speech.from_pretrained("${e.id}")

speech, *_ = model("text to generate speech from")`],kx=e=>[`from espnet2.bin.asr_inference import Speech2Text

model = Speech2Text.from_pretrained(
  "${e.id}"
)

speech, rate = soundfile.read("speech.wav")
text, *_ = model(speech)[0]`],_x=()=>["unknown model type (must be text-to-speech or automatic-speech-recognition)"],Sx=e=>e.tags.includes("text-to-speech")?bx(e):e.tags.includes("automatic-speech-recognition")?kx(e):_x(),Ex=e=>[`from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub

models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
    "${e.id}"
)`],Tx=e=>[`from flair.models import SequenceTagger

tagger = SequenceTagger.load("${e.id}")`],Cx=e=>[`from gliner import GLiNER

model = GLiNER.from_pretrained("${e.id}")`],Ax=e=>[`# CLI usage
# see docs: https://ai-riksarkivet.github.io/htrflow/latest/getting_started/quick_start.html
htrflow pipeline <path/to/pipeline.yaml> <path/to/image>`,`# Python usage
from htrflow.pipeline.pipeline import Pipeline
from htrflow.pipeline.steps import Task
from htrflow.models.framework.model import ModelClass

pipeline = Pipeline(
    [
        Task(
            ModelClass, {"model": "${e.id}"}, {}
        ),
    ])`],Ix=e=>[`# Available backend options are: "jax", "torch", "tensorflow".
import os
os.environ["KERAS_BACKEND"] = "jax"
	
import keras

model = keras.saving.load_model("hf://${e.id}")
`],jx=e=>[`# Available backend options are: "jax", "torch", "tensorflow".
import os
os.environ["KERAS_BACKEND"] = "jax"

import keras_nlp

tokenizer = keras_nlp.models.Tokenizer.from_preset("hf://${e.id}")
backbone = keras_nlp.models.Backbone.from_preset("hf://${e.id}")
`],Lx=e=>[`# Available backend options are: "jax", "torch", "tensorflow".
import os
os.environ["KERAS_BACKEND"] = "jax"

import keras_hub

# Load a task-specific model (*replace CausalLM with your task*)
model = keras_hub.models.CausalLM.from_preset("hf://${e.id}", dtype="bfloat16")

# Possible tasks are CausalLM, TextToImage, ImageClassifier, ...
# full list here: https://keras.io/api/keras_hub/models/#api-documentation
`],Px=e=>[`from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="${e.id}",
	filename="{{GGUF_FILE}}",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)`],Rx=e=>[`# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy)
# See https://github.com/keras-team/tf-keras for more details.
from huggingface_hub import from_pretrained_keras

model = from_pretrained_keras("${e.id}")
`],Dx=e=>[`from mamba_ssm import MambaLMHeadModel

model = MambaLMHeadModel.from_pretrained("${e.id}")`],Nx=e=>[`# Install from https://github.com/Camb-ai/MARS5-TTS

from inference import Mars5TTS
mars5 = Mars5TTS.from_pretrained("${e.id}")`],Mx=()=>[`# Install from https://github.com/buaacyw/MeshAnything.git

from MeshAnything.models.meshanything import MeshAnything

# refer to https://github.com/buaacyw/MeshAnything/blob/main/main.py#L91 on how to define args
# and https://github.com/buaacyw/MeshAnything/blob/main/app.py regarding usage
model = MeshAnything(args)`],Ox=e=>[`import open_clip

model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:${e.id}')
tokenizer = open_clip.get_tokenizer('hf-hub:${e.id}')`],Fx=e=>{var t,n;if((n=(t=e.config)==null?void 0:t.architectures)!=null&&n[0]){const i=e.config.architectures[0];return[[`from paddlenlp.transformers import AutoTokenizer, ${i}`,"",`tokenizer = AutoTokenizer.from_pretrained("${e.id}", from_hf_hub=True)`,`model = ${i}.from_pretrained("${e.id}", from_hf_hub=True)`].join(`
`)]}else return[["# ⚠️ Type of model unknown","from paddlenlp.transformers import AutoTokenizer, AutoModel","",`tokenizer = AutoTokenizer.from_pretrained("${e.id}", from_hf_hub=True)`,`model = AutoModel.from_pretrained("${e.id}", from_hf_hub=True)`].join(`
`)]},Ux=e=>[`from pyannote.audio import Pipeline
  
pipeline = Pipeline.from_pretrained("${e.id}")

# inference on the whole file
pipeline("file.wav")

# inference on an excerpt
from pyannote.core import Segment
excerpt = Segment(start=2.0, end=5.0)

from pyannote.audio import Audio
waveform, sample_rate = Audio().crop("file.wav", excerpt)
pipeline({"waveform": waveform, "sample_rate": sample_rate})`],zx=e=>[`from pyannote.audio import Model, Inference

model = Model.from_pretrained("${e.id}")
inference = Inference(model)

# inference on the whole file
inference("file.wav")

# inference on an excerpt
from pyannote.core import Segment
excerpt = Segment(start=2.0, end=5.0)
inference.crop("file.wav", excerpt)`],$x=e=>e.tags.includes("pyannote-audio-pipeline")?Ux(e):zx(e),Bx=e=>[`from relik import Relik
 
relik = Relik.from_pretrained("${e.id}")`],Vx=e=>[`from tensorflow_tts.inference import AutoProcessor, TFAutoModel

processor = AutoProcessor.from_pretrained("${e.id}")
model = TFAutoModel.from_pretrained("${e.id}")
`],Hx=e=>[`from tensorflow_tts.inference import TFAutoModel

model = TFAutoModel.from_pretrained("${e.id}")
audios = model.inference(mels)
`],qx=e=>[`from tensorflow_tts.inference import TFAutoModel

