diff --git a/README.md b/README.md
index 7af69a57f..bc30a6916 100644
--- a/README.md
+++ b/README.md
@@ -10,20 +10,30 @@
 </p>
 
 ```ts
+// Programatically interact with the Hub
+
+await createRepo({
+  repo: {type: "model", name: "my-user/nlp-model"},
+  credentials: {accessToken: HF_TOKEN}
+});
+
+await uploadFile({
+  repo: "my-user/nlp-model",
+  credentials: {accessToken: HF_TOKEN},
+  // Can work with native File in browsers
+  file: {
+    path: "pytorch_model.bin",
+    content: new Blob(...) 
+  }
+});
+
+// Use hosted inference
+
 await inference.translation({
   model: 't5-base',
   inputs: 'My name is Wolfgang and I live in Berlin'
 })
 
-await hf.translation({
-  model: "facebook/nllb-200-distilled-600M",
-  inputs: "how is the weather like in Gaborone",
-  parameters : {
-    src_lang: "eng_Latn",
-    tgt_lang: "sot_Latn"
-  }
-})
-
 await inference.textToImage({
   model: 'stabilityai/stable-diffusion-2',
   inputs: 'award winning high resolution photo of a giant tortoise/((ladybird)) hybrid, [trending on artstation]',
@@ -31,13 +41,15 @@ await inference.textToImage({
     negative_prompt: 'blurry',
   }
 })
+
+// and much more…
 ```
 
 # Hugging Face JS libraries
 
 This is a collection of JS libraries to interact with the Hugging Face API, with TS types included.
 
-- [@huggingface/inference](packages/inference/README.md): Use Inference Endpoints (serverless) to make calls to 100,000+ Machine Learning models
+- [@huggingface/inference](packages/inference/README.md): Use Inference Endpoints (serverless or dedicated) to make calls to 100,000+ Machine Learning models
 - [@huggingface/hub](packages/hub/README.md): Interact with huggingface.co to create or delete repos and commit / download files
 - [@huggingface/agents](packages/agents/README.md): Interact with HF models through a natural language interface
 
@@ -130,30 +142,6 @@ await inference.imageToText({
 const gpt2 = inference.endpoint('https://xyz.eu-west-1.aws.endpoints.huggingface.cloud/gpt2');
 const { generated_text } = await gpt2.textGeneration({inputs: 'The answer to the universe is'});
 ```
-### @huggingface/agents example
-
-```ts
-import {HfAgent, LLMFromHub, defaultTools} from '@huggingface/agents';
-
-const HF_TOKEN = "hf_...";
-
-const agent = new HfAgent(
-  HF_TOKEN,
-  LLMFromHub(HF_TOKEN),
-  [...defaultTools]
-);
-
-
-// you can generate the code, inspect it and then run it
-const code = await agent.generateCode("Draw a picture of a cat wearing a top hat. Then caption the picture and read it out loud.");
-console.log(code);
-const messages = await agent.evaluateCode(code)
-console.log(messages); // contains the data
-
-// or you can run the code directly, however you can't check that the code is safe to execute this way, use at your own risk.
-const messages = await agent.run("Draw a picture of a cat wearing a top hat. Then caption the picture and read it out loud.")
-console.log(messages); 
-```
 
 ### @huggingface/hub examples
 
@@ -184,6 +172,31 @@ await deleteFiles({
 });
 ```
 
+### @huggingface/agents example
+
+```ts
+import {HfAgent, LLMFromHub, defaultTools} from '@huggingface/agents';
+
+const HF_TOKEN = "hf_...";
+
+const agent = new HfAgent(
+  HF_TOKEN,
+  LLMFromHub(HF_TOKEN),
+  [...defaultTools]
+);
+
+
+// you can generate the code, inspect it and then run it
+const code = await agent.generateCode("Draw a picture of a cat wearing a top hat. Then caption the picture and read it out loud.");
+console.log(code);
+const messages = await agent.evaluateCode(code)
+console.log(messages); // contains the data
+
+// or you can run the code directly, however you can't check that the code is safe to execute this way, use at your own risk.
+const messages = await agent.run("Draw a picture of a cat wearing a top hat. Then caption the picture and read it out loud.")
+console.log(messages); 
+```
+
 
 There are more features of course, check each library's README!
 
diff --git a/packages/hub/README.md b/packages/hub/README.md
index 6a5cb3f4e..9ce14e2ba 100644
--- a/packages/hub/README.md
+++ b/packages/hub/README.md
@@ -1,6 +1,6 @@
 # 🤗 Hugging Face Hub API
 
-Official utilities to use the Hugging Face hub API, still very experimental.
+Official utilities to use the Hugging Face Hub API.
 
 ## Install
 
diff --git a/packages/tasks/README.md b/packages/tasks/README.md
index 3c5639b5d..91724b29a 100644
--- a/packages/tasks/README.md
+++ b/packages/tasks/README.md
@@ -25,7 +25,7 @@ This package contains the definition files (written in Typescript) for the huggi
 
 - **pipeline types** a.k.a. **task types** (used to determine which widget to display on the model page, and which inference API to run)
 - **default widget inputs** (when they aren't provided in the model card)
-- definitions and UI elements for **third party libraries**.
+- definitions and UI elements for **model libraries** (and soon for **dataset libraries**).
 
