11 |
11 |
12 |
13 | #### How does it work
14 |
15 | When Academia Hub is enabled, any affiliated user will need to add their `@your-university-name.edu` email address (or other university domain) to their HF account.
16 |
17 | Academia Hub is designed for:
18 | - **Students:** Unlock powerful features to learn about AI and Machine learning in the most efficient way.
19 | - **Researchers:** Collaborate with peers using the standard AI ecosystem of tools.
20 | - **Faculty members:** Enhance your classes' projects with PRO capabilities.
21 |
22 | Key Features of Academia Hub:
23 |
24 | - **ZeroGPU:** Get 5x usage quota and highest GPU queue priority.
25 | - **Spaces Hosting:** Create ZeroGPU Spaces with H200 hardware.
26 | - **Spaces Dev Mode:** Fast iterations via SSH/VS Code for Spaces.
27 | - **Inference Providers:** Get monthly included credits across all Inference Providers.
28 | - **Dataset Viewer:** Activate it on private datasets.
29 | - **Blog Articles:** Publish articles to the Hugging Face blog.
30 | - **Social Posts:** Share short updates with the community.
31 | - **Seamless Onboarding:** Quick and secure sign-up with your academic email.
32 | - **Enhanced Collaboration:** Connect with peers and mentors.
33 | - **Exclusive Resources:** Access datasets, models, and projects tailored for academia.
34 |
35 | #### Eligibility
36 |
37 | - Must possess a valid university or college email address.
38 | - Open to all students regardless of discipline or level of study.
39 | - Pricing: Academia Hub is priced based on volume of usage and number of active users at your institution.
40 |
41 | #### Subscribe
42 |
43 | Ask your university's IT or Procurement Team to get in touch to initiate the subscription process to the Academia Hub today. Academia Hub can not be initiated by students themselves.
44 |
45 | #### Private storage
46 |
47 | Academia Hub includes 1TB of [private repository storage](./storage-limits) per seat in the subscription, i.e. if your institution has 400 students and faculty, your students and faculty have 400TB included storage across their private models and datasets.
48 |
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/docs/hub/advanced-compute-options.md:
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1 | # Advanced Compute Options
2 |
3 | 
19 | 
24 | 
15 | 
20 | 
9 | 
10 | 
14 | 
15 | 
8 | 
15 | 
16 | 
11 | 
11 | 
12 | 
13 | 
14 | 
15 | 
16 | 
15 | 
20 | 
11 | 
12 |
13 |
14 | In this section we will document the following Enterprise Hub features:
15 |
16 | - [Single Sign-On (SSO)](./enterprise-sso)
17 | - [Advanced Single Sign-On (SSO)](./enterprise-hub-advanced-sso)
18 | - [Audit Logs](./audit-logs)
19 | - [Storage Regions](./storage-regions)
20 | - [Dataset viewer for Private datasets](./enterprise-hub-datasets)
21 | - [Resource Groups](./security-resource-groups)
22 | - [Advanced Compute Options](./advanced-compute-options)
23 | - [Advanced Security](./enterprise-hub-advanced-security)
24 | - [Tokens Management](./enterprise-hub-tokens-management)
25 | - [Analytics](./enterprise-hub-analytics)
26 | - [Network Security](./enterprise-hub-network-security)
27 | - [Gating Group Collections](./enterprise-hub-gating-group-collections)
28 |
29 | Finally, Enterprise Hub includes 1TB of [private repository storage](./storage-limits) per seat in the subscription, i.e. if your organization has 40 members, then you have 40TB included storage for your private models and datasets.
30 |
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/docs/hub/enterprise-sso.md:
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1 | # Single Sign-On (SSO)
2 |
3 | 
17 | 
22 | 
41 | 
42 | 
45 | 
46 | 
57 | 
58 | 
61 | 
62 | 
9 | 
10 | 
16 | 
17 | 
23 | 
24 | 
17 | 
39 | 
40 |
41 |
42 | See result [here](https://huggingface.co/alvdansen/littletinies#little-tinies).
43 |
44 | > Hint: Support of Card Components through the GUI editor coming soon...
45 |
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/docs/hub/models-advanced.md:
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1 | # Advanced Topics
2 |
3 | ## Contents
4 |
5 | - [Integrate your library with the Hub](./models-adding-libraries)
6 | - [Adding new tasks to the Hub](./models-tasks)
7 | - [GGUF format](./gguf)
8 | - [DDUF format](./dduf)
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/docs/hub/models-inference.md:
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1 | # Inference Providers
2 |
3 | Please refer to the [Inference Providers Documentation](https://huggingface.co/docs/inference-providers) for detailed information.
4 |
5 | ## What is HF-Inference API?
6 |
7 | HF-Inference API is one of the many providers available on the Hugging Face Hub.
8 | It is deployed by Hugging Face ourselves, using text-generation-inference for LLMs for instance. This service used to be called “Inference API (serverless)” prior to Inference Providers.
9 |
10 | For more details about the HF-Inference API, check out its [dedicated page](https://huggingface.co/docs/inference-providers/providers/hf-inference).
11 |
12 | ## What technology do you use to power the HF-Inference API?
13 |
14 | The HF-Inference API is powered by [Inference Endpoints](https://huggingface.co/docs/inference-endpoints/index) under the hood.
15 |
16 | ## Why don't I see an inference widget, or why can't I use the API?
17 |
18 | For some tasks, there might not be support by any Inference Provider, and hence, there is no widget.
19 |
20 | ## How can I see my usage?
21 |
22 | To check usage across all providers, check out your [billing page](https://huggingface.co/settings/billing).
23 |
24 | To check your HF-Inference usage specifically, check out the [Inference Dashboard](https://ui.endpoints.huggingface.co/endpoints). The dashboard shows both your serverless and dedicated endpoints usage.
25 |
26 | ## Is there programmatic access to Inference Providers?
27 |
28 | Yes! We provide client wrappers in both JS and Python:
29 | - [JS (`@huggingface/inference`)](https://huggingface.co/docs/huggingface.js/inference/classes/InferenceClient)
30 | - [Python (`huggingface_hub`)](https://huggingface.co/docs/huggingface_hub/guides/inference)
31 |
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/docs/hub/models-the-hub.md:
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1 | # The Model Hub
2 |
3 | ## What is the Model Hub?
4 |
5 | The Model Hub is where the members of the Hugging Face community can host all of their model checkpoints for simple storage, discovery, and sharing. Download pre-trained models with the [`huggingface_hub` client library](https://huggingface.co/docs/huggingface_hub/index), with 🤗 [`Transformers`](https://huggingface.co/docs/transformers/index) for fine-tuning and other usages or with any of the over [15 integrated libraries](./models-libraries). You can even leverage [Inference Providers](/docs/inference-providers/) or [Inference Endpoints](https://huggingface.co/docs/inference-endpoints) to use models in production settings.
6 |
7 | You can refer to the following video for a guide on navigating the Model Hub:
8 |
9 |
10 |
11 | To learn how to upload models to the Hub, you can refer to the [Repositories Getting Started Guide](./repositories-getting-started).