model = TFAutoModel.from_pretrained("${e.id}")
`],Wx=e=>e.tags.includes("text-to-mel")?Vx(e):e.tags.includes("mel-to-wav")?Hx(e):qx(e),Kx=e=>[`import timm

model = timm.create_model("hf_hub:${e.id}", pretrained=True)`],Qx=()=>[`# pip install sae-lens
from sae_lens import SAE

sae, cfg_dict, sparsity = SAE.from_pretrained(
    release = "RELEASE_ID", # e.g., "gpt2-small-res-jb". See other options in https://github.com/jbloomAus/SAELens/blob/main/sae_lens/pretrained_saes.yaml
    sae_id = "SAE_ID", # e.g., "blocks.8.hook_resid_pre". Won't always be a hook point
)`],Xx=()=>[`# seed_story_cfg_path refers to 'https://github.com/TencentARC/SEED-Story/blob/master/configs/clm_models/agent_7b_sft.yaml'
# llm_cfg_path refers to 'https://github.com/TencentARC/SEED-Story/blob/master/configs/clm_models/llama2chat7b_lora.yaml'
from omegaconf import OmegaConf
import hydra

# load Llama2
llm_cfg = OmegaConf.load(llm_cfg_path)
llm = hydra.utils.instantiate(llm_cfg, torch_dtype="fp16")

# initialize seed_story
seed_story_cfg = OmegaConf.load(seed_story_cfg_path)
seed_story = hydra.utils.instantiate(seed_story_cfg, llm=llm) `],Yx=(e,t)=>[`import joblib
from skops.hub_utils import download
download("${e.id}", "path_to_folder")
model = joblib.load(
	"${t}"
)
# only load pickle files from sources you trust
# read more about it here https://skops.readthedocs.io/en/stable/persistence.html`],Jx=(e,t)=>[`from skops.hub_utils import download
from skops.io import load
download("${e.id}", "path_to_folder")
# make sure model file is in skops format
# if model is a pickle file, make sure it's from a source you trust
model = load("path_to_folder/${t}")`],Zx=e=>[`from huggingface_hub import hf_hub_download
import joblib
model = joblib.load(
	hf_hub_download("${e.id}", "sklearn_model.joblib")
)
# only load pickle files from sources you trust
# read more about it here https://skops.readthedocs.io/en/stable/persistence.html`],Gx=e=>{var t,n,i,r,a;if(e.tags.includes("skops")){const o=(i=(n=(t=e.config)==null?void 0:t.sklearn)==null?void 0:n.model)==null?void 0:i.file,s=(a=(r=e.config)==null?void 0:r.sklearn)==null?void 0:a.model_format;return o?s==="pickle"?Yx(e,o):Jx(e,o):["# ⚠️ Model filename not specified in config.json"]}else return Zx(e)},eb=e=>[`import torch
import torchaudio
from einops import rearrange
from stable_audio_tools import get_pretrained_model
from stable_audio_tools.inference.generation import generate_diffusion_cond

device = "cuda" if torch.cuda.is_available() else "cpu"

# Download model
model, model_config = get_pretrained_model("${e.id}")
sample_rate = model_config["sample_rate"]
sample_size = model_config["sample_size"]

model = model.to(device)

# Set up text and timing conditioning
conditioning = [{
	"prompt": "128 BPM tech house drum loop",
}]

# Generate stereo audio
output = generate_diffusion_cond(
	model,
	conditioning=conditioning,
	sample_size=sample_size,
	device=device
)

# Rearrange audio batch to a single sequence
output = rearrange(output, "b d n -> d (b n)")

# Peak normalize, clip, convert to int16, and save to file
output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
torchaudio.save("output.wav", output, sample_rate)`],tb=e=>[`from huggingface_hub import from_pretrained_fastai

learn = from_pretrained_fastai("${e.id}")`],nb=e=>{const t=`# Use SAM2 with images
import torch
from sam2.sam2_image_predictor import SAM2ImagePredictor

predictor = SAM2ImagePredictor.from_pretrained(${e.id})

with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
    predictor.set_image(<your_image>)
    masks, _, _ = predictor.predict(<input_prompts>)`,n=`# Use SAM2 with videos
import torch
from sam2.sam2_video_predictor import SAM2VideoPredictor
	
predictor = SAM2VideoPredictor.from_pretrained(${e.id})

with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
    state = predictor.init_state(<your_video>)

    # add new prompts and instantly get the output on the same frame
    frame_idx, object_ids, masks = predictor.add_new_points(state, <your_prompts>):

    # propagate the prompts to get masklets throughout the video
    for frame_idx, object_ids, masks in predictor.propagate_in_video(state):
        ...`;return[t,n]},ib=e=>[`python -m sample_factory.huggingface.load_from_hub -r ${e.id} -d ./train_dir`];function rb(e){var n;const t=(n=e.widgetData)==null?void 0:n[0];if(t)return[t.source_sentence,...t.sentences]}var ab=e=>{const t=e.tags.includes($p)?", trust_remote_code=True":"",n=rb(e)??["The weather is lovely today.","It's so sunny outside!","He drove to the stadium."];return[`from sentence_transformers import SentenceTransformer

model = SentenceTransformer("${e.id}"${t})

sentences = ${JSON.stringify(n,null,4)}
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [${n.length}, ${n.length}]`]},ob=e=>[`from setfit import SetFitModel

model = SetFitModel.from_pretrained("${e.id}")`],sb=e=>[`!pip install https://huggingface.co/${e.id}/resolve/main/${fn(e.id)}-any-py3-none-any.whl