 Please add to any of those definitions by opening a PR. Thanks 🔥
 
diff --git a/packages/tasks/src/index.ts b/packages/tasks/src/index.ts
index d4173be09..e5cd667a6 100644
--- a/packages/tasks/src/index.ts
+++ b/packages/tasks/src/index.ts
@@ -1,5 +1,4 @@
 export { LIBRARY_TASK_MAPPING_EXCLUDING_TRANSFORMERS } from "./library-to-tasks";
-export { MODEL_LIBRARIES_UI_ELEMENTS } from "./library-ui-elements";
 export { MAPPING_DEFAULT_WIDGET } from "./default-widget-inputs";
 export type { TaskData, TaskDemo, TaskDemoEntry, ExampleRepo } from "./tasks";
 export * from "./tasks";
@@ -14,8 +13,8 @@ export {
 	SUBTASK_TYPES,
 	PIPELINE_TYPES_SET,
 } from "./pipelines";
-export { ModelLibrary, ALL_DISPLAY_MODEL_LIBRARY_KEYS } from "./model-libraries";
-export type { ModelLibraryKey } from "./model-libraries";
+export { ALL_DISPLAY_MODEL_LIBRARY_KEYS, ALL_MODEL_LIBRARY_KEYS, MODEL_LIBRARIES_UI_ELEMENTS } from "./model-libraries";
+export type { LibraryUiElement, ModelLibraryKey } from "./model-libraries";
 export type { ModelData, TransformersInfo } from "./model-data";
 export type {
 	WidgetExample,
@@ -41,5 +40,3 @@ export { InferenceDisplayability } from "./model-data";
 
 import * as snippets from "./snippets";
 export { snippets };
-
-export type { LibraryUiElement } from "./library-ui-elements";
diff --git a/packages/tasks/src/library-to-tasks.ts b/packages/tasks/src/library-to-tasks.ts
index b7ddcc381..3430f30ba 100644
--- a/packages/tasks/src/library-to-tasks.ts
+++ b/packages/tasks/src/library-to-tasks.ts
@@ -3,7 +3,7 @@ import type { PipelineType } from "./pipelines";
 
 /**
  * Mapping from library name (excluding Transformers) to its supported tasks.
- * Inference Endpoints (serverless) should be disabled for all other (library, task) pairs beyond this mapping.
+ * Inference API (serverless) should be disabled for all other (library, task) pairs beyond this mapping.
  * As an exception, we assume Transformers supports all inference tasks.
  * This mapping is generated automatically by "python-api-export-tasks" action in huggingface/api-inference-community repo upon merge.
  * Ref: https://github.com/huggingface/api-inference-community/pull/158
diff --git a/packages/tasks/src/library-ui-elements.ts b/packages/tasks/src/model-libraries-snippets.ts
similarity index 61%
rename from packages/tasks/src/library-ui-elements.ts
rename to packages/tasks/src/model-libraries-snippets.ts
index ae8571a9e..242119e03 100644
--- a/packages/tasks/src/library-ui-elements.ts
+++ b/packages/tasks/src/model-libraries-snippets.ts
@@ -1,35 +1,7 @@
 import type { ModelData } from "./model-data";
-import type { ModelLibraryKey } from "./model-libraries";
 
 const TAG_CUSTOM_CODE = "custom_code";
 
-/**
- * Elements configurable by a model library.
- */
-export interface LibraryUiElement {
-	/**
-	 * Name displayed on the main
-	 * call-to-action button on the model page.
-	 */
-	btnLabel: string;
-	/**
-	 * Repo name
-	 */
-	repoName: string;
-	/**
-	 * URL to library's repo
-	 */
-	repoUrl: string;
-	/**
-	 * URL to library's docs
-	 */
-	docsUrl?: string;
-	/**
-	 * Code snippet displayed on model page
-	 */
-	snippets: (model: ModelData) => string[];
-}
-
 function nameWithoutNamespace(modelId: string): string {
 	const splitted = modelId.split("/");
 	return splitted.length === 1 ? splitted[0] : splitted[1];
@@ -37,7 +9,7 @@ function nameWithoutNamespace(modelId: string): string {
 
 //#region snippets
 
-const adapters = (model: ModelData) => [
+export const adapters = (model: ModelData): string[] => [
 	`from adapters import AutoAdapterModel
 
 model = AutoAdapterModel.from_pretrained("${model.config?.adapter_transformers?.model_name}")
@@ -60,14 +32,14 @@ predictor_input = {"passage": "My name is Wolfgang and I live in Berlin", "quest
 predictions = predictor.predict_json(predictor_input)`,
 ];
 
-const allennlp = (model: ModelData) => {
+export const allennlp = (model: ModelData): string[] => {
 	if (model.tags?.includes("question-answering")) {
 		return allennlpQuestionAnswering(model);
 	}
 	return allennlpUnknown(model);
 };
 
-const asteroid = (model: ModelData) => [
+export const asteroid = (model: ModelData): string[] => [
 	`from asteroid.models import BaseModel
 
 model = BaseModel.from_pretrained("${model.id}")`,
@@ -77,7 +49,7 @@ function get_base_diffusers_model(model: ModelData): string {
 	return model.cardData?.base_model?.toString() ?? "fill-in-base-model";
 }
 
-const bertopic = (model: ModelData) => [
+export const bertopic = (model: ModelData): string[] => [
 	`from bertopic import BERTopic
 
 model = BERTopic.load("${model.id}")`,
@@ -112,7 +84,7 @@ pipeline = DiffusionPipeline.from_pretrained("${get_base_diffusers_model(model)}
 pipeline.load_textual_inversion("${model.id}")`,
 ];
 
-const diffusers = (model: ModelData) => {
+export const diffusers = (model: ModelData): string[] => {
 	if (model.tags?.includes("controlnet")) {
 		return diffusers_controlnet(model);
 	} else if (model.tags?.includes("lora")) {
@@ -124,7 +96,7 @@ const diffusers = (model: ModelData) => {
 	}
 };
 
-const espnetTTS = (model: ModelData) => [
+export const espnetTTS = (model: ModelData): string[] => [
 	`from espnet2.bin.tts_inference import Text2Speech
 
 model = Text2Speech.from_pretrained("${model.id}")
@@ -132,7 +104,7 @@ model = Text2Speech.from_pretrained("${model.id}")
 speech, *_ = model("text to generate speech from")`,
 ];
 
