Open AI platform
How to use
Hugging Face.
Discovering and evaluating open models and datasets, running hosted inference, collaborating through repositories, and publishing ML demos with Spaces.
Hugging Face is a open AI model, dataset, application, and inference platform. Discovering and evaluating open models and datasets, running hosted inference, collaborating through repositories, and publishing ML demos with Spaces. This guide covers the whole path in one place: official access, a first session that produces something reviewable, the checks that make output trustworthy, and the permissions worth limiting before you connect real work.
AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.
Access & setup
Find, install, and sign in to Hugging Face
Get into the official Hugging Face experience with the right account and a setup you understand.
- 01
Start at https://huggingface.co/ and confirm the domain before entering account or payment information.
- 02
Availability: Web Hub and Playground with Python, JavaScript, CLI, Git, and API tooling for local and hosted workflows.
- 03
A Hugging Face account for publishing and tokens for authenticated API use. Models, datasets, providers, Spaces, hardware, and licenses have separate requirements.
- 04
Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.
- 05
Run one low-risk test task before connecting sensitive files, repositories, or workspace data.
- Use official download pages.
- Review permissions during setup.
- Keep installers and applications updated.
First session
Your first useful Hugging Face session
Learn the interaction loop using a small task with a clear outcome.
- 01
Define the task and search the Hub for a model with a relevant card, license, modality, and active ecosystem.
- 02
Test it with non-sensitive representative inputs in the widget or Inference Playground.
- 03
Create a fine-grained token and reproduce the test through an official client or compatible API.
- 04
Pin the model revision and provider, then document license, cost, latency, limitations, and safety checks.
- State the outcome before the background.
- Provide the real source material.
- Review the result before expanding the task.
Quality control
Check the quality of Hugging Face output
Establish that a model's output is reliable enough for the job you are handing it.
- 01
Restate the task, the acceptance criteria, and the failure cost before evaluating any response.
- 02
Test with a fixed evaluation set rather than ad-hoc prompts, so changes are measurable across model versions.
- 03
Check determinism and drift: record model version, parameters, and date, because providers update models underneath you.
- 04
Probe the failure modes deliberately — ambiguous inputs, adversarial phrasing, and out-of-scope requests.
- 05
Validate structure as well as content: confirm schemas, types, and required fields survive real inputs.
- Pin and record the model version with every result.
- Evaluate against a fixed set, not one-off prompts.
- Validate output structure before it reaches downstream code.
Privacy & permissions
Use Hugging Face safely
Understand what leaves your system on every call, and what the provider keeps.
- 01
Classify the data in each request before it is sent to a hosted model.
- 02
Check the provider's retention and training terms for your specific plan, not the marketing page.
- 03
Store API keys in a secret manager, scope them per project, and rotate them on a schedule.
- 04
Strip or tokenise personal and regulated data before it reaches the request body.
- 05
Set rate limits, spend caps, and monitoring so a runaway loop is bounded and visible.
- Hub content comes from many publishers with different licenses and security postures. Review repositories and model cards, avoid unsafe remote code, use fine-grained tokens, and control Space visibility and costs.
- Follow your organisation's approved-use policy.
- Never treat fluent output as authorization to act.
Core workflows
Step-by-step ways to use Hugging Face for the work it does best.
Each workflow below has its own outcome, steps and review checkpoints.
Move from model discovery to a reproducible evaluation that includes licensing and deployment fit.
↓ 02 WorkflowPublish a safe Hugging Face SpaceTurn a model workflow into a reviewable demo with deliberate visibility, secrets, and hardware settings.
↓Workflow 01
Evaluate an open model on the Hugging Face Hub
Move from model discovery to a reproducible evaluation that includes licensing and deployment fit.
Outcome: Choose an open model based on evidence for the intended task and operating environment.
- 01
Define the task, languages, modalities, hardware, latency, context, safety, and license requirements.
- 02
Filter Hub results and inspect model cards, files, license, training disclosures, limitations, and recent activity.
- 03
Test candidates in widgets or the Inference Playground using the same representative and adversarial examples.
- 04
Record output quality, provider, configuration, latency, cost, and failures, then reproduce the test in code.
- 05
Pin the model revision and document evaluation results, license obligations, and monitoring before integration.
What good use looks like
- Read the full model card.
- Pin revisions for reproducibility.
- Evaluate your task, not a leaderboard alone.
Workflow 02
Publish a safe Hugging Face Space
Turn a model workflow into a reviewable demo with deliberate visibility, secrets, and hardware settings.
Outcome: Deploy a useful ML demonstration without exposing code, credentials, data, or uncontrolled costs.
- 01
Define the demo audience, inputs, outputs, model license, abuse cases, resource limits, and data-handling notice.
- 02
Choose Gradio, Docker, or static HTML and select public, protected, or private visibility deliberately.
- 03
Build locally with pinned dependencies, input validation, clear errors, and representative example data.
- 04
Store credentials as Space secrets, never variables, and test cold starts, concurrency, timeouts, and large inputs.
- 05
Review public source exposure, logs, analytics, hardware cost, moderation, and ownership before sharing the URL.
What good use looks like
- Assume public Space code is visible.
- Keep secrets out of commits.
- Set cost and abuse guardrails.
Official references
Check the current product documentation.
Features, plan limits, availability, and data controls change. These official pages are the starting points used for this guide.