# Using spacy.load().
import spacy
nlp = spacy.load("${fn(e.id)}")

# Importing as module.
import ${fn(e.id)}
nlp = ${fn(e.id)}.load()`],lb=e=>[`from span_marker import SpanMarkerModel

model = SpanMarkerModel.from_pretrained("${e.id}")`],ub=e=>[`import stanza

stanza.download("${fn(e.id).replace("stanza-","")}")
nlp = stanza.Pipeline("${fn(e.id).replace("stanza-","")}")`],cb=e=>{switch(e){case"EncoderClassifier":return"classify_file";case"EncoderDecoderASR":case"EncoderASR":return"transcribe_file";case"SpectralMaskEnhancement":return"enhance_file";case"SepformerSeparation":return"separate_file";default:return}},db=e=>{var i,r;const t=(r=(i=e.config)==null?void 0:i.speechbrain)==null?void 0:r.speechbrain_interface;if(t===void 0)return["# interface not specified in config.json"];const n=cb(t);return n===void 0?["# interface in config.json invalid"]:[`from speechbrain.pretrained import ${t}
model = ${t}.from_hparams(
  "${e.id}"
)
model.${n}("file.wav")`]},fb=e=>{var r,a,o,s,l;const t=e.transformersInfo;if(!t)return["# ⚠️ Type of model unknown"];const n=e.tags.includes($p)?", trust_remote_code=True":"";let i;if(t.processor){const u=t.processor==="AutoTokenizer"?"tokenizer":t.processor==="AutoFeatureExtractor"?"extractor":"processor";i=["# Load model directly",`from transformers import ${t.processor}, ${t.auto_model}`,"",`${u} = ${t.processor}.from_pretrained("${e.id}"`+n+")",`model = ${t.auto_model}.from_pretrained("${e.id}"`+n+")"].join(`
`)}else i=["# Load model directly",`from transformers import ${t.auto_model}`,`model = ${t.auto_model}.from_pretrained("${e.id}"`+n+")"].join(`
`);if(e.pipeline_tag&&((r=C0.transformers)!=null&&r.includes(e.pipeline_tag))){const u=["# Use a pipeline as a high-level helper","from transformers import pipeline",""];return e.tags.includes("conversational")&&((o=(a=e.config)==null?void 0:a.tokenizer_config)!=null&&o.chat_template)&&u.push("messages = [",'    {"role": "user", "content": "Who are you?"},',"]"),u.push(`pipe = pipeline("${e.pipeline_tag}", model="${e.id}"`+n+")"),e.tags.includes("conversational")&&((l=(s=e.config)==null?void 0:s.tokenizer_config)!=null&&l.chat_template)&&u.push("pipe(messages)"),[u.join(`
`),i]}return[i]},pb=e=>{if(!e.pipeline_tag)return["// ⚠️ Unknown pipeline tag"];const t="@huggingface/transformers";return[`// npm i ${t}
import { pipeline } from '${t}';

// Allocate pipeline
const pipe = await pipeline('${e.pipeline_tag}', '${e.id}');`]},mb=e=>{switch(e){case"CAUSAL_LM":return"CausalLM";case"SEQ_2_SEQ_LM":return"Seq2SeqLM";case"TOKEN_CLS":return"TokenClassification";case"SEQ_CLS":return"SequenceClassification";default:return}},hb=e=>{var r;const{base_model_name_or_path:t,task_type:n}=((r=e.config)==null?void 0:r.peft)??{},i=mb(n);return i?t?[`from peft import PeftModel, PeftConfig
from transformers import AutoModelFor${i}

config = PeftConfig.from_pretrained("${e.id}")
base_model = AutoModelFor${i}.from_pretrained("${t}")
model = PeftModel.from_pretrained(base_model, "${e.id}")`]:["Base model is not found."]:["Task type is invalid."]},gb=e=>[`from huggingface_hub import hf_hub_download
import fasttext

model = fasttext.load_model(hf_hub_download("${e.id}", "model.bin"))`],yb=e=>[`from huggingface_sb3 import load_from_hub
checkpoint = load_from_hub(
	repo_id="${e.id}",
	filename="{MODEL FILENAME}.zip",
)`],vb=(e,t)=>{switch(e){case"ASR":return[`import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained("${t.id}")

transcriptions = asr_model.transcribe(["file.wav"])`];default:return}},wb=e=>[`mlagents-load-from-hf --repo-id="${e.id}" --local-dir="./download: string[]s"`],xb=()=>[`string modelName = "[Your model name here].sentis";
Model model = ModelLoader.Load(Application.streamingAssetsPath + "/" + modelName);
IWorker engine = WorkerFactory.CreateWorker(BackendType.GPUCompute, model);
// Please see provided C# file for more details
`],bb=e=>[`from Trainer_finetune import Model

model = Model.from_pretrained("${e.id}")`],kb=e=>[`from voicecraft import VoiceCraft

model = VoiceCraft.from_pretrained("${e.id}")`],_b=()=>[`import ChatTTS
import torchaudio

chat = ChatTTS.Chat()
chat.load_models(compile=False) # Set to True for better performance

texts = ["PUT YOUR TEXT HERE",]

wavs = chat.infer(texts, )

torchaudio.save("output1.wav", torch.from_numpy(wavs[0]), 24000)`],Sb=e=>[`from ultralytics import YOLOv10

model = YOLOv10.from_pretrained("${e.id}")
source = 'http://images.cocodataset.org/val2017/000000039769.jpg'
model.predict(source=source, save=True)
`],Eb=e=>[`# Option 1: use with transformers

from transformers import AutoModelForImageSegmentation
birefnet = AutoModelForImageSegmentation.from_pretrained("${e.id}", trust_remote_code=True)
`,`# Option 2: use with BiRefNet