--------------------------------------------------------------------------------
/docs/hub/models.md:
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1 | # Models
2 |
3 | The Hugging Face Hub hosts many models for a [variety of machine learning tasks](https://huggingface.co/tasks). Models are stored in repositories, so they benefit from [all the features](./repositories) possessed by every repo on the Hugging Face Hub. Additionally, model repos have attributes that make exploring and using models as easy as possible. These docs will take you through everything you'll need to know to find models on the Hub, upload your models, and make the most of everything the Model Hub offers!
4 |
5 | ## Contents
6 |
7 | - [The Model Hub](./models-the-hub)
8 | - [Model Cards](./model-cards)
9 | - [CO2 emissions](./model-cards-co2)
10 | - [Gated models](./models-gated)
11 | - [Libraries](./models-libraries)
12 | - [Uploading Models](./models-uploading)
13 | - [Downloading Models](./models-downloading)
14 | - [Widgets](./models-widgets)
15 | - [Widget Examples](./models-widgets-examples)
16 | - [Inference API](./models-inference)
17 | - [Frequently Asked Questions](./models-faq)
18 | - [Advanced Topics](./models-advanced)
19 | - [Integrating libraries with the Hub](./models-adding-libraries)
20 | - [Tasks](./models-tasks)
21 |
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/docs/hub/moderation.md:
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1 | # Moderation
2 |
3 | 
15 | 
16 | 
24 | 
25 | 
9 | 
10 | 19 | This is the organization grouping all the models and datasets used in the Hugging Face course. 20 |
21 | ``` 22 | 23 | For more examples, take a look at: 24 | 25 | * [Amazon's](https://huggingface.co/spaces/amazon/README/blob/main/README.md) organization card source code 26 | * [spaCy's](https://huggingface.co/spaces/spacy/README/blob/main/README.md) organization card source code. 27 | -------------------------------------------------------------------------------- /docs/hub/organizations-managing.md: -------------------------------------------------------------------------------- 1 | # Managing organizations 2 | 3 | ## Creating an organization 4 | 5 | Visit the [New Organization](https://hf.co/organizations/new) form to create an organization. 6 | 7 | ## Managing members 8 | 9 | New members can be added to an organization by visiting the **Organization settings** and clicking on the **Members** tab. There, you'll be able to generate an invite link, add members individually, or send out email invitations in bulk. If the **Allow requests to join from the organization page** setting is enabled, you'll also be able to approve or reject any pending requests on the **Members** page. 10 | 11 |
13 | 
14 | 
31 | 
32 | 
47 | 
48 | 
35 | 
36 | 
9 | 
10 | 
14 | 
15 | 
23 | 
24 | 
15 |
16 | 
11 | 
12 | 
22 | 
23 | 
26 | 
27 | 
17 |
18 | Here is an example repository you can check out to see the feature in action: [mcpotato/42-eicar-street](https://huggingface.co/mcpotato/42-eicar-street).
19 |
20 | ## Model security refresher
21 |
22 | To share models, we serialize the data structures we use to interact with the models, in order to facilitate storage and transport. Some serialization formats are vulnerable to nasty exploits, such as arbitrary code execution (looking at you pickle), making sharing models potentially dangerous.
23 |
24 | As Hugging Face has become a popular platform for model sharing, we’d like to protect the community from this, hence why we have developed tools like [picklescan](https://github.com/mmaitre314/picklescan) and why we integrate third party scanners.
25 |
26 | Pickle is not the only exploitable format out there, [see for reference](https://github.com/Azure/counterfit/wiki/Abusing-ML-model-file-formats-to-create-malware-on-AI-systems:-A-proof-of-concept) how one can exploit Keras Lambda layers to achieve arbitrary code execution.
27 |
28 |
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/docs/hub/security-secrets.md:
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1 | # Secrets Scanning
2 |
3 | It is important to manage [your secrets (env variables) properly](./spaces-overview#managing-secrets). The most common way people expose their secrets to the outside world is by hard-coding their secrets in their code files directly, which makes it possible for a malicious user to utilize your secrets and services your secrets have access to.
4 |
5 | For example, this is what a compromised `app.py` file might look like:
6 |
7 | ```py
8 | import numpy as np
9 | import scipy as sp
10 |
11 | api_key = "sw-xyz1234567891213"
12 |
13 | def call_inference(prompt: str) -> str:
14 | result = call_api(prompt, api_key)
15 | return result
16 | ```
17 |
18 | To prevent this issue, we run [TruffleHog](https://trufflesecurity.com/trufflehog) on each push you make. TruffleHog scans for hard-coded secrets, and we will send you an email upon detection.
19 |
20 | 
22 | 
10 |
11 | For any other security questions, please feel free to send us an email at security@huggingface.co.
12 |
13 | ## Contents
14 |
15 | - [User Access Tokens](./security-tokens)
16 | - [Two-Factor Authentication (2FA)](./security-2fa)
17 | - [Git over SSH](./security-git-ssh)
18 | - [Signing commits with GPG](./security-gpg)
19 | - [Single Sign-On (SSO)](./security-sso)
20 | - [Malware Scanning](./security-malware)
21 | - [Pickle Scanning](./security-pickle)
22 | - [Secrets Scanning](./security-secrets)
23 | - [Third-party scanner: Protect AI](./security-protectai)
24 | - [Third-party scanner: JFrog](./security-jfrog)
25 | - [Resource Groups](./security-resource-groups)
26 |
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/docs/hub/setfit.md:
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1 | # Using SetFit with Hugging Face
2 |
3 | SetFit is an efficient and prompt-free framework for few-shot fine-tuning of [Sentence Transformers](https://sbert.net/). It achieves high accuracy with little labeled data - for instance, with only 8 labeled examples per class on the Customer Reviews sentiment dataset, SetFit is competitive with fine-tuning RoBERTa Large on the full training set of 3k examples 🤯!
4 |
5 | Compared to other few-shot learning methods, SetFit has several unique features:
6 |
7 | * 🗣 **No prompts or verbalizers:** Current techniques for few-shot fine-tuning require handcrafted prompts or verbalizers to convert examples into a format suitable for the underlying language model. SetFit dispenses with prompts altogether by generating rich embeddings directly from text examples.
8 | * 🏎 **Fast to train:** SetFit doesn't require large-scale models like [T0](https://huggingface.co/bigscience/T0) or GPT-3 to achieve high accuracy. As a result, it is typically an order of magnitude (or more) faster to train and run inference with.
9 | * 🌎 **Multilingual support**: SetFit can be used with any [Sentence Transformer](https://huggingface.co/models?library=sentence-transformers&sort=downloads) on the Hub, which means you can classify text in multiple languages by simply fine-tuning a multilingual checkpoint.
10 |
11 | ## Exploring SetFit on the Hub
12 |
13 | You can find SetFit models by filtering at the left of the [models page](https://huggingface.co/models?library=setfit).
14 |
15 | All models on the Hub come with these useful features:
16 | 1. An automatically generated model card with a brief description.
17 | 2. An interactive widget you can use to play with the model directly in the browser.
18 | 3. An Inference API that allows you to make inference requests.
