Glossary

These short definitions explain terms that appear on catalog pages. Each entry says what the term means in general, how this catalog uses it, and where to see it on a real record. Where a definition depends on an outside standard or document, the entry links to it. The rules behind the catalog are on the methodology page, and What open weight and open source actually mean explains licensing in more depth. Nothing here is legal advice.

Accelerator (AI chip)

A processor built to speed up particular kinds of computation, working alongside a computer's general-purpose CPU. For AI, accelerators carry out the large matrix calculations used in training and inference; GPUs are one kind, and some companies design chips specifically for neural-network workloads.

In this catalog: accelerators appear as hardware products on the records of the companies that make them, and software records note the hardware their documentation supports under the Supported platforms checklist item (methodology).

Example: Intel lists the Intel Gaudi 3 AI accelerator, which it offers as a PCIe card, an OAM mezzanine card, and a baseboard, with PyTorch support through Intel Gaudi software.

Acceptable-use policy

A document from a model's publisher that lists uses it prohibits. A license can incorporate such a policy by reference, which makes following the policy one of the license's conditions.

In this catalog: use policies are summarized in the license notes of the release they belong to, next to the license that brings them in (methodology).

Example: EmbeddingGemma 300M is under the Gemma Terms of Use, which incorporate the Gemma Prohibited Use Policy and require anyone who redistributes the model to pass those use restrictions on.

Benchmark

A fixed set of tasks with a scoring method, used to compare models under the same conditions. A score that a publisher reports for its own model is a reported result: it has not necessarily been reproduced by anyone else, and it depends on the prompts and settings used.

In this catalog: benchmarks have their own project records. The catalog does not publish benchmark scores, and a model release whose card reports results without code or prompts to re-run them is marked Partial for evaluation materials (methodology).

Example: HumanEval is a set of 164 hand-written Python programming problems, each scored by running unit tests against the generated code.

Checkpoint

A saved copy of a model's state at some point in training. A training checkpoint holds the weights and usually what is needed to resume training, such as the optimizer state, as PyTorch's tutorial on saving and loading models (external site: docs.pytorch.org) describes. Publishers also use the word for any downloadable set of weights, including a quantized version prepared for deployment.

In this catalog: when a publisher releases intermediate checkpoints, the note on the release's Weights checklist item says so (methodology).

Example: For SmolLM3 3B, Hugging Face publishes intermediate checkpoints in a separate repository, one branch per checkpoint, saved every 40,000 steps during pretraining and at later stages such as long-context extension and supervised fine-tuning.

Context window

The maximum number of tokens (small pieces of text or other input) that a model can take into account in one request, usually counting both the prompt and the generated output.

In this catalog: context lengths appear only as the publisher documents them, with a citation.

Example: The model card for Gemma 4 31B lists a 256K-token context window.

Data center

A building or campus that houses servers, storage, and networking equipment, together with the electrical and cooling systems that keep them running. Data centers built for AI hold large clusters of accelerators connected by high-speed networks.

In this catalog: companies that build or operate data centers are organization records with roles such as Compute infrastructure or Cloud provider. Their records describe what the companies offer but give no power-capacity figures (methodology).

Example: Crusoe designs, builds, and operates data centers for AI workloads, with cooling methods that include direct liquid-to-chip cooling, and runs Crusoe Cloud, a GPU cloud platform.

Evaluation harness

Software that runs a model against one or more benchmarks in a consistent way: it formats the prompts, collects the outputs, and computes the scores, so that others can re-run the same evaluation.

In this catalog: evaluation tools are described with a checklist for code, tasks or data, scoring method, reproducibility instructions, and stated limitations, not with the model tiers (methodology).

Example: LM Evaluation Harness, maintained by EleutherAI, runs many benchmark tasks through one interface against local models or hosted model APIs.

Fine-tuning

Continuing to train an existing model on additional data so that it behaves differently, for example to follow instructions or to handle a narrower task. The result is a new set of weights, and the base model's license may still apply to it.

In this catalog: the base model is recorded as provenance. A U.S. fine-tune of a non-U.S. base model does not make the base model American (methodology).

Example: Llama 3.1 Tülu 3.1 8B is Ai2's post-trained version of Meta's Llama 3.1 8B base model, and its weights carry the Llama 3.1 Community License Agreement.

Foundation (open source)

A nonprofit organization that gives open-source projects a neutral home: it can hold their money and trademarks and provide shared services, while each project keeps its own technical governance. A large foundation can also host directed funds, programs with their own name, charter, and governing board whose money the host foundation holds and administers.

In this catalog: a directed fund has its own organization record, labeled Foundation-hosted and linked to the foundation that hosts it. Eligibility rests on the documented governing entity, never on the names or assumed nationalities of contributors (methodology).

Example: The Agentic AI Foundation is a directed fund of The Linux Foundation. Its charter (external site: raw.githubusercontent.com) has a Governing Board approve the projects it supports, leaves each project's governance to that project's own charter, and gives The Linux Foundation control over how the fund's money is held and spent.