# Install from https://github.com/ZhengPeng7/BiRefNet

from models.birefnet import BiRefNet
model = BiRefNet.from_pretrained("${e.id}")`],Tb=e=>[`pip install huggingface_hub hf_transfer

export HF_HUB_ENABLE_HF_TRANS: string[]FER=1
huggingface-cli download --local-dir ${fn(e.id)} ${e.id}`],Cb=e=>[`from mlxim.model import create_model

model = create_model(${e.id})`],Ab=e=>[`from model2vec import StaticModel

model = StaticModel.from_pretrained("${e.id}")`],Ib=e=>{let t;return e.tags.includes("automatic-speech-recognition")&&(t=vb("ASR",e)),t??["# tag did not correspond to a valid NeMo domain."]},jb=e=>[`from pxia import AutoModel

model = AutoModel.from_pretrained("${e.id}")`],Lb=e=>[`from pythae.models import AutoModel

model = AutoModel.load_from_hf_hub("${e.id}")`],Pb=e=>[`from audiocraft.models import MusicGen

model = MusicGen.get_pretrained("${e.id}")

descriptions = ['happy rock', 'energetic EDM', 'sad jazz']
wav = model.generate(descriptions)  # generates 3 samples.`],Rb=e=>[`from audiocraft.models import MAGNeT
	
model = MAGNeT.get_pretrained("${e.id}")

descriptions = ['disco beat', 'energetic EDM', 'funky groove']
wav = model.generate(descriptions)  # generates 3 samples.`],Db=e=>[`from audiocraft.models import AudioGen
	
model = AudioGen.get_pretrained("${e.id}")
model.set_generation_params(duration=5)  # generate 5 seconds.
descriptions = ['dog barking', 'sirene of an emergency vehicle', 'footsteps in a corridor']
wav = model.generate(descriptions)  # generates 3 samples.`],Nb=e=>e.tags.includes("musicgen")?Pb(e):e.tags.includes("audiogen")?Db(e):e.tags.includes("magnet")?Rb(e):["# Type of model unknown."],Mb=()=>[`# Install CLI with Homebrew on macOS device
brew install whisperkit-cli

# View all available inference options
whisperkit-cli transcribe --help
	
# Download and run inference using whisper base model
whisperkit-cli transcribe --audio-path /path/to/audio.mp3

# Or use your preferred model variant
whisperkit-cli transcribe --model "large-v3" --model-prefix "distil" --audio-path /path/to/audio.mp3 --verbose`],Ob=e=>[`from threedtopia_xl.models import threedtopia_xl

model = threedtopia_xl.from_pretrained("${e.id}")
model.generate(cond="path/to/image.png")`],Fb={"adapter-transformers":{prettyLabel:"Adapters",repoName:"adapters",repoUrl:"https://github.com/Adapter-Hub/adapters",docsUrl:"https://huggingface.co/docs/hub/adapters",snippets:nx,filter:!0,countDownloads:'path:"adapter_config.json"'},allennlp:{prettyLabel:"AllenNLP",repoName:"AllenNLP",repoUrl:"https://github.com/allenai/allennlp",docsUrl:"https://huggingface.co/docs/hub/allennlp",snippets:ax,filter:!0},asteroid:{prettyLabel:"Asteroid",repoName:"Asteroid",repoUrl:"https://github.com/asteroid-team/asteroid",docsUrl:"https://huggingface.co/docs/hub/asteroid",snippets:ox,filter:!0,countDownloads:'path:"pytorch_model.bin"'},audiocraft:{prettyLabel:"Audiocraft",repoName:"audiocraft",repoUrl:"https://github.com/facebookresearch/audiocraft",snippets:Nb,filter:!1,countDownloads:'path:"state_dict.bin"'},audioseal:{prettyLabel:"AudioSeal",repoName:"audioseal",repoUrl:"https://github.com/facebookresearch/audioseal",filter:!1,countDownloads:'path_extension:"pth"',snippets:sx},bertopic:{prettyLabel:"BERTopic",repoName:"BERTopic",repoUrl:"https://github.com/MaartenGr/BERTopic",snippets:lx,filter:!0},big_vision:{prettyLabel:"Big Vision",repoName:"big_vision",repoUrl:"https://github.com/google-research/big_vision",filter:!1,countDownloads:'path_extension:"npz"'},birefnet:{prettyLabel:"BiRefNet",repoName:"BiRefNet",repoUrl:"https://github.com/ZhengPeng7/BiRefNet",snippets:Eb,filter:!1},bm25s:{prettyLabel:"BM25S",repoName:"bm25s",repoUrl:"https://github.com/xhluca/bm25s",snippets:ux,filter:!1,countDownloads:'path:"params.index.json"'},champ:{prettyLabel:"Champ",repoName:"Champ",repoUrl:"https://github.com/fudan-generative-vision/champ",countDownloads:'path:"champ/motion_module.pth"'},chat_tts:{prettyLabel:"ChatTTS",repoName:"ChatTTS",repoUrl:"https://github.com/2noise/ChatTTS.git",snippets:_b,filter:!1,countDownloads:'path:"asset/GPT.pt"'},colpali:{prettyLabel:"ColPali",repoName:"ColPali",repoUrl:"https://github.com/ManuelFay/colpali",filter:!1,countDownloads:'path:"adapter_config.json"'},deepforest:{prettyLabel:"DeepForest",repoName:"deepforest",docsUrl:"https://deepforest.readthedocs.io/en/latest/",repoUrl:"https://github.com/weecology/DeepForest",countDownloads:'path_extension:"pt" 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	-X POST \\
	--data-binary '@${be(e,!0,!0)}' \\
	-H "Authorization: Bearer ${t||"{API_TOKEN}"}"`}),gi={"text-classification":qe,"token-classification":qe,"table-question-answering":qe,"question-answering":qe,"zero-shot-classification":Wp,translation:qe,summarization:qe,"feature-extraction":qe,"text-generation":tl,"image-text-to-text":tl,"text2text-generation":qe,"fill-mask":qe,"sentence-similarity":qe,"automatic-speech-recognition":tn,"text-to-image":qe,"text-to-speech":qe,"text-to-audio":qe,"audio-to-audio":tn,"audio-classification":tn,"image-classification":tn,"image-to-text":tn,"object-detection":tn,"image-segmentation":tn};function ck(e,t){var n;return e.pipeline_tag&&e.pipeline_tag in gi?((n=gi[e.pipeline_tag])==null?void 0:n.call(gi,e,t))??{content:""}:{content:""}}function dk(e){return!!e.pipeline_tag&&e.pipeline_tag in gi}var Kp={};Ur(Kp,{getPythonInferenceSnippet:()=>fk,hasPythonInferenceSnippet:()=>pk,pythonSnippets:()=>yi,snippetBasic:()=>ut,snippetConversational:()=>du,snippetDocumentQuestionAnswering:()=>Jp,snippetFile:()=>nn,snippetTabular:()=>nl,snippetTextToAudio:()=>il,snippetTextToImage:()=>Yp,snippetZeroShotClassification:()=>Qp,snippetZeroShotImageClassification:()=>Xp});var du=(e,t,n)=>{const i=(n==null?void 0:n.streaming)??!0,r=be(e),a=(n==null?void 0:n.messages)??r,o=uu(a,{attributeKeyQuotes:!0}),s={...n!=null&&n.temperature?{temperature:n.temperature}:void 0,max_tokens:(n==null?void 0:n.max_tokens)??500,...n!=null&&n.top_p?{top_p:n.top_p}:void 0},l=cu(s,{indent:`
	`,attributeValueConnector:"="});return i?[{client:"huggingface_hub",content:`from huggingface_hub import InferenceClient