19 |
20 | ## Installation
21 |
22 | To get started, you can follow the [SetFit installation guide](https://huggingface.co/docs/setfit/installation). You can also use the following one-line install through pip:
23 |
24 | ```
25 | pip install -U setfit
26 | ```
27 |
28 | ## Using existing models
29 |
30 | All `setfit` models can easily be loaded from the Hub.
31 |
32 | ```py
33 | from setfit import SetFitModel
34 |
35 | model = SetFitModel.from_pretrained("tomaarsen/setfit-paraphrase-mpnet-base-v2-sst2-8-shot")
36 | ```
37 |
38 | Once loaded, you can use [`SetFitModel.predict`](https://huggingface.co/docs/setfit/reference/main#setfit.SetFitModel.predict) to perform inference.
39 |
40 | ```py
41 | model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
42 | ```
43 | ```bash
44 | ['positive', 'negative']
45 | ```
46 |
47 | If you want to load a specific SetFit model, you can click `Use in SetFit` and you will be given a working snippet!
48 |
49 | ## Additional resources
50 | * [All SetFit models available on the Hub](https://huggingface.co/models?library=setfit)
51 | * SetFit [repository](https://github.com/huggingface/setfit)
52 | * SetFit [docs](https://huggingface.co/docs/setfit)
53 | * SetFit [paper](https://arxiv.org/abs/2209.11055)
54 |
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/docs/hub/spaces-advanced.md:
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1 | # Advanced Topics
2 |
3 | ## Contents
4 |
5 | - [Using OpenCV in Spaces](./spaces-using-opencv)
6 | - [More ways to create Spaces](./spaces-more-ways-to-create)
7 | - [Managing Spaces with Github Actions](./spaces-github-actions)
8 | - [Managing Spaces with CircleCI Workflows](./spaces-circleci)
9 | - [Custom Python Spaces](./spaces-sdks-python)
10 | - [How to Add a Space to ArXiv](./spaces-add-to-arxiv)
11 | - [Cookie limitations in Spaces](./spaces-cookie-limitations)
12 | - [How to handle URL parameters in Spaces](./spaces-handle-url-parameters)
13 |
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/docs/hub/spaces-circleci.md:
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1 | # Managing Spaces with CircleCI Workflows
2 |
3 | You can keep your app in sync with your GitHub repository with a **CircleCI workflow**.
4 |
5 | [CircleCI](https://circleci.com) is a continuous integration and continuous delivery (CI/CD) platform that helps automate the software development process. A [CircleCI workflow](https://circleci.com/docs/workflows/) is a set of automated tasks defined in a configuration file, orchestrated by CircleCI, to streamline the process of building, testing, and deploying software applications.
6 |
7 | *Note: For files larger than 10MB, Spaces requires Git-LFS. If you don't want to use Git-LFS, you may need to review your files and check your history. Use a tool like [BFG Repo-Cleaner](https://rtyley.github.io/bfg-repo-cleaner/) to remove any large files from your history. BFG Repo-Cleaner will keep a local copy of your repository as a backup.*
8 |
9 | First, set up your GitHub repository and Spaces app together. Add your Spaces app as an additional remote to your existing Git repository.
10 |
11 | ```bash
12 | git remote add space https://huggingface.co/spaces/HF_USERNAME/SPACE_NAME
13 | ```
14 |
15 | Then force push to sync everything for the first time:
16 |
17 | ```bash
18 | git push --force space main
19 | ```
20 |
21 | Next, set up a [CircleCI workflow](https://circleci.com/docs/workflows/) to push your `main` git branch to Spaces.
22 |
23 | In the example below:
24 |
25 | * Replace `HF_USERNAME` with your username and `SPACE_NAME` with your Space name.
26 | * [Create a context in CircleCI](https://circleci.com/docs/contexts/) and add an env variable into it called *HF_PERSONAL_TOKEN* (you can give it any name, use the key you create in place of HF_PERSONAL_TOKEN) and the value as your Hugging Face API token. You can find your Hugging Face API token under **API Tokens** on [your Hugging Face profile](https://huggingface.co/settings/tokens).
27 |
28 | ```yaml
29 | version: 2.1
30 |
31 | workflows:
32 | main:
33 | jobs:
34 | - sync-to-huggingface:
35 | context:
36 | - HuggingFace
37 | filters:
38 | branches:
39 | only:
40 | - main
41 |
42 | jobs:
43 | sync-to-huggingface:
44 | docker:
45 | - image: alpine
46 | resource_class: small
47 | steps:
48 | - run:
49 | name: install git
50 | command: apk update && apk add openssh-client git
51 | - checkout
52 | - run:
53 | name: push to Huggingface hub
54 | command: |
55 | git config user.email "
19 | 
20 | 
45 | 
8 | 
9 | 
11 |
12 |
13 | You should provide a MongoDB endpoint where your chats will be written. If you leave this section blank, your logs will be persisted to a database inside the Space. Note that Hugging Face does not have access to your chats. You can configure the name and the theme of the Space by providing the application name and application color parameters.
14 | Below this, you can select the Hugging Face Hub ID of the model you wish to serve. You can also change the generation hyperparameters in the dictionary below in JSON format.
15 |
16 | _Note_: If you'd like to deploy a model with gated access or a model in a private repository, you can simply provide `HF_TOKEN` in repository secrets. You need to set its value to an access token you can get from [here](https://huggingface.co/settings/tokens).
17 |
18 | 
19 |
20 | Once the creation is complete, you will see `Building` on your Space. Once built, you can try your own HuggingChat!
21 |
22 | 
23 |
24 | Start chatting!
25 |
26 | 
27 |
28 | ## Read more
29 |
30 | - [HF Docker Spaces](https://huggingface.co/docs/hub/spaces-sdks-docker)
31 | - [chat-ui GitHub Repository](https://github.com/huggingface/chat-ui)
32 | - [text-generation-inference GitHub repository](https://github.com/huggingface/text-generation-inference)
33 |
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/docs/hub/spaces-sdks-docker-evidence.md:
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1 | # Evidence on Spaces
2 |
3 | **Evidence** is an open-source framework designed for building data-driven applications, reports, and dashboards using SQL and Markdown. With Evidence, you can quickly create decision-support tools, reports, and interactive dashboards without relying on traditional drag-and-drop business intelligence (BI) platforms.
4 |
5 | Evidence enables you to:
6 | - Write reports and dashboards directly in Markdown with SQL-backed components.
7 | - Integrate data from multiple sources, including SQL databases and APIs.
8 | - Use templated pages to automatically generate multiple pages based on a single template.
9 | - Deploy reports seamlessly to various hosting solutions.
10 |
11 | Visit [Evidence’s documentation](https://docs.evidence.dev/) for guides, examples, and best practices for using Evidence to create data products.
12 |
13 | ## Deploy Evidence on Spaces
14 |
15 | You can deploy Evidence on Hugging Face Spaces with just a few clicks:
16 |
17 |
18 | 
19 |
20 |
21 | Once created, the Space will display `Building` status. Refresh the page if the status doesn't automatically update to `Running`.
22 |
23 | Your Evidence app will automatically be deployed on Hugging Face Spaces.
24 |
25 | ## Editing your Evidence app from the CLI
26 |
27 | To edit your app, clone the Space and edit the files locally.