-const espnetASR = (model: ModelData) => [
+export const espnetASR = (model: ModelData): string[] => [
 	`from espnet2.bin.asr_inference import Speech2Text
 
 model = Speech2Text.from_pretrained(
@@ -145,7 +117,7 @@ text, *_ = model(speech)[0]`,
 
 const espnetUnknown = () => [`unknown model type (must be text-to-speech or automatic-speech-recognition)`];
 
-const espnet = (model: ModelData) => {
+export const espnet = (model: ModelData): string[] => {
 	if (model.tags?.includes("text-to-speech")) {
 		return espnetTTS(model);
 	} else if (model.tags?.includes("automatic-speech-recognition")) {
@@ -154,7 +126,7 @@ const espnet = (model: ModelData) => {
 	return espnetUnknown();
 };
 
-const fairseq = (model: ModelData) => [
+export const fairseq = (model: ModelData): string[] => [
 	`from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
 
 models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
@@ -162,27 +134,27 @@ models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
 )`,
 ];
 
-const flair = (model: ModelData) => [
+export const flair = (model: ModelData): string[] => [
 	`from flair.models import SequenceTagger
 
 tagger = SequenceTagger.load("${model.id}")`,
 ];
 
-const keras = (model: ModelData) => [
+export const keras = (model: ModelData): string[] => [
 	`from huggingface_hub import from_pretrained_keras
 
 model = from_pretrained_keras("${model.id}")
 `,
 ];
 
-const open_clip = (model: ModelData) => [
+export const open_clip = (model: ModelData): string[] => [
 	`import open_clip
 
 model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:${model.id}')
 tokenizer = open_clip.get_tokenizer('hf-hub:${model.id}')`,
 ];
 
-const paddlenlp = (model: ModelData) => {
+export const paddlenlp = (model: ModelData): string[] => {
 	if (model.config?.architectures?.[0]) {
 		const architecture = model.config.architectures[0];
 		return [
@@ -206,7 +178,7 @@ const paddlenlp = (model: ModelData) => {
 	}
 };
 
-const pyannote_audio_pipeline = (model: ModelData) => [
+export const pyannote_audio_pipeline = (model: ModelData): string[] => [
 	`from pyannote.audio import Pipeline
   
 pipeline = Pipeline.from_pretrained("${model.id}")
@@ -223,7 +195,7 @@ waveform, sample_rate = Audio().crop("file.wav", excerpt)
 pipeline({"waveform": waveform, "sample_rate": sample_rate})`,
 ];
 
-const pyannote_audio_model = (model: ModelData) => [
+const pyannote_audio_model = (model: ModelData): string[] => [
 	`from pyannote.audio import Model, Inference
 
 model = Model.from_pretrained("${model.id}")
@@ -238,14 +210,14 @@ excerpt = Segment(start=2.0, end=5.0)
 inference.crop("file.wav", excerpt)`,
 ];
 
-const pyannote_audio = (model: ModelData) => {
+export const pyannote_audio = (model: ModelData): string[] => {
 	if (model.tags?.includes("pyannote-audio-pipeline")) {
 		return pyannote_audio_pipeline(model);
 	}
 	return pyannote_audio_model(model);
 };
 
-const tensorflowttsTextToMel = (model: ModelData) => [
+const tensorflowttsTextToMel = (model: ModelData): string[] => [
 	`from tensorflow_tts.inference import AutoProcessor, TFAutoModel
 
 processor = AutoProcessor.from_pretrained("${model.id}")
@@ -253,7 +225,7 @@ model = TFAutoModel.from_pretrained("${model.id}")
 `,
 ];
 
-const tensorflowttsMelToWav = (model: ModelData) => [
+const tensorflowttsMelToWav = (model: ModelData): string[] => [
 	`from tensorflow_tts.inference import TFAutoModel
 
 model = TFAutoModel.from_pretrained("${model.id}")
@@ -261,14 +233,14 @@ audios = model.inference(mels)
 `,
 ];
 
-const tensorflowttsUnknown = (model: ModelData) => [
+const tensorflowttsUnknown = (model: ModelData): string[] => [
 	`from tensorflow_tts.inference import TFAutoModel
 
 model = TFAutoModel.from_pretrained("${model.id}")
 `,
 ];
 
-const tensorflowtts = (model: ModelData) => {
+export const tensorflowtts = (model: ModelData): string[] => {
 	if (model.tags?.includes("text-to-mel")) {
 		return tensorflowttsTextToMel(model);
 	} else if (model.tags?.includes("mel-to-wav")) {
@@ -277,7 +249,7 @@ const tensorflowtts = (model: ModelData) => {
 	return tensorflowttsUnknown(model);
 };
 
-const timm = (model: ModelData) => [
+export const timm = (model: ModelData): string[] => [
 	`import timm
 
 model = timm.create_model("hf_hub:${model.id}", pretrained=True)`,
@@ -319,7 +291,7 @@ model = joblib.load(
 	];
 };
 
-const sklearn = (model: ModelData) => {
+export const sklearn = (model: ModelData): string[] => {
 	if (model.tags?.includes("skops")) {
 		const skopsmodelFile = model.config?.sklearn?.filename;
 		const skopssaveFormat = model.config?.sklearn?.model_format;
@@ -336,29 +308,29 @@ const sklearn = (model: ModelData) => {
 	}
 };
 
-const fastai = (model: ModelData) => [
+export const fastai = (model: ModelData): string[] => [
 	`from huggingface_hub import from_pretrained_fastai
 
 learn = from_pretrained_fastai("${model.id}")`,
 ];
 
-const sampleFactory = (model: ModelData) => [
+export const sampleFactory = (model: ModelData): string[] => [
 	`python -m sample_factory.huggingface.load_from_hub -r ${model.id} -d ./train_dir`,
 ];
 