client = InferenceClient(api_key="${t||"{API_TOKEN}"}")

messages = ${o}

stream = client.chat.completions.create(
    model="${e.id}", 
	messages=messages, 
	${l},
	stream=True
)

for chunk in stream:
    print(chunk.choices[0].delta.content, end="")`},{client:"openai",content:`from openai import OpenAI

client = OpenAI(
	base_url="https://api-inference.huggingface.co/v1/",
	api_key="${t||"{API_TOKEN}"}"
)

messages = ${o}

stream = client.chat.completions.create(
    model="${e.id}", 
	messages=messages, 
	${l},
	stream=True
)

for chunk in stream:
    print(chunk.choices[0].delta.content, end="")`}]:[{client:"huggingface_hub",content:`from huggingface_hub import InferenceClient

client = InferenceClient(api_key="${t||"{API_TOKEN}"}")

messages = ${o}

completion = client.chat.completions.create(
    model="${e.id}", 
	messages=messages, 
	${l}
)

print(completion.choices[0].message)`},{client:"openai",content:`from openai import OpenAI

client = OpenAI(
	base_url="https://api-inference.huggingface.co/v1/",
	api_key="${t||"{API_TOKEN}"}"
)

messages = ${o}

completion = client.chat.completions.create(
    model="${e.id}", 
	messages=messages, 
	${l}
)

print(completion.choices[0].message)`}]},Qp=e=>({content:`def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.json()

output = query({
    "inputs": ${be(e)},
    "parameters": {"candidate_labels": ["refund", "legal", "faq"]},
})`}),Xp=e=>({content:`def query(data):
	with open(data["image_path"], "rb") as f:
		img = f.read()
	payload={
		"parameters": data["parameters"],
		"inputs": base64.b64encode(img).decode("utf-8")
	}
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.json()

output = query({
    "image_path": ${be(e)},
    "parameters": {"candidate_labels": ["cat", "dog", "llama"]},
})`}),ut=e=>({content:`def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.json()
	
output = query({
	"inputs": ${be(e)},
})`}),nn=e=>({content:`def query(filename):
    with open(filename, "rb") as f:
        data = f.read()
    response = requests.post(API_URL, headers=headers, data=data)
    return response.json()

output = query(${be(e)})`}),Yp=e=>({content:`def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.content
image_bytes = query({
	"inputs": ${be(e)},
})
# You can access the image with PIL.Image for example
import io
from PIL import Image
image = Image.open(io.BytesIO(image_bytes))`}),nl=e=>({content:`def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.content
response = query({
	"inputs": {"data": ${be(e)}},
})`}),il=e=>e.library_name==="transformers"?{content:`def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.content

audio_bytes = query({
	"inputs": ${be(e)},
})
# You can access the audio with IPython.display for example
from IPython.display import Audio
Audio(audio_bytes)`}:{content:`def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.json()
	
audio, sampling_rate = query({
	"inputs": ${be(e)},
})
# You can access the audio with IPython.display for example
from IPython.display import Audio
Audio(audio, rate=sampling_rate)`},Jp=e=>({content:`def query(payload):
 	with open(payload["image"], "rb") as f:
  		img = f.read()
		payload["image"] = base64.b64encode(img).decode("utf-8")  
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.json()

output = query({
    "inputs": ${be(e)},
})`}),yi={"text-classification":ut,"token-classification":ut,"table-question-answering":ut,"question-answering":ut,"zero-shot-classification":Qp,translation:ut,summarization:ut,"feature-extraction":ut,"text-generation":ut,"text2text-generation":ut,"image-text-to-text":du,"fill-mask":ut,"sentence-similarity":ut,"automatic-speech-recognition":nn,"text-to-image":Yp,"text-to-speech":il,"text-to-audio":il,"audio-to-audio":nn,"audio-classification":nn,"image-classification":nn,"tabular-regression":nl,"tabular-classification":nl,"object-detection":nn,"image-segmentation":nn,"document-question-answering":Jp,"image-to-text":nn,"zero-shot-image-classification":Xp};function fk(e,t,n){var i;if(e.tags.includes("conversational"))return du(e,t,n);{let r=e.pipeline_tag&&e.pipeline_tag in yi?((i=yi[e.pipeline_tag])==null?void 0:i.call(yi,e,t))??{content:""}:{content:""};return r=Array.isArray(r)?r:[r],r.map(a=>({...a,content:`import requests

API_URL = "https://api-inference.huggingface.co/models/${e.id}"
headers = {"Authorization": ${t?`"Bearer ${t}"`:'f"Bearer {API_TOKEN}"'}}