28 |
29 | ```bash
30 | git clone https://huggingface.co/spaces/your-username/your-space-name
31 | cd your-space-name
32 | npm install
33 | npm run sources
34 | npm run dev
35 | ```
36 |
37 | You can then modify pages/index.md to change the content of your app.
38 |
39 | ## Editing your Evidence app from VS Code
40 |
41 | The easiest way to develop with Evidence is using the [VS Code Extension](https://marketplace.visualstudio.com/items?itemName=Evidence.evidence-vscode):
42 |
43 | 1. Install the extension from the VS Code Marketplace
44 | 2. Open the Command Palette (Ctrl/Cmd + Shift + P) and enter `Evidence: Copy Existing Project`
45 | 3. Paste the URL of the Hugging Face Spaces Evidence app you'd like to copy (e.g. `https://huggingface.co/spaces/your-username/your-space-name`) and press Enter
46 | 4. Select the folder you'd like to clone the project to and press Enter
47 | 5. Press `Start Evidence` in the bottom status bar
48 |
49 | Check out the docs for [alternative install methods](https://docs.evidence.dev/getting-started/install-evidence), Github Codespaces, and alongside dbt.
50 |
51 | ## Learning More
52 |
53 | - [Docs](https://docs.evidence.dev/)
54 | - [Github](https://github.com/evidence-dev/evidence)
55 | - [Slack Community](https://slack.evidence.dev/)
56 | - [Evidence Home Page](https://www.evidence.dev)
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/docs/hub/spaces-sdks-docker-examples.md:
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1 | # Docker Spaces Examples
2 |
3 | We gathered some example demos in the [Spaces Examples](https://huggingface.co/SpacesExamples) organization. Please check them out!
4 |
5 | * Dummy FastAPI app: https://huggingface.co/spaces/DockerTemplates/fastapi_dummy
6 | * FastAPI app serving a static site and using `transformers`: https://huggingface.co/spaces/DockerTemplates/fastapi_t5
7 | * Phoenix app for https://huggingface.co/spaces/DockerTemplates/single_file_phx_bumblebee_ml
8 | * HTTP endpoint in Go with query parameters https://huggingface.co/spaces/XciD/test-docker-go?q=Adrien
9 | * Shiny app written in Python https://huggingface.co/spaces/elonmuskceo/shiny-orbit-simulation
10 | * Genie.jl app in Julia https://huggingface.co/spaces/nooji/GenieOnHuggingFaceSpaces
11 | * Argilla app for data labelling and curation: https://huggingface.co/spaces/argilla/live-demo and [write-up about hosting Argilla on Spaces](./spaces-sdks-docker-argilla) by [@dvilasuero](https://huggingface.co/dvilasuero) 🎉
12 | * JupyterLab and VSCode: https://huggingface.co/spaces/DockerTemplates/docker-examples by [@camenduru](https://twitter.com/camenduru) and [@nateraw](https://hf.co/nateraw).
13 | * Zeno app for interactive model evaluation: https://huggingface.co/spaces/zeno-ml/diffusiondb and [instructions for setup](https://zenoml.com/docs/deployment#hugging-face-spaces)
14 | * Gradio App: https://huggingface.co/spaces/sayakpaul/demo-docker-gradio
15 |
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/docs/hub/spaces-sdks-docker-jupyter.md:
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1 | # JupyterLab on Spaces
2 |
3 | [JupyterLab](https://jupyter.org/) is a web-based interactive development environment for Jupyter notebooks, code, and data. It is a great tool for data science and machine learning, and it is widely used by the community. With Hugging Face Spaces, you can deploy your own JupyterLab instance and use it for development directly from the Hugging Face website.
4 |
5 | ## ⚡️ Deploy a JupyterLab instance on Spaces
6 |
7 | You can deploy JupyterLab on Spaces with just a few clicks. First, go to [this link](https://huggingface.co/new-space?template=SpacesExamples/jupyterlab) or click the button below:
8 |
9 |
10 |
11 |
12 |
13 | Spaces requires you to define:
14 |
15 | * An **Owner**: either your personal account or an organization you're a
16 | part of.
17 |
18 | * A **Space name**: the name of the Space within the account
19 | you're creating the Space.
20 |
21 | * The **Visibility**: _private_ if you want the
22 | Space to be visible only to you or your organization, or _public_ if you want
23 | it to be visible to other users.
24 |
25 | * The **Hardware**: the hardware you want to use for your JupyterLab instance. This goes from CPUs to H100s.
26 |
27 | * You can optionally configure a `JUPYTER_TOKEN` password to protect your JupyterLab workspace. When unspecified, defaults to `huggingface`. We strongly recommend setting this up if your Space is public or if the Space is in an organization.
28 |
29 | 
35 | 
36 | 
39 | 
40 | 
11 | 
12 | | Python (original) | 19 |Javascript (ours) | 20 |
|---|---|
| 23 | 24 | ```python 25 | from transformers import pipeline 26 | 27 | # Allocate a pipeline for sentiment-analysis 28 | pipe = pipeline('sentiment-analysis') 29 | 30 | out = pipe('I love transformers!') 31 | # [{'label': 'POSITIVE', 'score': 0.999806941}] 32 | ``` 33 | 34 | | 35 |36 | 37 | ```javascript 38 | import { pipeline } from '@huggingface/transformers'; 39 | 40 | // Allocate a pipeline for sentiment-analysis 41 | let pipe = await pipeline('sentiment-analysis'); 42 | 43 | let out = await pipe('I love transformers!'); 44 | // [{'label': 'POSITIVE', 'score': 0.999817686}] 45 | ``` 46 | 47 | | 48 |
25 | 
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25 | 
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25 | 
26 |
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25 | 
26 |
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25 | 
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16 |
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/docs/sagemaker/Makefile:
--------------------------------------------------------------------------------
1 | .PHONY: docs clean help
2 |
3 | docs: clean
4 | @echo "Processing README.md files from examples/gke, examples/cloud-run, and examples/vertex-ai..."
5 | @mkdir -p source/examples
6 | @echo "Converting Jupyter Notebooks to MDX..."
7 | @doc-builder notebook-to-mdx notebooks/sagemaker-sdk/
8 | @echo "Auto-generating example files for documentation..."
9 | @python scripts/auto-generate-examples.py
10 | @echo "Cleaning up generated Markdown Notebook files..."
11 | @find notebooks/sagemaker-sdk -name "sagemaker-notebook.md" -type f -delete
12 | @echo "Generating YAML tree structure and appending to _toctree.yml..."
13 | @python scripts/auto-update-toctree.py
14 | @echo "YAML tree structure appended to docs/source/_toctree.yml"
15 | @echo "Documentation setup complete."
16 |
17 | clean:
18 | @echo "Cleaning up generated documentation..."
19 | @rm -rf source/examples
20 | @awk '/^# GENERATED CONTENT DO NOT EDIT!/,/^# END GENERATED CONTENT/{next} {print}' source/_toctree.yml > source/_toctree.yml.tmp && mv source/_toctree.yml.tmp source/_toctree.yml
21 | @echo "Cleaning up generated Markdown Notebook files (if any)..."