-const sentenceTransformers = (model: ModelData) => [
+export const sentenceTransformers = (model: ModelData): string[] => [
 	`from sentence_transformers import SentenceTransformer
 
 model = SentenceTransformer("${model.id}")`,
 ];
 
-const setfit = (model: ModelData) => [
+export const setfit = (model: ModelData): string[] => [
 	`from setfit import SetFitModel
 
 model = SetFitModel.from_pretrained("${model.id}")`,
 ];
 
-const spacy = (model: ModelData) => [
+export const spacy = (model: ModelData): string[] => [
 	`!pip install https://huggingface.co/${model.id}/resolve/main/${nameWithoutNamespace(model.id)}-any-py3-none-any.whl
 
 # Using spacy.load().
@@ -370,13 +342,13 @@ import ${nameWithoutNamespace(model.id)}
 nlp = ${nameWithoutNamespace(model.id)}.load()`,
 ];
 
-const span_marker = (model: ModelData) => [
+export const span_marker = (model: ModelData): string[] => [
 	`from span_marker import SpanMarkerModel
 
 model = SpanMarkerModel.from_pretrained("${model.id}")`,
 ];
 
-const stanza = (model: ModelData) => [
+export const stanza = (model: ModelData): string[] => [
 	`import stanza
 
 stanza.download("${nameWithoutNamespace(model.id).replace("stanza-", "")}")
@@ -399,7 +371,7 @@ const speechBrainMethod = (speechbrainInterface: string) => {
 	}
 };
 
-const speechbrain = (model: ModelData) => {
+export const speechbrain = (model: ModelData): string[] => {
 	const speechbrainInterface = model.config?.speechbrain?.interface;
 	if (speechbrainInterface === undefined) {
 		return [`# interface not specified in config.json`];
@@ -419,7 +391,7 @@ model.${speechbrainMethod}("file.wav")`,
 	];
 };
 
-const transformers = (model: ModelData) => {
+export const transformers = (model: ModelData): string[] => {
 	const info = model.transformersInfo;
 	if (!info) {
 		return [`# ⚠️ Type of model unknown`];
@@ -461,7 +433,7 @@ const transformers = (model: ModelData) => {
 	return [autoSnippet];
 };
 
-const transformersJS = (model: ModelData) => {
+export const transformersJS = (model: ModelData): string[] => {
 	if (!model.pipeline_tag) {
 		return [`// ⚠️ Unknown pipeline tag`];
 	}
@@ -492,7 +464,7 @@ const peftTask = (peftTaskType?: string) => {
 	}
 };
 
-const peft = (model: ModelData) => {
+export const peft = (model: ModelData): string[] => {
 	const { base_model_name: peftBaseModel, task_type: peftTaskType } = model.config?.peft ?? {};
 	const pefttask = peftTask(peftTaskType);
 	if (!pefttask) {
@@ -512,14 +484,14 @@ model = PeftModel.from_pretrained(model, "${model.id}")`,
 	];
 };
 
-const fasttext = (model: ModelData) => [
+export const fasttext = (model: ModelData): string[] => [
 	`from huggingface_hub import hf_hub_download
 import fasttext
 
 model = fasttext.load_model(hf_hub_download("${model.id}", "model.bin"))`,
 ];
 
-const stableBaselines3 = (model: ModelData) => [
+export const stableBaselines3 = (model: ModelData): string[] => [
 	`from huggingface_sb3 import load_from_hub
 checkpoint = load_from_hub(
 	repo_id="${model.id}",
@@ -541,9 +513,11 @@ transcriptions = asr_model.transcribe(["file.wav"])`,
 	}
 };
 
-const mlAgents = (model: ModelData) => [`mlagents-load-from-hf --repo-id="${model.id}" --local-dir="./downloads"`];
+export const mlAgents = (model: ModelData): string[] => [
+	`mlagents-load-from-hf --repo-id="${model.id}" --local-dir="./download: string[]s"`,
+];
 
-const sentis = (/* model: ModelData */) => [
+export const sentis = (/* model: ModelData */): string[] => [
 	`string modelName = "[Your model name here].sentis";
 Model model = ModelLoader.Load(Application.streamingAssetsPath + "/" + modelName);
 IWorker engine = WorkerFactory.CreateWorker(BackendType.GPUCompute, model);
@@ -551,14 +525,14 @@ IWorker engine = WorkerFactory.CreateWorker(BackendType.GPUCompute, model);
 `,
 ];
 
-const mlx = (model: ModelData) => [
+export const mlx = (model: ModelData): string[] => [
 	`pip install huggingface_hub hf_transfer
 
-export HF_HUB_ENABLE_HF_TRANSFER=1
+export HF_HUB_ENABLE_HF_TRANS: string[]FER=1
 huggingface-cli download --local-dir ${nameWithoutNamespace(model.id)} ${model.id}`,
 ];
 
-const nemo = (model: ModelData) => {
+export const nemo = (model: ModelData): string[] => {
 	let command: string[] | undefined = undefined;
 	// Resolve the tag to a nemo domain/sub-domain
 	if (model.tags?.includes("automatic-speech-recognition")) {
@@ -568,232 +542,10 @@ const nemo = (model: ModelData) => {
 	return command ?? [`# tag did not correspond to a valid NeMo domain.`];
 };
 
-const pythae = (model: ModelData) => [
+export const pythae = (model: ModelData): string[] => [
 	`from pythae.models import AutoModel
 
 model = AutoModel.load_from_hf_hub("${model.id}")`,
 ];
 