${a.content}`}))}}function pk(e){return!!e.pipeline_tag&&e.pipeline_tag in yi}var Zp={};Ur(Zp,{getJsInferenceSnippet:()=>mk,hasJsInferenceSnippet:()=>hk,jsSnippets:()=>vi,snippetBasic:()=>ct,snippetFile:()=>rn,snippetTextGeneration:()=>rl,snippetTextToAudio:()=>al,snippetTextToImage:()=>em,snippetZeroShotClassification:()=>Gp});var ct=(e,t)=>({content:`async function query(data) {
	const response = await fetch(
		"https://api-inference.huggingface.co/models/${e.id}",
		{
			headers: {
				Authorization: "Bearer ${t||"{API_TOKEN}"}"
				"Content-Type": "application/json",
			},
			method: "POST",
			body: JSON.stringify(data),
		}
	);
	const result = await response.json();
	return result;
}

query({"inputs": ${be(e)}}).then((response) => {
	console.log(JSON.stringify(response));
});`}),rl=(e,t,n)=>{if(e.tags.includes("conversational")){const i=(n==null?void 0:n.streaming)??!0,r=be(e),a=(n==null?void 0:n.messages)??r,o=uu(a,{indent:"	"}),s={...n!=null&&n.temperature?{temperature:n.temperature}:void 0,max_tokens:(n==null?void 0:n.max_tokens)??500,...n!=null&&n.top_p?{top_p:n.top_p}:void 0},l=cu(s,{indent:`
	`,attributeValueConnector:": "});return i?[{client:"huggingface.js",content:`import { HfInference } from "@huggingface/inference"

const client = new HfInference("${t||"{API_TOKEN}"}")

let out = "";

const stream = client.chatCompletionStream({
	model: "${e.id}",
	messages: ${o},
	${l}
});

for await (const chunk of stream) {
	if (chunk.choices && chunk.choices.length > 0) {
		const newContent = chunk.choices[0].delta.content;
		out += newContent;
		console.log(newContent);
	}  
}`},{client:"openai",content:`import { OpenAI } from "openai"

const client = new OpenAI({
	baseURL: "https://api-inference.huggingface.co/v1/",
    apiKey: "${t||"{API_TOKEN}"}"
})

let out = "";

const stream = await client.chat.completions.create({
	model: "${e.id}",
	messages: ${o},
	${l},
	stream: true,
});

for await (const chunk of stream) {
	if (chunk.choices && chunk.choices.length > 0) {
		const newContent = chunk.choices[0].delta.content;
		out += newContent;
		console.log(newContent);
	}  
}`}]:[{client:"huggingface.js",content:`import { HfInference } from '@huggingface/inference'

const client = new HfInference("${t||"{API_TOKEN}"}")

const chatCompletion = await client.chatCompletion({
	model: "${e.id}",
	messages: ${o},
	${l}
});

console.log(chatCompletion.choices[0].message);`},{client:"openai",content:`import { OpenAI } from "openai"

const client = new OpenAI({
    baseURL: "https://api-inference.huggingface.co/v1/",
    apiKey: "${t||"{API_TOKEN}"}"
})

const chatCompletion = await client.chat.completions.create({
	model: "${e.id}",
	messages: ${o},
	${l}
});

console.log(chatCompletion.choices[0].message);`}]}else return ct(e,t)},Gp=(e,t)=>({content:`async function query(data) {
	const response = await fetch(
		"https://api-inference.huggingface.co/models/${e.id}",
		{
			headers: {
				Authorization: "Bearer ${t||"{API_TOKEN}"}"
				"Content-Type": "application/json",
			},
			method: "POST",
			body: JSON.stringify(data),
		}
	);
	const result = await response.json();
	return result;
}

query({"inputs": ${be(e)}, "parameters": {"candidate_labels": ["refund", "legal", "faq"]}}).then((response) => {
	console.log(JSON.stringify(response));
});`}),em=(e,t)=>({content:`async function query(data) {
	const response = await fetch(
		"https://api-inference.huggingface.co/models/${e.id}",
		{
			headers: {
				Authorization: "Bearer ${t||"{API_TOKEN}"}"
				"Content-Type": "application/json",
			},
			method: "POST",
			body: JSON.stringify(data),
		}
	);
	const result = await response.blob();
	return result;
}
query({"inputs": ${be(e)}}).then((response) => {
	// Use image
});`}),al=(e,t)=>{const n=`async function query(data) {
		const response = await fetch(
			"https://api-inference.huggingface.co/models/${e.id}",
			{
				headers: {
					Authorization: "Bearer ${t||"{API_TOKEN}"}"
					"Content-Type": "application/json",
				},
				method: "POST",
				body: JSON.stringify(data),
			}
		);`;return e.library_name==="transformers"?{content:n+`
			const result = await response.blob();
			return result;
		}
		query({"inputs": ${be(e)}}).then((response) => {
			// Returns a byte object of the Audio wavform. Use it directly!
		});`}:{content:n+`
			const result = await response.json();
			return result;
		}
		
		query({"inputs": ${be(e)}}).then((response) => {
			console.log(JSON.stringify(response));
		});`}},rn=(e,t)=>({content:`async function query(filename) {
	const data = fs.readFileSync(filename);
	const response = await fetch(
		"https://api-inference.huggingface.co/models/${e.id}",
		{
			headers: {
				Authorization: "Bearer ${t||"{API_TOKEN}"}"
				"Content-Type": "application/json",
			},
			method: "POST",
			body: data,
		}
	);
	const result = await response.json();
	return result;
}