22 | @find notebooks/sagemaker-sdk -name "sagemaker-notebook.md" -type f -delete
23 | @echo "Cleanup complete."
24 |
25 | serve:
26 | @echo "Serving documentation via doc-builder"
27 | doc-builder preview sagemaker source/ --not_python_module
28 |
29 | help:
30 | @echo "Usage:"
31 | @echo " make docs - Auto-generate the examples for the docs"
32 | @echo " make clean - Remove the auto-generated docs"
33 | @echo " make help - Display this help message"
34 |
35 |
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/docs/sagemaker/notebooks/sagemaker-sdk/deploy-embedding-models/assets/cw.png:
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https://raw.githubusercontent.com/huggingface/hub-docs/1204a9db04239eb0b9a8264bdd07294d639331e0/docs/sagemaker/notebooks/sagemaker-sdk/deploy-embedding-models/assets/cw.png
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/docs/sagemaker/notebooks/sagemaker-sdk/fine-tune-embedding-models/assets/requirements.txt:
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1 | transformers==4.41.2
2 | accelerate==0.31.0
3 | sentence-transformers==3.0.0
4 | datasets==2.18.0
5 | torch==2.1.2
6 |
--------------------------------------------------------------------------------
/docs/sagemaker/scripts/auto-update-toctree.py:
--------------------------------------------------------------------------------
1 | import glob
2 | import os
3 | import re
4 | from pathlib import Path
5 |
6 |
7 | def update_toctree_yaml():
8 | input_file = "source/_toctree.yml"
9 | output_file = "source/_toctree.yml"
10 | dirnames = ["sagemaker-sdk"]
11 |
12 | # Read the existing content
13 | with open(input_file, "r") as f:
14 | content = f.read()
15 |
16 | # Find the position between tutorials and reference sections
17 | tutorials_end = content.find("- sections:\n - local: reference/inference-toolkit")
18 | if tutorials_end == -1:
19 | print("Error: Could not find the reference section in the file")
20 | return
21 |
22 | # Generate the new content
23 | new_content = []
24 | new_content.append("# GENERATED CONTENT DO NOT EDIT!")
25 | new_content.append("- title: Examples")
26 | new_content.append(" sections:")
27 |
28 | for dirname in dirnames:
29 | # Get sorted files excluding index
30 | files = sorted(glob.glob(f"source/examples/{dirname}-*.mdx"))
31 | files = [f for f in files if not f.endswith(f"{dirname}-index.mdx")]
32 |
33 | file_entries = []
34 | for file_path in files:
35 | with open(file_path, "r") as mdx_file:
36 | first_line = mdx_file.readline().strip()
37 | if first_line.startswith("# "):
38 | title = first_line[2:].strip()
39 | base_name = Path(file_path).stem
40 | file_entries.append((base_name, title))
41 | else:
42 | print(f"⚠️ Skipping {Path(file_path).name} - missing H1 title")
43 | continue
44 |
45 | # Write directory section
46 | new_content.append(" - title: SageMaker SDK")
47 | new_content.append(" isExpanded: false")
48 |
49 | for idx, (base, title) in enumerate(file_entries):
50 | if idx == 0:
51 | new_content.append(" sections:")
52 | new_content.append(f" - local: examples/{base}")
53 | new_content.append(f' title: "{title}"')
54 |
55 | new_content.append("# END GENERATED CONTENT")
56 |
57 | # Insert the new content
58 | updated_content = content[:tutorials_end] + "\n" + "\n".join(new_content) + "\n" + content[tutorials_end:]
59 |
60 | # Write the updated content back to the file
61 | with open(output_file, "w") as f:
62 | f.write(updated_content)
63 |
64 |
65 | if __name__ == "__main__":
66 | update_toctree_yaml()
67 |
--------------------------------------------------------------------------------
/docs/sagemaker/source/.gitignore:
--------------------------------------------------------------------------------
1 | examples/
2 |
--------------------------------------------------------------------------------
/docs/sagemaker/source/_toctree.yml:
--------------------------------------------------------------------------------
1 | - sections:
2 | - local: index
3 | title: Hugging Face on AWS
4 | title: Get Started
5 | isExpanded: true
6 | - sections:
7 | - local: dlcs/introduction
8 | title: Introduction
9 | - local: dlcs/available
10 | title: Available DLCs on AWS
11 | title: Deep Learning Containers (DLCs)
12 | isExpanded: true
13 | - sections:
14 | - local: tutorials/index
15 | title: Introduction
16 | - sections:
17 | - local: tutorials/sagemaker-sdk/sagemaker-sdk-quickstart
18 | title: SageMaker SDK Quickstart
19 | - local: tutorials/sagemaker-sdk/training-sagemaker-sdk
20 | title: Train models with SageMaker SDK
21 | - local: tutorials/sagemaker-sdk/deploy-sagemaker-sdk
22 | title: Deploy models with SageMaker SDK
23 | title: SageMaker SDK
24 | isExpanded: false
25 | - sections:
26 | - local: tutorials/jumpstart/jumpstart-quickstart
27 | title: Jumpstart Quickstart
28 | title: Jumpstart
29 | isExpanded: false
30 | - sections:
31 | - local: tutorials/bedrock/bedrock-quickstart
32 | title: Bedrock Quickstart
33 | title: Bedrock
34 | isExpanded: false
35 | - sections:
36 | - local: tutorials/compute-services/compute-services-quickstart
37 | title: EC2, ECS and EKS Quickstart
38 | title: EC2, ECS and EKS
39 | isExpanded: false
40 | title: Tutorials
41 | isExpanded: false
42 |
43 | # GENERATED CONTENT DO NOT EDIT!
44 | - title: Examples
45 | sections:
46 | - title: SageMaker SDK
47 | isExpanded: false
48 | sections:
49 | - local: examples/sagemaker-sdk-deploy-embedding-models
50 | title: "How to deploy Embedding Models to Amazon SageMaker using new Hugging Face Embedding DLC"
51 | - local: examples/sagemaker-sdk-deploy-llama-3-3-70b-inferentia2
52 | title: "Deploy Llama 3.3 70B on AWS Inferentia2"
53 | - local: examples/sagemaker-sdk-evaluate-llm-lighteval
54 | title: "Evaluate LLMs with Hugging Face Lighteval on Amazon SageMaker"
55 | - local: examples/sagemaker-sdk-fine-tune-embedding-models
56 | title: "Fine-tune and deploy embedding models with Amazon SageMaker"
57 | # END GENERATED CONTENT
58 | - sections:
59 | - local: reference/inference-toolkit
60 | title: Inference Toolkit API
61 | - local: reference/resources
62 | title: Other Resources
63 | title: Reference
64 | isExpanded: false
65 |
--------------------------------------------------------------------------------
/docs/sagemaker/source/tutorials/compute-services/compute-services-quickstart.md:
--------------------------------------------------------------------------------
1 | # EC2, ECS and EKS Quickstart
2 |
3 | This page is under construction, bear with us!
--------------------------------------------------------------------------------
/docs/sagemaker/source/tutorials/index.md:
--------------------------------------------------------------------------------
1 | # How to
2 |
3 | Take a look at our tutorials about using Hugging Face models on AWS.
