Eval directory

Evals for Hugging Face

Eval coverage for Hugging Face, mapped from its public product surface.

About Hugging Face

Hugging Face is a collaboration platform where the machine learning community hosts and shares models, datasets, and applications, spanning text, image, video, audio, and 3D modalities. It also publishes an open-source stack of core ML libraries — including Transformers, Diffusers, Datasets, PEFT, and TRL — for training, fine-tuning, and inference. Paid Compute and Enterprise tiers add dedicated inference infrastructure plus enterprise-grade security features such as SSO, audit logs, and resource groups.

Industry

machine learning model & dataset collaboration platform

Website

huggingface.co

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Coverage map

What would you measure for Hugging Face?

6 scoring areas · 23 capabilities mapped · grounded in 8 cited pages

Every eval set is graded on

Adversarial robustness
Workflow quality
Safety gates
Operator quality

Pass/Fail + LLM judge 1–5 · critical severity flags · negative controls

Hub Discovery & Repositories

Finding and reasoning about the Git-based repositories the Hub hosts — 2M+ models, 500k+ datasets, and 1M+ applications — across text, image, video, audio, and 3D modalities.

“The platform where the machine learning community collaborates on models, datasets, and applications.” huggingface.co

Mapped capabilities

4 capabilities

Model search and trending navigation
Directing users to browse/filter the model catalog and interpret listing signals such as recent updates, download counts, and likes.
Dataset discovery and viewer
Locating datasets on the Hub and using the Dataset viewer API for metadata, statistics, and content inspection.
Modality-aware routing
Mapping a stated task to the right modality and Tasks pages spanning text, image, video, audio, and 3D.
Repo hosting semantics
Explaining that models, datasets, and Spaces are Git-based repos, and that unlimited public hosting is available.

Spaces & Applications

Publishing and running hosted ML applications — the demo layer where the community ships interactive apps such as image, video, and editing pipelines.

Mapped capabilities

3 capabilities

Space creation and hosting
Guiding a user from a working model or pipeline to a hosted, shareable application on the Hub.
App discovery
Browsing the 1M+ application catalog and interpreting trending Spaces.
Portfolio and profile surfacing
Using shared work — models, datasets, Spaces — to build a public ML profile.

Inference & Deployment

Getting a model to serve traffic, from partner-routed Inference Providers to dedicated managed Endpoints and self-hosted serving toolkits, including major cloud paths.

“Call 200k+ models hosted by our 10+ Inference partners” huggingface.co

Mapped capabilities

4 capabilities

Inference Providers routing
Calling models hosted across the 10+ inference partner network covering 200k+ models.
Dedicated Inference Endpoints
Deploying on dedicated, fully managed HF infrastructure as part of the paid Compute offering.
TGI and TEI serving toolkits
Choosing between Text Generation Inference for language models and Text Embeddings Inference for embedding models.
Cloud deployment paths
Training and deploying HF models on AWS (including DLCs and Trainium/Inferentia), Microsoft Azure, and Google Cloud.

Illustrative example

Input: I need to self-host an embedding model for semantic search and also want a fully managed option. What should I use?
Expected behavior: Names Text Embeddings Inference (TEI) as the optimized toolkit for serving embedding models, and Inference Endpoints as the dedicated fully managed option on HF infrastructure. Does not present Text Generation Inference (TGI) as the embeddings server.

Core ML Libraries

The open-source stack developers use to load, run, and process models and data — the surface where API-level correctness and library selection matter most.

“Host and collaborate on unlimited public models, datasets and applications.” huggingface.co

Mapped capabilities

4 capabilities

Transformers usage
Loading and running state-of-the-art PyTorch models for training and inference.
Diffusers pipelines
Using DiffusionPipeline for video, image, and audio generation, mixing components, loading LoRA adapters, and applying offloading, quantization, or torch.compile.
Datasets loading and processing
One-line dataset loading, streaming, and Arrow-backed zero-copy processing for audio, vision, and NLP tasks.
Specialized libraries
Selecting among Tokenizers, timm, Sentence Transformers, Transformers.js, and Evaluate for the stated task.

Training, Fine-Tuning & Optimization

Adapting pretrained models efficiently — parameter-efficient methods, preference and RL-based post-training, distributed training, and inference/training optimization.

“PEFT methods only fine-tune a small number of (extra) model parameters - significantly decreasing computational and storage costs” huggingface.co

Mapped capabilities

4 capabilities

PEFT adapter methods
Fine-tuning a small number of extra parameters instead of full fine-tuning, including integration with Transformers, Diffusers, and Accelerate.
TRL post-training methods
Selecting among SFT, GRPO, DPO, and reward modeling, including GRPOTrainer multi-environment agentic RL with per-example environment selection and environment-owned rewards.
Accelerate distributed training
Running PyTorch training across multi-GPU, TPU, and mixed-precision configurations.
Optimum optimization
Optimizing Transformers for faster training and inference, including accelerator-specific backends.

Illustrative example

Input: I have a dataset of prompt responses labeled chosen versus rejected. Which Hugging Face library and training method should I use to align my model on these preferences?
Expected behavior: Recommends TRL and identifies Direct Preference Optimization (DPO) as the method that trains directly on chosen/rejected pairs, noting TRL also offers SFT, GRPO, and reward modeling. Does not attribute preference training to PEFT or Accelerate.

Team & Enterprise Controls

The paid organizational tier starting at $20/user/month, covering identity, auditability, data residency, and scoped access for teams building AI together.

“Give your team the most advanced platform to build AI with enterprise-grade security, access controls and dedicated support.” huggingface.co

Mapped capabilities

4 capabilities

Single Sign-On
Enterprise identity integration for organization members.
Audit logs
Reviewing organizational activity records for security and compliance needs.
Resource groups and access controls
Scoping which members can reach which repositories and resources.
Regions and private data handling
Region selection plus the Private Datasets Viewer for inspecting non-public data.

Coverage is mapped from Hugging Face's public pages (8 crawled). Examples are illustrative, not real test cases. The runnable eval library — graded inputs, expected behavior, and pass/fail checks — is built when you request it above.

Frequently asked questions

What do the Corsac evals for Hugging Face test?+

The coverage map is generated from Hugging Face's own public product surface (machine learning model & dataset collaboration platform): 6 scoring areas — Hub Discovery & Repositories, Spaces & Applications, and Inference & Deployment, and more — spanning 23 mapped capabilities, each graded on adversarial robustness, workflow quality, safety gates, and operator quality once the library is built.

How are the Hugging Face evals scored?+

Every case generated for Hugging Face — across Hub Discovery & Repositories and Spaces & Applications and the other mapped areas — is graded with pass/fail checks plus an LLM judge scoring 1–5 against its expected behavior, with critical-severity flags and negative controls. Only judge-passed evals are published.

How many test cases does the Hugging Face library include?+

The full Hugging Face library is built on request. The coverage map spans 6 areas and 23 capabilities (for example, Model search and trending navigation and Dataset discovery and viewer under Hub Discovery & Repositories); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

How do I run these evals against Hugging Face or my own agent?+

Request the library with your work email above. We'll build out all 6 mapped Hugging Face areas and set them up in a Corsac workspace, where you can run every test case against Hugging Face or your own agent with your own data.