query(${be(e)}).then((response) => {
	console.log(JSON.stringify(response));
});`}),vi={"text-classification":ct,"token-classification":ct,"table-question-answering":ct,"question-answering":ct,"zero-shot-classification":Gp,translation:ct,summarization:ct,"feature-extraction":ct,"text-generation":rl,"image-text-to-text":rl,"text2text-generation":ct,"fill-mask":ct,"sentence-similarity":ct,"automatic-speech-recognition":rn,"text-to-image":em,"text-to-speech":al,"text-to-audio":al,"audio-to-audio":rn,"audio-classification":rn,"image-classification":rn,"image-to-text":rn,"object-detection":rn,"image-segmentation":rn};function mk(e,t){var n;return e.pipeline_tag&&e.pipeline_tag in vi?((n=vi[e.pipeline_tag])==null?void 0:n.call(vi,e,t))??{content:""}:{content:""}}function hk(e){return!!e.pipeline_tag&&e.pipeline_tag in vi}var tm=(e=>(e[e.F32=0]="F32",e[e.F16=1]="F16",e[e.Q4_0=2]="Q4_0",e[e.Q4_1=3]="Q4_1",e[e.Q5_0=6]="Q5_0",e[e.Q5_1=7]="Q5_1",e[e.Q8_0=8]="Q8_0",e[e.Q8_1=9]="Q8_1",e[e.Q2_K=10]="Q2_K",e[e.Q3_K=11]="Q3_K",e[e.Q4_K=12]="Q4_K",e[e.Q5_K=13]="Q5_K",e[e.Q6_K=14]="Q6_K",e[e.Q8_K=15]="Q8_K",e[e.IQ2_XXS=16]="IQ2_XXS",e[e.IQ2_XS=17]="IQ2_XS",e[e.IQ3_XXS=18]="IQ3_XXS",e[e.IQ1_S=19]="IQ1_S",e[e.IQ4_NL=20]="IQ4_NL",e[e.IQ3_S=21]="IQ3_S",e[e.IQ2_S=22]="IQ2_S",e[e.IQ4_XS=23]="IQ4_XS",e[e.I8=24]="I8",e[e.I16=25]="I16",e[e.I32=26]="I32",e[e.I64=27]="I64",e[e.F64=28]="F64",e[e.IQ1_M=29]="IQ1_M",e[e.BF16=30]="BF16",e))(tm||{}),gk=Object.values(tm).filter(e=>typeof e=="string");new RegExp(`(?<quant>${gk.join("|")})(_(?<sizeVariation>[A-Z]+))?`);var yk=Object.defineProperty,vk=(e,t)=>{for(var n in t)yk(e,n,{get:t[n],enumerable:!0})},wk={};vk(wk,{audioClassification:()=>am,audioToAudio:()=>lm,automaticSpeechRecognition:()=>om,chatCompletion:()=>Ik,chatCompletionStream:()=>jk,documentQuestionAnswering:()=>Cm,featureExtraction:()=>gm,fillMask:()=>ym,imageClassification:()=>um,imageSegmentation:()=>cm,imageToImage:()=>mm,imageToText:()=>dm,objectDetection:()=>fm,questionAnswering:()=>vm,request:()=>ie,sentenceSimilarity:()=>wm,streamingRequest:()=>_o,summarization:()=>xm,tableQuestionAnswering:()=>bm,tabularClassification:()=>jm,tabularRegression:()=>Im,textClassification:()=>km,textGeneration:()=>_m,textGenerationStream:()=>Ak,textToImage:()=>pm,textToSpeech:()=>sm,tokenClassification:()=>Sm,translation:()=>Em,visualQuestionAnswering:()=>Am,zeroShotClassification:()=>Tm,zeroShotImageClassification:()=>hm});function xk(e,t){return Object.assign({},...t.map(n=>{if(e[n]!==void 0)return{[n]:e[n]}}))}function bk(e,t){return e.includes(t)}function kk(e,t){const n=Array.isArray(t)?t:[t],i=Object.keys(e).filter(r=>!bk(n,r));return xk(e,i)}function Ea(e){return/^http(s?):/.test(e)||e.startsWith("/")}var ii=new Map,_k=10*60*1e3,Sk=1e3,nm="https://huggingface.co";async function im(e,t,n){if(Ea(e))return null;const i=`${e}:${t}`;let r=ii.get(i);if(r&&r.date<new Date(Date.now()-_k)&&(ii.delete(i),r=void 0),r===void 0){const a=await((n==null?void 0:n.fetch)??fetch)(`${nm}/api/models/${e}?expand[]=pipeline_tag`,{headers:t?{Authorization:`Bearer ${t}`}:{}}).then(o=>o.json()).then(o=>o.pipeline_tag).catch(()=>null);if(!a)return null;r={task:a,date:new Date},ii.set(i,{task:a,date:new Date}),ii.size>Sk&&ii.delete(ii.keys().next().value)}return r.task}var sd="https://api-inference.huggingface.co",da=null;async function rm(e,t){const{accessToken:n,endpointUrl:i,...r}=e;let{model:a}=e;const{forceTask:o,includeCredentials:s,taskHint:l,wait_for_model:u,use_cache:m,dont_load_model:f,chatCompletion:h}=t??{},v={};if(n&&(v.Authorization=`Bearer ${n}`),!a&&!da&&l){const p=await fetch(`${nm}/api/tasks`);p.ok&&(da=await p.json())}if(!a&&da&&l){const p=da[l];p&&(a=p.models[0].id)}if(!a)throw new Error("No model provided, and no default model found for this task");const _="data"in e&&!!e.data;_||(v["Content-Type"]="application/json"),u&&(v["X-Wait-For-Model"]="true"),m===!1&&(v["X-Use-Cache"]="false"),f&&(v["X-Load-Model"]="0");let k=(()=>{if(i&&Ea(a))throw new TypeError("Both model and endpointUrl cannot be URLs");return Ea(a)?(console.warn("Using a model URL is deprecated, please use the `endpointUrl` parameter instead"),a):i||(o?`${sd}/pipeline/${o}/${a}`:`${sd}/models/${a}`)})();h&&!k.endsWith("/chat/completions")&&(k+="/v1/chat/completions");let I;typeof s=="string"?I=s:s===!0&&(I="include");const g={headers:v,method:"POST",body:_?e.data:JSON.stringify({...r.model&&Ea(r.model)?kk(r,"model"):r}),...I&&{credentials:I},signal:t==null?void 0:t.signal};return{url:k,info:g}}async function ie(e,t){var a,o;const{url:n,info:i}=await rm(e,t),r=await((t==null?void 0:t.fetch)??fetch)(n,i);if((t==null?void 0:t.retry_on_error)!