4 |
5 | ## Sagemaker SDK
6 |
7 | - [Sagemaker SDK Quickstart](https://huggingface.co/docs/sagemaker/main/en/tutorials/sagemaker-sdk/sagemaker-sdk-quickstart)
8 | - [Train models with Sagemaker SDK](https://huggingface.co/docs/sagemaker/main/en/tutorials/sagemaker-sdk/training-sagemaker-sdk)
9 | - [Deploy models with Sagemaker SDK](https://huggingface.co/docs/sagemaker/main/en/tutorials/sagemaker-sdk/deploy-sagemaker-sdk)
10 |
11 | ## Jumpstart
12 |
13 | - [Sagemaker Jumpstart Quickstart](https://huggingface.co/docs/sagemaker/main/en/tutorials/jumpstart/jumpstart-quickstart)
14 |
15 | ## Bedrock
16 |
17 | - [AWS Bedrock Quickstart](https://huggingface.co/docs/sagemaker/main/en/tutorials/bedrock/bedrock-quickstart)
18 |
19 | ## EC2, ECS and EKS
20 |
21 | - [EC2, ECS and EKS Quickstart](https://huggingface.co/docs/sagemaker/main/en/tutorials/compute-services/compute-services-quickstart)
22 |
--------------------------------------------------------------------------------
/scripts/inference-providers/.gitignore:
--------------------------------------------------------------------------------
1 | dist
--------------------------------------------------------------------------------
/scripts/inference-providers/.prettierignore:
--------------------------------------------------------------------------------
1 | pnpm-lock.yaml
2 | # In order to avoid code samples to have tabs, they don't display well on npm
3 | README.md
4 | dist
5 | *.handlebars
--------------------------------------------------------------------------------
/scripts/inference-providers/README.md:
--------------------------------------------------------------------------------
1 | Install dependencies.
2 |
3 | ```sh
4 | pnpm install
5 | ```
6 |
7 | Generate documentation.
8 |
9 | ```sh
10 | pnpm run generate
11 | ```
--------------------------------------------------------------------------------
/scripts/inference-providers/package.json:
--------------------------------------------------------------------------------
1 | {
2 | "name": "inference-providers-generator",
3 | "packageManager": "pnpm@9.7.1",
4 | "version": "1.0.0",
5 | "description": "",
6 | "main": "index.js",
7 | "type": "module",
8 | "scripts": {
9 | "format": "prettier --write .",
10 | "format:check": "prettier --check .",
11 | "generate": "tsx scripts/generate.ts"
12 | },
13 | "keywords": [],
14 | "author": "",
15 | "license": "ISC",
16 | "dependencies": {
17 | "@huggingface/inference": "^4.0.2",
18 | "@huggingface/tasks": "^0.19.11",
19 | "@types/node": "^22.5.0",
20 | "handlebars": "^4.7.8",
21 | "node": "^20.17.0",
22 | "prettier": "^3.3.3",
23 | "ts-node": "^10.9.2",
24 | "tsx": "^4.17.0",
25 | "type-fest": "^4.25.0",
26 | "typescript": "^5.5.4"
27 | }
28 | }
29 |
--------------------------------------------------------------------------------
/scripts/inference-providers/scripts/.gitignore:
--------------------------------------------------------------------------------
1 | *.js
--------------------------------------------------------------------------------
/scripts/inference-providers/templates/common/follow-us-button.handlebars:
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1 |
4 |
5 |
6 | 
4 | 
5 |
6 | 
32 |
33 | Access the Inference UI Playground and start exploring: [https://huggingface.co/playground](https://huggingface.co/playground)
34 |
35 | ### Using the API
36 |
37 | The API supports:
38 |
39 | * Using the chat completion API compatible with the OpenAI SDK.
40 | * Using grammars, constraints, and tools.
41 | * Streaming the output
42 |
43 | #### Code snippet example for conversational LLMs
44 |
45 | {{{snippets.chat-completion}}}
46 |
47 | #### Code snippet example for conversational VLMs
48 |
49 | {{{snippets.conversational-image-text-to-text}}}
50 |
51 | ### API specification
52 |
53 | #### Request
54 |
55 | {{{constants.specsHeaders}}}
56 |
57 | {{{specs.chat-completion.input}}}
58 |
59 | #### Response
60 |
61 | Output type depends on the `stream` input parameter.
62 | If `stream` is `false` (default), the response will be a JSON object with the following fields:
63 |
64 | {{{specs.chat-completion.output}}}
65 |
66 | If `stream` is `true`, generated tokens are returned as a stream, using Server-Sent Events (SSE).
67 | For more information about streaming, check out [this guide](https://huggingface.co/docs/text-generation-inference/conceptual/streaming).
68 |
69 | {{{specs.chat-completion.stream_output}}}
70 |
71 |
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/scripts/inference-providers/templates/task/feature-extraction.handlebars:
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1 | ## Feature Extraction
2 |
3 | Feature extraction is the task of converting a text into a vector (often called "embedding").
4 |
5 | Example applications:
6 | * Retrieving the most relevant documents for a query (for RAG applications).
7 | * Reranking a list of documents based on their similarity to a query.
8 | * Calculating the similarity between two sentences.
9 |
10 | {{{tips.linksToTaskPage.feature-extraction}}}
11 |
12 | ### Recommended models
13 |
14 | {{#each recommendedModels.feature-extraction}}
15 | - [{{this.id}}](https://huggingface.co/{{this.id}}): {{this.description}}
16 | {{/each}}
17 |
18 | {{{tips.listModelsLink.feature-extraction}}}
19 |
20 | ### Using the API
21 |
22 | {{{snippets.feature-extraction}}}
23 |
24 | ### API specification
25 |
26 | #### Request
27 |
28 | {{{constants.specsHeaders}}}
29 |
30 | {{{specs.feature-extraction.input}}}
31 |
32 | #### Response
33 |
34 | {{{specs.feature-extraction.output}}}
35 |
36 |
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/scripts/inference-providers/templates/task/fill-mask.handlebars:
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1 | ## Fill-mask
2 |
3 | Mask filling is the task of predicting the right word (token to be precise) in the middle of a sequence.
4 |
5 | {{{tips.linksToTaskPage.fill-mask}}}
6 |
7 | ### Recommended models
8 |
9 | {{#each recommendedModels.fill-mask}}
10 | - [{{this.id}}](https://huggingface.co/{{this.id}}): {{this.description}}
11 | {{/each}}
12 |
13 | {{{tips.listModelsLink.fill-mask}}}
14 |
15 | ### Using the API
16 |
17 | {{{snippets.fill-mask}}}
18 |
19 | ### API specification
20 |
21 | #### Request
22 |
23 | {{{constants.specsHeaders}}}
24 |
25 | {{{specs.fill-mask.input}}}
26 |
27 | #### Response
28 |
29 | {{{specs.fill-mask.output}}}
30 |
--------------------------------------------------------------------------------
/scripts/inference-providers/templates/task/image-classification.handlebars:
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1 | ## Image Classification
2 |
3 | Image classification is the task of assigning a label or class to an entire image. Images are expected to have only one class for each image.