 //#endregion
-
-export const MODEL_LIBRARIES_UI_ELEMENTS: Partial<Record<ModelLibraryKey, LibraryUiElement>> = {
-	"adapter-transformers": {
-		btnLabel: "Adapters",
-		repoName: "adapters",
-		repoUrl: "https://github.com/Adapter-Hub/adapters",
-		docsUrl: "https://huggingface.co/docs/hub/adapters",
-		snippets: adapters,
-	},
-	allennlp: {
-		btnLabel: "AllenNLP",
-		repoName: "AllenNLP",
-		repoUrl: "https://github.com/allenai/allennlp",
-		docsUrl: "https://huggingface.co/docs/hub/allennlp",
-		snippets: allennlp,
-	},
-	asteroid: {
-		btnLabel: "Asteroid",
-		repoName: "Asteroid",
-		repoUrl: "https://github.com/asteroid-team/asteroid",
-		docsUrl: "https://huggingface.co/docs/hub/asteroid",
-		snippets: asteroid,
-	},
-	bertopic: {
-		btnLabel: "BERTopic",
-		repoName: "BERTopic",
-		repoUrl: "https://github.com/MaartenGr/BERTopic",
-		snippets: bertopic,
-	},
-	diffusers: {
-		btnLabel: "Diffusers",
-		repoName: "🤗/diffusers",
-		repoUrl: "https://github.com/huggingface/diffusers",
-		docsUrl: "https://huggingface.co/docs/hub/diffusers",
-		snippets: diffusers,
-	},
-	espnet: {
-		btnLabel: "ESPnet",
-		repoName: "ESPnet",
-		repoUrl: "https://github.com/espnet/espnet",
-		docsUrl: "https://huggingface.co/docs/hub/espnet",
-		snippets: espnet,
-	},
-	fairseq: {
-		btnLabel: "Fairseq",
-		repoName: "fairseq",
-		repoUrl: "https://github.com/pytorch/fairseq",
-		snippets: fairseq,
-	},
-	flair: {
-		btnLabel: "Flair",
-		repoName: "Flair",
-		repoUrl: "https://github.com/flairNLP/flair",
-		docsUrl: "https://huggingface.co/docs/hub/flair",
-		snippets: flair,
-	},
-	keras: {
-		btnLabel: "Keras",
-		repoName: "Keras",
-		repoUrl: "https://github.com/keras-team/keras",
-		docsUrl: "https://huggingface.co/docs/hub/keras",
-		snippets: keras,
-	},
-	mlx: {
-		btnLabel: "MLX",
-		repoName: "MLX",
-		repoUrl: "https://github.com/ml-explore/mlx-examples/tree/main",
-		snippets: mlx,
-	},
-	nemo: {
-		btnLabel: "NeMo",
-		repoName: "NeMo",
-		repoUrl: "https://github.com/NVIDIA/NeMo",
-		snippets: nemo,
-	},
-	open_clip: {
-		btnLabel: "OpenCLIP",
-		repoName: "OpenCLIP",
-		repoUrl: "https://github.com/mlfoundations/open_clip",
-		snippets: open_clip,
-	},
-	paddlenlp: {
-		btnLabel: "paddlenlp",
-		repoName: "PaddleNLP",
-		repoUrl: "https://github.com/PaddlePaddle/PaddleNLP",
-		docsUrl: "https://huggingface.co/docs/hub/paddlenlp",
-		snippets: paddlenlp,
-	},
-	peft: {
-		btnLabel: "PEFT",
-		repoName: "PEFT",
-		repoUrl: "https://github.com/huggingface/peft",
-		snippets: peft,
-	},
-	"pyannote-audio": {
-		btnLabel: "pyannote.audio",
-		repoName: "pyannote-audio",
-		repoUrl: "https://github.com/pyannote/pyannote-audio",
-		snippets: pyannote_audio,
-	},
-	"sentence-transformers": {
-		btnLabel: "sentence-transformers",
-		repoName: "sentence-transformers",
-		repoUrl: "https://github.com/UKPLab/sentence-transformers",
-		docsUrl: "https://huggingface.co/docs/hub/sentence-transformers",
-		snippets: sentenceTransformers,
-	},
-	setfit: {
-		btnLabel: "setfit",
-		repoName: "setfit",
-		repoUrl: "https://github.com/huggingface/setfit",
-		docsUrl: "https://huggingface.co/docs/hub/setfit",
-		snippets: setfit,
-	},
-	sklearn: {
-		btnLabel: "Scikit-learn",
-		repoName: "Scikit-learn",
-		repoUrl: "https://github.com/scikit-learn/scikit-learn",
-		snippets: sklearn,
-	},
-	fastai: {
-		btnLabel: "fastai",
-		repoName: "fastai",
-		repoUrl: "https://github.com/fastai/fastai",
-		docsUrl: "https://huggingface.co/docs/hub/fastai",
-		snippets: fastai,
-	},
-	spacy: {
-		btnLabel: "spaCy",
-		repoName: "spaCy",
-		repoUrl: "https://github.com/explosion/spaCy",
-		docsUrl: "https://huggingface.co/docs/hub/spacy",
-		snippets: spacy,
-	},
-	"span-marker": {
-		btnLabel: "SpanMarker",
-		repoName: "SpanMarkerNER",
-		repoUrl: "https://github.com/tomaarsen/SpanMarkerNER",
-		docsUrl: "https://huggingface.co/docs/hub/span_marker",
-		snippets: span_marker,
-	},
-	speechbrain: {
-		btnLabel: "speechbrain",
-		repoName: "speechbrain",
-		repoUrl: "https://github.com/speechbrain/speechbrain",
-		docsUrl: "https://huggingface.co/docs/hub/speechbrain",
-		snippets: speechbrain,
-	},
-	stanza: {
-		btnLabel: "Stanza",
-		repoName: "stanza",
-		repoUrl: "https://github.com/stanfordnlp/stanza",
-		docsUrl: "https://huggingface.co/docs/hub/stanza",
-		snippets: stanza,
-	},
-	tensorflowtts: {
-		btnLabel: "TensorFlowTTS",
-		repoName: "TensorFlowTTS",
-		repoUrl: "https://github.com/TensorSpeech/TensorFlowTTS",
-		snippets: tensorflowtts,