==!1&&r.status===503&&!(t!=null&&t.wait_for_model))return ie(e,{...t,wait_for_model:!0});if(!r.ok){if((a=r.headers.get("Content-Type"))!=null&&a.startsWith("application/json")){const s=await r.json();if([400,422,404,500].includes(r.status)&&(t!=null&&t.chatCompletion))throw new Error(`Server ${e.model} does not seem to support chat completion. Error: ${s.error}`);if(s.error)throw new Error(JSON.stringify(s.error))}throw new Error("An error occurred while fetching the blob")}return(o=r.headers.get("Content-Type"))!=null&&o.startsWith("application/json")?await r.json():await r.blob()}function Ek(e){let t,n,i,r=!1;return function(o){t===void 0?(t=o,n=0,i=-1):t=Ck(t,o);const s=t.length;let l=0;for(;n<s;){r&&(t[n]===10&&(l=++n),r=!1);let u=-1;for(;n<s&&u===-1;++n)switch(t[n]){case 58:i===-1&&(i=n-l);break;case 13:r=!0;case 10:u=n;break}if(u===-1)break;e(t.subarray(l,u),i),l=n,i=-1}l===s?t=void 0:l!==0&&(t=t.subarray(l),n-=l)}}function Tk(e,t,n){let i=ld();const r=new TextDecoder;return function(o,s){if(o.length===0)n==null||n(i),i=ld();else if(s>0){const l=r.decode(o.subarray(0,s)),u=s+(o[s+1]===32?2:1),m=r.decode(o.subarray(u));switch(l){case"data":i.data=i.data?i.data+`
`+m:m;break;case"event":i.event=m;break;case"id":e(i.id=m);break;case"retry":const f=parseInt(m,10);isNaN(f)||t(i.retry=f);break}}}}function Ck(e,t){const n=new Uint8Array(e.length+t.length);return n.set(e),n.set(t,e.length),n}function ld(){return{data:"",event:"",id:"",retry:void 0}}async function*_o(e,t){var u,m;const{url:n,info:i}=await rm({...e,stream:!0},t),r=await((t==null?void 0:t.fetch)??fetch)(n,i);if((t==null?void 0:t.retry_on_error)!==!1&&r.status===503&&!(t!=null&&t.wait_for_model))return yield*_o(e,{...t,wait_for_model:!0});if(!r.ok){if((u=r.headers.get("Content-Type"))!=null&&u.startsWith("application/json")){const f=await r.json();if([400,422,404,500].includes(r.status)&&(t!=null&&t.chatCompletion))throw new Error(`Server ${e.model} does not seem to support chat completion. Error: ${f.error}`);if(f.error)throw new Error(f.error)}throw new Error(`Server response contains error: ${r.status}`)}if(!((m=r.headers.get("content-type"))!=null&&m.startsWith("text/event-stream")))throw new Error("Server does not support event stream content type, it returned "+r.headers.get("content-type"));if(!r.body)return;const a=r.body.getReader();let o=[];const l=Ek(Tk(()=>{},()=>{},f=>{o.push(f)}));try{for(;;){const{done:f,value:h}=await a.read();if(f)return;l(h);for(const v of o)if(v.data.length>0){if(v.data==="[DONE]")return;const _=JSON.parse(v.data);if(typeof _=="object"&&_!==null&&"error"in _)throw new Error(_.error);yield _}o=[]}}finally{a.releaseLock()}}var oe=class extends TypeError{constructor(e){super(`Invalid inference output: ${e}. Use the 'request' method with the same parameters to do a custom call with no type checking.`),this.name="InferenceOutputError"}};async function am(e,t){const n=await ie(e,{...t,taskHint:"audio-classification"});if(!(Array.isArray(n)&&n.every(r=>typeof r.label=="string"&&typeof r.score=="number")))throw new oe("Expected Array<{label: string, score: number}>");return n}async function om(e,t){const n=await ie(e,{...t,taskHint:"automatic-speech-recognition"});if(!(typeof(n==null?void 0:n.text)=="string"))throw new oe("Expected {text: string}");return n}async function sm(e,t){const n=await ie(e,{...t,taskHint:"text-to-speech"});if(!(n&&n instanceof Blob))throw new oe("Expected Blob");return n}async function lm(e,t){const n=await ie(e,{...t,taskHint:"audio-to-audio"});if(!(Array.isArray(n)&&n.every(r=>typeof r.label=="string"&&typeof r.blob=="string"&&typeof r["content-type"]=="string")))throw new oe("Expected Array<{label: string, blob: string, content-type: string}>");return n}async 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this task":"Select a task to view available models"}),c.jsx(L1,{accessToken:l,loading:e,model:s,setLoading:t,task:o})]}):c.jsx(d.Fragment,{})},N1=()=>{const t=document.getElementById("root");if(t){const n=Sp(t),r=Qv([{path:"*",element:c.jsx(k0,{}),children:[{path:"sign-in",element:c.jsx(S0,{})},{path:"*",element:c.jsxs(b0,{children:[c.jsx(E0,{}),c.jsx(D1,{})]})}]}]),a=c.jsx(d.StrictMode,{children:c.jsx(_0,{children:c.jsx(x0,{children:c.jsx(i0,{router:r})})})});n.render(a)}};N1();