4 |
5 | {{{tips.linksToTaskPage.image-classification}}}
6 |
7 | ### Recommended models
8 |
9 | {{#each recommendedModels.image-classification}}
10 | - [{{this.id}}](https://huggingface.co/{{this.id}}): {{this.description}}
11 | {{/each}}
12 |
13 | {{{tips.listModelsLink.image-classification}}}
14 |
15 | ### Using the API
16 |
17 | {{{snippets.image-classification}}}
18 |
19 | ### API specification
20 |
21 | #### Request
22 |
23 | {{{constants.specsHeaders}}}
24 |
25 | {{{specs.image-classification.input}}}
26 |
27 | #### Response
28 |
29 | {{{specs.image-classification.output}}}
30 |
31 |
--------------------------------------------------------------------------------
/scripts/inference-providers/templates/task/image-segmentation.handlebars:
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1 | ## Image Segmentation
2 |
3 | Image Segmentation divides an image into segments where each pixel in the image is mapped to an object.
4 |
5 | {{{tips.linksToTaskPage.image-segmentation}}}
6 |
7 | ### Recommended models
8 |
9 | {{#each recommendedModels.image-segmentation}}
10 | - [{{this.id}}](https://huggingface.co/{{this.id}}): {{this.description}}
11 | {{/each}}
12 |
13 | {{{tips.listModelsLink.image-segmentation}}}
14 |
15 | ### Using the API
16 |
17 | {{{snippets.image-segmentation}}}
18 |
19 | ### API specification
20 |
21 | #### Request
22 |
23 | {{{constants.specsHeaders}}}
24 |
25 | {{{specs.image-segmentation.input}}}
26 |
27 | #### Response
28 |
29 | {{{specs.image-segmentation.output}}}
30 |
31 |
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/scripts/inference-providers/templates/task/image-to-image.handlebars:
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1 | ## Image to Image
2 |
3 | Image-to-image is the task of transforming a source image to match the characteristics of a target image or a target image domain.
4 |
5 | Example applications:
6 | * Transferring the style of an image to another image
7 | * Colorizing a black and white image
8 | * Increasing the resolution of an image
9 |
10 | {{{tips.linksToTaskPage.image-to-image}}}
11 |
12 | ### Recommended models
13 |
14 | {{#each recommendedModels.image-to-image}}
15 | - [{{this.id}}](https://huggingface.co/{{this.id}}): {{this.description}}
16 | {{/each}}
17 |
18 | {{{tips.listModelsLink.image-to-image}}}
19 |
20 | ### Using the API
21 |
22 | {{{snippets.image-to-image}}}
23 |
24 | ### API specification
25 |
26 | #### Request
27 |
28 | {{{constants.specsHeaders}}}
29 |
30 | {{{specs.image-to-image.input}}}
31 |
32 | #### Response
33 |
34 | {{{specs.image-to-image.output}}}
35 |
36 |
--------------------------------------------------------------------------------
/scripts/inference-providers/templates/task/object-detection.handlebars:
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1 | ## Object detection
2 |
3 | Object Detection models allow users to identify objects of certain defined classes. These models receive an image as input and output the images with bounding boxes and labels on detected objects.
4 |
5 | {{{tips.linksToTaskPage.object-detection}}}
6 |
7 | ### Recommended models
8 |
9 | {{#each recommendedModels.object-detection}}
10 | - [{{this.id}}](https://huggingface.co/{{this.id}}): {{this.description}}
11 | {{/each}}
12 |
13 | {{{tips.listModelsLink.object-detection}}}
14 |
15 | ### Using the API
16 |
17 | {{{snippets.object-detection}}}
18 |
19 | ### API specification
20 |
21 | #### Request
22 |
23 | {{{constants.specsHeaders}}}
24 |
25 | {{{specs.object-detection.input}}}
26 |
27 | #### Response
28 |
29 | {{{specs.object-detection.output}}}
30 |
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/scripts/inference-providers/templates/task/question-answering.handlebars:
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1 | ## Question Answering
2 |
3 | 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.
4 |
5 | {{{tips.linksToTaskPage.question-answering}}}
6 |
7 | ### Recommended models
8 |
9 | {{#each recommendedModels.question-answering}}
10 | - [{{this.id}}](https://huggingface.co/{{this.id}}): {{this.description}}
11 | {{/each}}
12 |
13 | {{{tips.listModelsLink.question-answering}}}
14 |
15 | ### Using the API
16 |
17 | {{{snippets.question-answering}}}
18 |
19 | ### API specification
20 |
21 | #### Request
22 |
23 | {{{constants.specsHeaders}}}
24 |
25 | {{{specs.question-answering.input}}}
26 |
27 | #### Response
28 |
29 | {{{specs.question-answering.output}}}
30 |
--------------------------------------------------------------------------------
/scripts/inference-providers/templates/task/summarization.handlebars:
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1 | ## Summarization
2 |
3 | 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.
4 |
5 | {{{tips.linksToTaskPage.summarization}}}
6 |
7 | ### Recommended models
8 |
9 | {{#each recommendedModels.summarization}}
10 | - [{{this.id}}](https://huggingface.co/{{this.id}}): {{this.description}}
11 | {{/each}}
12 |
13 | {{{tips.listModelsLink.summarization}}}
14 |
15 | ### Using the API
16 |
17 | {{{snippets.summarization}}}
18 |
19 | ### API specification
20 |
21 | #### Request
22 |
23 | {{{constants.specsHeaders}}}
24 |
25 | {{{specs.summarization.input}}}
26 |
27 | #### Response
28 |
29 | {{{specs.summarization.output}}}
30 |
--------------------------------------------------------------------------------
/scripts/inference-providers/templates/task/table-question-answering.handlebars:
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1 | ## Table Question Answering
2 |
3 | Table Question Answering (Table QA) is the answering a question about an information on a given table.
4 |
5 | {{{tips.linksToTaskPage.table-question-answering}}}
6 |
7 | ### Recommended models
8 |
9 | {{#each recommendedModels.table-question-answering}}
10 | - [{{this.id}}](https://huggingface.co/{{this.id}}): {{this.description}}
11 | {{/each}}
12 |
13 | {{{tips.listModelsLink.table-question-answering}}}
14 |
15 | ### Using the API
16 |
17 | {{{snippets.table-question-answering}}}
18 |
19 | ### API specification
20 |
21 | #### Request
22 |
23 | {{{constants.specsHeaders}}}
24 |
25 | {{{specs.table-question-answering.input}}}
26 |
27 | #### Response
28 |
29 | {{{specs.table-question-answering.output}}}
30 |
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/scripts/inference-providers/templates/task/text-classification.handlebars:
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1 | ## Text Classification
2 |
3 | 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.