-	},
-	timm: {
-		btnLabel: "timm",
-		repoName: "pytorch-image-models",
-		repoUrl: "https://github.com/rwightman/pytorch-image-models",
-		docsUrl: "https://huggingface.co/docs/hub/timm",
-		snippets: timm,
-	},
-	transformers: {
-		btnLabel: "Transformers",
-		repoName: "🤗/transformers",
-		repoUrl: "https://github.com/huggingface/transformers",
-		docsUrl: "https://huggingface.co/docs/hub/transformers",
-		snippets: transformers,
-	},
-	"transformers.js": {
-		btnLabel: "Transformers.js",
-		repoName: "transformers.js",
-		repoUrl: "https://github.com/xenova/transformers.js",
-		docsUrl: "https://huggingface.co/docs/hub/transformers-js",
-		snippets: transformersJS,
-	},
-	fasttext: {
-		btnLabel: "fastText",
-		repoName: "fastText",
-		repoUrl: "https://fasttext.cc/",
-		snippets: fasttext,
-	},
-	"sample-factory": {
-		btnLabel: "sample-factory",
-		repoName: "sample-factory",
-		repoUrl: "https://github.com/alex-petrenko/sample-factory",
-		docsUrl: "https://huggingface.co/docs/hub/sample-factory",
-		snippets: sampleFactory,
-	},
-	"stable-baselines3": {
-		btnLabel: "stable-baselines3",
-		repoName: "stable-baselines3",
-		repoUrl: "https://github.com/huggingface/huggingface_sb3",
-		docsUrl: "https://huggingface.co/docs/hub/stable-baselines3",
-		snippets: stableBaselines3,
-	},
-	"ml-agents": {
-		btnLabel: "ml-agents",
-		repoName: "ml-agents",
-		repoUrl: "https://github.com/Unity-Technologies/ml-agents",
-		docsUrl: "https://huggingface.co/docs/hub/ml-agents",
-		snippets: mlAgents,
-	},
-	"unity-sentis": {
-		btnLabel: "unity-sentis",
-		repoName: "unity-sentis",
-		repoUrl: "https://github.com/Unity-Technologies/sentis-samples",
-		snippets: sentis,
-	},
-	pythae: {
-		btnLabel: "pythae",
-		repoName: "pythae",
-		repoUrl: "https://github.com/clementchadebec/benchmark_VAE",
-		snippets: pythae,
-	},
-} as const;
diff --git a/packages/tasks/src/model-libraries.ts b/packages/tasks/src/model-libraries.ts
index 59123724a..e12602e1a 100644
--- a/packages/tasks/src/model-libraries.ts
+++ b/packages/tasks/src/model-libraries.ts
@@ -1,52 +1,333 @@
+import * as snippets from "./model-libraries-snippets";
+import type { ModelData } from "./model-data";
+
+/**
+ * Elements configurable by a model library.
+ */
+export interface LibraryUiElement {
+	/**
+	 * Pretty name of the library.
+	 * displayed in tags, and on the main
+	 * call-to-action button on the model page.
+	 */
+	prettyLabel: string;
+	/**
+	 * Repo name of the library's (usually on GitHub) code repo
+	 */
+	repoName: string;
+	/**
+	 * URL to library's (usually on GitHub) code repo
+	 */
+	repoUrl: string;
+	/**
+	 * URL to library's docs
+	 */
+	docsUrl?: string;
+	/**
+	 * Code snippet(s) displayed on model page
+	 */
+	snippets?: (model: ModelData) => string[];
+	/**
+	 * should we display this library in hf.co/models filter
+	 * (only for popular libraries with > 100 models)
+	 */
+	filter?: boolean;
+}
+
 /**
  * Add your new library here.
  *
  * This is for modeling (= architectures) libraries, not for file formats (like ONNX, etc).
- * File formats live in an enum inside the internal codebase.
+ * (unlike libraries, file formats live in an enum inside the internal codebase.)
+ *
+ * Doc on how to add a library to the Hub:
+ *
+ * https://huggingface.co/docs/hub/models-adding-libraries
+ *
+ * /!\ IMPORTANT
+ *
+ * The key you choose is the tag your models have in their library_name on the Hub.
  */
-export enum ModelLibrary {
-	"adapter-transformers" = "Adapters",
-	"allennlp" = "allenNLP",
-	"asteroid" = "Asteroid",
-	"bertopic" = "BERTopic",
-	"diffusers" = "Diffusers",
-	"doctr" = "docTR",
-	"espnet" = "ESPnet",
-	"fairseq" = "Fairseq",
-	"flair" = "Flair",
-	"keras" = "Keras",
-	"k2" = "K2",
-	"mlx" = "MLX",
-	"nemo" = "NeMo",
-	"open_clip" = "OpenCLIP",
-	"paddlenlp" = "PaddleNLP",
-	"peft" = "PEFT",
-	"pyannote-audio" = "pyannote.audio",
-	"sample-factory" = "Sample Factory",
-	"sentence-transformers" = "Sentence Transformers",
-	"setfit" = "SetFit",
-	"sklearn" = "Scikit-learn",
-	"spacy" = "spaCy",
-	"span-marker" = "SpanMarker",
-	"speechbrain" = "speechbrain",
-	"tensorflowtts" = "TensorFlowTTS",
-	"timm" = "Timm",
-	"fastai" = "fastai",
-	"transformers" = "Transformers",
-	"transformers.js" = "Transformers.js",
-	"stanza" = "Stanza",
-	"fasttext" = "fastText",
-	"stable-baselines3" = "Stable-Baselines3",
-	"ml-agents" = "Unity ML-Agents",
-	"pythae" = "Pythae",
-	"mindspore" = "MindSpore",
-	"unity-sentis" = "Unity Sentis",
-}
 