4 |
5 | {{{tips.linksToTaskPage.text-classification}}}
6 |
7 | ### Recommended models
8 |
9 | {{#each recommendedModels.text-classification}}
10 | - [{{this.id}}](https://huggingface.co/{{this.id}}): {{this.description}}
11 | {{/each}}
12 |
13 | {{{tips.listModelsLink.text-classification}}}
14 |
15 | ### Using the API
16 |
17 | {{{snippets.text-classification}}}
18 |
19 | ### API specification
20 |
21 | #### Request
22 |
23 | {{{constants.specsHeaders}}}
24 |
25 | {{{specs.text-classification.input}}}
26 |
27 | #### Response
28 |
29 | {{{specs.text-classification.output}}}
30 |
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/scripts/inference-providers/templates/task/text-generation.handlebars:
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1 | ## Text Generation
2 |
3 | Generate text based on a prompt.
4 |
5 | If you are interested in a Chat Completion task, which generates a response based on a list of messages, check out the [`chat-completion`](./chat_completion) task.
6 |
7 | {{{tips.linksToTaskPage.text-generation}}}
8 |
9 | ### Recommended models
10 |
11 | {{#each recommendedModels.text-generation}}
12 | - [{{this.id}}](https://huggingface.co/{{this.id}}): {{this.description}}
13 | {{/each}}
14 |
15 | {{{tips.listModelsLink.text-generation}}}
16 |
17 | ### Using the API
18 |
19 | {{{snippets.text-generation}}}
20 |
21 | ### API specification
22 |
23 | #### Request
24 |
25 | {{{constants.specsHeaders}}}
26 |
27 | {{{specs.text-generation.input}}}
28 |
29 | #### Response
30 |
31 | Output type depends on the `stream` input parameter.
32 | If `stream` is `false` (default), the response will be a JSON object with the following fields:
33 |
34 | {{{specs.text-generation.output}}}
35 |
36 | If `stream` is `true`, generated tokens are returned as a stream, using Server-Sent Events (SSE).
37 | For more information about streaming, check out [this guide](https://huggingface.co/docs/text-generation-inference/conceptual/streaming).
38 |
39 | {{{specs.text-generation.stream_output}}}
40 |
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/scripts/inference-providers/templates/task/text-to-image.handlebars:
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1 | ## Text to Image
2 |
3 | Generate an image based on a given text prompt.
4 |
5 | {{{tips.linksToTaskPage.text-to-image}}}
6 |
7 | ### Recommended models
8 |
9 | {{#each recommendedModels.text-to-image}}
10 | - [{{this.id}}](https://huggingface.co/{{this.id}}): {{this.description}}
11 | {{/each}}
12 |
13 | {{{tips.listModelsLink.text-to-image}}}
14 |
15 | ### Using the API
16 |
17 | {{{snippets.text-to-image}}}
18 |
19 | ### API specification
20 |
21 | #### Request
22 |
23 | {{{constants.specsHeaders}}}
24 |
25 | {{{specs.text-to-image.input}}}
26 |
27 | #### Response
28 |
29 | {{{specs.text-to-image.output}}}
30 |
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/scripts/inference-providers/templates/task/text-to-video.handlebars:
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1 | ## Text to Video
2 |
3 | Generate an video based on a given text prompt.
4 |
5 | {{{tips.linksToTaskPage.text-to-video}}}
6 |
7 | ### Recommended models
8 |
9 | {{#each recommendedModels.text-to-video}}
10 | - [{{this.id}}](https://huggingface.co/{{this.id}}): {{this.description}}
11 | {{/each}}
12 |
13 | {{{tips.listModelsLink.text-to-video}}}
14 |
15 | ### Using the API
16 |
17 | {{{snippets.text-to-video}}}
18 |
19 | ### API specification
20 |
21 | #### Request
22 |
23 | {{{specs.text-to-video.input}}}
24 |
25 | {{{constants.specsHeaders}}}
26 |
27 | #### Response
28 |
29 | {{{specs.text-to-video.output}}}
30 |
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/scripts/inference-providers/templates/task/token-classification.handlebars:
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1 | ## Token Classification
2 |
3 | Token classification is a 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.
4 |
5 | {{{tips.linksToTaskPage.token-classification}}}
6 |
7 | ### Recommended models
8 |
9 | {{#each recommendedModels.token-classification}}
10 | - [{{this.id}}](https://huggingface.co/{{this.id}}): {{this.description}}
11 | {{/each}}
12 |
13 | {{{tips.listModelsLink.token-classification}}}
14 |
15 | ### Using the API
16 |
17 | {{{snippets.token-classification}}}
18 |
19 | ### API specification
20 |
21 | #### Request
22 |
23 | {{{constants.specsHeaders}}}
24 |
25 | {{{specs.token-classification.input}}}
26 |
27 | #### Response
28 |
29 | {{{specs.token-classification.output}}}
30 |
31 |
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/scripts/inference-providers/templates/task/translation.handlebars:
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1 | ## Translation
2 |
3 | Translation is the task of converting text from one language to another.
4 |
5 | {{{tips.linksToTaskPage.translation}}}
6 |
7 | ### Recommended models
8 |
9 | {{#each recommendedModels.translation}}
10 | - [{{this.id}}](https://huggingface.co/{{this.id}}): {{this.description}}
11 | {{/each}}
12 |
13 | {{{tips.listModelsLink.translation}}}
14 |
15 | ### Using the API
16 |
17 | {{{snippets.translation}}}
18 |
19 | ### API specification
20 |
21 | #### Request
22 |
23 | {{{constants.specsHeaders}}}
24 |
25 | {{{specs.translation.input}}}
26 |
27 | #### Response
28 |
29 | {{{specs.translation.output}}}
30 |
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/scripts/inference-providers/templates/task/zero-shot-classification.handlebars:
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1 | ## Zero-Shot Classification
2 |
3 | Zero-shot text classification is super useful to try out classification with zero code, you simply pass a sentence/paragraph and the possible labels for that sentence, and you get a result. The model has not been necessarily trained on the labels you provide, but it can still predict the correct label.
4 |
5 | {{{tips.linksToTaskPage.zero-shot-classification}}}
6 |
7 | ### Recommended models
8 |
9 | {{#each recommendedModels.zero-shot-classification}}
10 | - [{{this.id}}](https://huggingface.co/{{this.id}}): {{this.description}}
11 | {{/each}}
12 |
13 | {{{tips.listModelsLink.zero-shot-classification}}}
14 |
15 | ### Using the API
16 |
17 | {{{snippets.zero-shot-classification}}}
18 |
19 | ### API specification
20 |
21 | #### Request
22 |
23 | {{{constants.specsHeaders}}}
24 |
25 | {{{specs.zero-shot-classification.input}}}
26 |
27 | #### Response
28 |
29 | {{{specs.zero-shot-classification.output}}}
30 |
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/scripts/inference-providers/tsconfig.json:
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1 | {
2 | "compilerOptions": {
3 | "allowSyntheticDefaultImports": true,
4 | "lib": ["ES2022", "DOM"],
5 | "module": "ESNext",
6 | "target": "ESNext",
7 | "moduleResolution": "node",
8 | "forceConsistentCasingInFileNames": true,
9 | "strict": true,
10 | "noImplicitAny": true,
11 | "strictNullChecks": true,
12 | "skipLibCheck": true,
13 | "noImplicitOverride": true,
14 | "outDir": "./dist",
15 | "declaration": true,
16 | "declarationMap": true
17 | },
18 | "include": ["scripts"],
19 | "exclude": ["dist"]
20 | }
21 |
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