-export type ModelLibraryKey = keyof typeof ModelLibrary;
+export const MODEL_LIBRARIES_UI_ELEMENTS = {
+	"adapter-transformers": {
+		prettyLabel: "Adapters",
+		repoName: "adapters",
+		repoUrl: "https://github.com/Adapter-Hub/adapters",
+		docsUrl: "https://huggingface.co/docs/hub/adapters",
+		snippets: snippets.adapters,
+		filter: true,
+	},
+	allennlp: {
+		prettyLabel: "AllenNLP",
+		repoName: "AllenNLP",
+		repoUrl: "https://github.com/allenai/allennlp",
+		docsUrl: "https://huggingface.co/docs/hub/allennlp",
+		snippets: snippets.allennlp,
+		filter: true,
+	},
+	asteroid: {
+		prettyLabel: "Asteroid",
+		repoName: "Asteroid",
+		repoUrl: "https://github.com/asteroid-team/asteroid",
+		docsUrl: "https://huggingface.co/docs/hub/asteroid",
+		snippets: snippets.asteroid,
+		filter: true,
+	},
+	bertopic: {
+		prettyLabel: "BERTopic",
+		repoName: "BERTopic",
+		repoUrl: "https://github.com/MaartenGr/BERTopic",
+		snippets: snippets.bertopic,
+		filter: true,
+	},
+	diffusers: {
+		prettyLabel: "Diffusers",
+		repoName: "🤗/diffusers",
+		repoUrl: "https://github.com/huggingface/diffusers",
+		docsUrl: "https://huggingface.co/docs/hub/diffusers",
+		snippets: snippets.diffusers,
+		filter: true,
+	},
+	doctr: {
+		prettyLabel: "docTR",
+		repoName: "doctr",
+		repoUrl: "https://github.com/mindee/doctr",
+	},
+	espnet: {
+		prettyLabel: "ESPnet",
+		repoName: "ESPnet",
+		repoUrl: "https://github.com/espnet/espnet",
+		docsUrl: "https://huggingface.co/docs/hub/espnet",
+		snippets: snippets.espnet,
+		filter: true,
+	},
+	fairseq: {
+		prettyLabel: "Fairseq",
+		repoName: "fairseq",
+		repoUrl: "https://github.com/pytorch/fairseq",
+		snippets: snippets.fairseq,
+		filter: true,
+	},
+	fastai: {
+		prettyLabel: "fastai",
+		repoName: "fastai",
+		repoUrl: "https://github.com/fastai/fastai",
+		docsUrl: "https://huggingface.co/docs/hub/fastai",
+		snippets: snippets.fastai,
+		filter: true,
+	},
+	fasttext: {
+		prettyLabel: "fastText",
+		repoName: "fastText",
+		repoUrl: "https://fasttext.cc/",
+		snippets: snippets.fasttext,
+		filter: true,
+	},
+	flair: {
+		prettyLabel: "Flair",
+		repoName: "Flair",
+		repoUrl: "https://github.com/flairNLP/flair",
+		docsUrl: "https://huggingface.co/docs/hub/flair",
+		snippets: snippets.flair,
+		filter: true,
+	},
+	keras: {
+		prettyLabel: "Keras",
+		repoName: "Keras",
+		repoUrl: "https://github.com/keras-team/keras",
+		docsUrl: "https://huggingface.co/docs/hub/keras",
+		snippets: snippets.keras,
+		filter: true,
+	},
+	k2: {
+		prettyLabel: "K2",
+		repoName: "k2",
+		repoUrl: "https://github.com/k2-fsa/k2",
+	},
+	mindspore: {
+		prettyLabel: "MindSpore",
+		repoName: "mindspore",
+		repoUrl: "https://github.com/mindspore-ai/mindspore",
+	},
+	"ml-agents": {
+		prettyLabel: "ml-agents",
+		repoName: "ml-agents",
+		repoUrl: "https://github.com/Unity-Technologies/ml-agents",
+		docsUrl: "https://huggingface.co/docs/hub/ml-agents",
+		snippets: snippets.mlAgents,
+		filter: true,
+	},
+	mlx: {
+		prettyLabel: "MLX",
+		repoName: "MLX",
+		repoUrl: "https://github.com/ml-explore/mlx-examples/tree/main",
+		snippets: snippets.mlx,
+		filter: true,
+	},
+	nemo: {
+		prettyLabel: "NeMo",
+		repoName: "NeMo",
+		repoUrl: "https://github.com/NVIDIA/NeMo",
+		snippets: snippets.nemo,
+		filter: true,
+	},
+	open_clip: {
+		prettyLabel: "OpenCLIP",
+		repoName: "OpenCLIP",
+		repoUrl: "https://github.com/mlfoundations/open_clip",
+		snippets: snippets.open_clip,
+		filter: true,
+	},
+	paddlenlp: {
+		prettyLabel: "paddlenlp",
+		repoName: "PaddleNLP",
+		repoUrl: "https://github.com/PaddlePaddle/PaddleNLP",
+		docsUrl: "https://huggingface.co/docs/hub/paddlenlp",
+		snippets: snippets.paddlenlp,
+		filter: true,
+	},
+	peft: {
+		prettyLabel: "PEFT",
+		repoName: "PEFT",
+		repoUrl: "https://github.com/huggingface/peft",
+		snippets: snippets.peft,
+		filter: true,
+	},
+	"pyannote-audio": {
+		prettyLabel: "pyannote.audio",
+		repoName: "pyannote-audio",
+		repoUrl: "https://github.com/pyannote/pyannote-audio",
+		snippets: snippets.pyannote_audio,
+		filter: true,
+	},
+	pythae: {
+		prettyLabel: "pythae",
+		repoName: "pythae",
+		repoUrl: "https://github.com/clementchadebec/benchmark_VAE",
+		snippets: snippets.pythae,
+		filter: true,
+	},
+	"sample-factory": {
+		prettyLabel: "sample-factory",
+		repoName: "sample-factory",
+		repoUrl: "https://github.com/alex-petrenko/sample-factory",
+		docsUrl: "https://huggingface.co/docs/hub/sample-factory",
+		snippets: snippets.sampleFactory,
+		filter: true,
+	},
+	"sentence-transformers": {
+		prettyLabel: "sentence-transformers",
+		repoName: "sentence-transformers",
+		repoUrl: "https://github.com/UKPLab/sentence-transformers",
+		docsUrl: "https://huggingface.co/docs/hub/sentence-transformers",
+		snippets: snippets.sentenceTransformers,
+		filter: true,
+	},
+	setfit: {
+		prettyLabel: "setfit",
+		repoName: "setfit",
+		repoUrl: "https://github.com/huggingface/setfit",
+		docsUrl: "https://huggingface.co/docs/hub/setfit",
+		snippets: snippets.setfit,
+		filter: true,
+	},
+	sklearn: {
+		prettyLabel: "Scikit-learn",
+		repoName: "Scikit-learn",
+		repoUrl: "https://github.com/scikit-learn/scikit-learn",
+		snippets: snippets.sklearn,
+		filter: true,
+	},
+	spacy: {
+		prettyLabel: "spaCy",
+		repoName: "spaCy",
+		repoUrl: "https://github.com/explosion/spaCy",
+		docsUrl: "https://huggingface.co/docs/hub/spacy",
+		snippets: snippets.spacy,
+		filter: true,
+	},
+	"span-marker": {
+		prettyLabel: "SpanMarker",
+		repoName: "SpanMarkerNER",
+		repoUrl: "https://github.com/tomaarsen/SpanMarkerNER",
+		docsUrl: "https://huggingface.co/docs/hub/span_marker",
+		snippets: snippets.span_marker,
+		filter: true,
+	},
+	speechbrain: {
+		prettyLabel: "speechbrain",
+		repoName: "speechbrain",
+		repoUrl: "https://github.com/speechbrain/speechbrain",
+		docsUrl: "https://huggingface.co/docs/hub/speechbrain",
+		snippets: snippets.speechbrain,
+		filter: true,
+	},
+	"stable-baselines3": {
+		prettyLabel: "stable-baselines3",
+		repoName: "stable-baselines3",
+		repoUrl: "https://github.com/huggingface/huggingface_sb3",
+		docsUrl: "https://huggingface.co/docs/hub/stable-baselines3",
+		snippets: snippets.stableBaselines3,
+		filter: true,
+	},
+	stanza: {
+		prettyLabel: "Stanza",
+		repoName: "stanza",
+		repoUrl: "https://github.com/stanfordnlp/stanza",
+		docsUrl: "https://huggingface.co/docs/hub/stanza",
+		snippets: snippets.stanza,
+		filter: true,
+	},
+	tensorflowtts: {
+		prettyLabel: "TensorFlowTTS",
+		repoName: "TensorFlowTTS",
+		repoUrl: "https://github.com/TensorSpeech/TensorFlowTTS",
+		snippets: snippets.tensorflowtts,
+	},
+	timm: {
+		prettyLabel: "timm",
+		repoName: "pytorch-image-models",
+		repoUrl: "https://github.com/rwightman/pytorch-image-models",
+		docsUrl: "https://huggingface.co/docs/hub/timm",
+		snippets: snippets.timm,
+		filter: true,
+	},
+	transformers: {
+		prettyLabel: "Transformers",
+		repoName: "🤗/transformers",
+		repoUrl: "https://github.com/huggingface/transformers",
+		docsUrl: "https://huggingface.co/docs/hub/transformers",
+		snippets: snippets.transformers,
+		filter: true,
+	},
+	"transformers.js": {
+		prettyLabel: "Transformers.js",
+		repoName: "transformers.js",
+		repoUrl: "https://github.com/xenova/transformers.js",
+		docsUrl: "https://huggingface.co/docs/hub/transformers-js",
+		snippets: snippets.transformersJS,
+		filter: true,
+	},
+	"unity-sentis": {
+		prettyLabel: "unity-sentis",
+		repoName: "unity-sentis",
+		repoUrl: "https://github.com/Unity-Technologies/sentis-samples",
+		snippets: snippets.sentis,
+		filter: true,
+	},
+} satisfies Record<string, LibraryUiElement>;
+
+export type ModelLibraryKey = keyof typeof MODEL_LIBRARIES_UI_ELEMENTS;
 
-export const ALL_MODEL_LIBRARY_KEYS = Object.keys(ModelLibrary) as ModelLibraryKey[];
+export const ALL_MODEL_LIBRARY_KEYS = Object.keys(MODEL_LIBRARIES_UI_ELEMENTS) as ModelLibraryKey[];
 
-export const ALL_DISPLAY_MODEL_LIBRARY_KEYS = ALL_MODEL_LIBRARY_KEYS.filter(
-	(k) => !["doctr", "k2", "mindspore", "tensorflowtts"].includes(k)
-);
+export const ALL_DISPLAY_MODEL_LIBRARY_KEYS = (
+	Object.entries(MODEL_LIBRARIES_UI_ELEMENTS as Record<ModelLibraryKey, LibraryUiElement>) as [
+		ModelLibraryKey,
+		LibraryUiElement,
+	][]
+)
+	// eslint-disable-next-line @typescript-eslint/no-unused-vars
+	.filter(([_, v]) => v.filter)
+	.map(([k]) => k);