Gated download

A download that requires an extra step before the files are released, such as signing in, sharing contact details, or accepting terms. On Hugging Face, a gated repository can grant access automatically once a user agrees, or it can require the publisher to approve each request by hand, as described in Hugging Face's gated models documentation (external site: huggingface.co).

In this catalog: weights that are released only after a request is approved are recorded as Partial, and the release is labeled Restricted weights rather than Open-weight (methodology).

Example: SAM 3.1 checkpoints are gated on Hugging Face, and downloads work only after an access request is accepted.

GGUF

A single-file format that stores a model's weights together with the metadata needed to load them, for inference with the GGML library and tools built on it, such as llama.cpp. Models are usually trained in another framework and then converted to GGUF, often with quantized weights. The format is specified in the GGUF documentation (external site: github.com) in the ggml repository.

In this catalog: a GGUF build is mentioned in the Weights checklist note or run notes of the release it was made from, rather than recorded as a separate release.

Example: For Rnj-1 Instruct, Essential AI publishes an official quantized GGUF build in a separate Hugging Face repository, and the model card documents running it with llama.cpp.

Hosted API

Access to a model that runs on the provider's own servers. You send inputs over the network and receive outputs, but you do not receive the weights.

In this catalog: "Hosted product/API" is a delivery label on organization pages. It is not an openness tier, and it says nothing about the organization's other releases (methodology).

Example: OpenAI lists the OpenAI API, pay-as-you-go access to its hosted models, among its products; its open-weight releases have their own separate records.

Inference

Running a trained model to produce outputs from inputs, as opposed to training it. Inference code is the software that loads the weights and performs that computation.

In this catalog: inference code is its own checklist item for each model release, separate from training code (methodology).

Example: vLLM is an inference and serving engine for large language models.

License scope

The specific materials a license covers. One release can have different licenses for its weights, code, data, and documentation, and a license file in a code repository does not by itself license weights published elsewhere under other terms.

In this catalog: each license entry records what it applies to (weights, code, data, documentation, weights and code, or all), and license filters match a license only to the component it covers (methodology).

Example: For TimesFM 3.0, the weights are under the TimesFM Non-Commercial License v1.0, while the timesfm source-code repository is under the Apache License 2.0.

Maintainer

The organization or group that currently governs a project: it reviews and accepts changes and publishes releases. The maintainer can differ from the organization that first created the project.

In this catalog: a project's eligibility rests on its documented governing or maintaining entity, never on the names or assumed nationalities of contributors (methodology).

Example: PyTorch was originally developed at Meta and has been hosted by the PyTorch Foundation, under the Linux Foundation, since September 2022.

Mixture of experts (MoE)

A model design in which some layers contain many parallel sub-networks, called experts, and a small routing network sends each token to only a few of them. Because only part of the model runs for each token, the total parameter count differs from the number of active parameters. A sparsely gated form of the design was described in Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer (external site: arxiv.org) (Shazeer et al., 2017).

In this catalog: total and active parameter counts appear only as the publisher states them, with a citation.

Example: Trinity Mini, from Arcee AI, has 26B parameters in total and 3B active; its model card lists 128 experts, 8 of them active, plus 1 shared expert.

Model card

A document published with a model that describes what it is, how it was trained and evaluated, what it is intended for, and its known limitations. The format was proposed in the paper Model Cards for Model Reporting (external site: arxiv.org) (Mitchell et al., 2018). On Hugging Face, the model card is the repository's README file, with metadata such as the license, as described in its model cards documentation (external site: huggingface.co).

In this catalog: model cards are a preferred source for technical claims, after the applicable license file (methodology).

Example: Meta publishes the model card for Llama 4 Scout as a file in its llama-models GitHub repository.

Model family

A named line of related models released over time, often in several sizes or generations.

In this catalog: a family record summarizes the line and links to its releases. It never carries a license or a tier, because those can differ from one release to the next (methodology).

Example: The Gemma family record links releases including Gemma 4 31B, which is under the Apache License 2.0, and EmbeddingGemma 300M, which is under the Gemma Terms of Use.

Model release

One specific published model, identified by its name, size, and version, with its own files and its own terms.

In this catalog: licenses, availability, checklists, and tiers attach only to releases, and a release's evidence is never inherited from another version (methodology).

Example: Olmo 3.1 32B Think is one release in Ai2's Olmo family, with its own licenses and checklist.

Nonprofit

An organization that is not run to make a profit for owners or shareholders. In the United States, the IRS recognizes many nonprofits as tax-exempt: section 501(c)(3) (external site: irs.gov) covers organizations run for charitable, scientific, educational, and similar purposes, and section 501(c)(6) (external site: irs.gov) covers business leagues, such as trade associations, that promote a shared business interest. In both cases, net earnings may not benefit any private shareholder or individual.

In this catalog: U.S. nonprofit or lab is one of the four eligibility bases. Nonprofit status is documented from the organization's own pages and, where available, the IRS listing of tax-exempt organizations (methodology).

Example: EleutherAI appears in the IRS exempt-organization data for the District of Columbia as EleutherAI Institute, a 501(c)(3) organization with a ruling date of November 2023.

Open source (software)

Software distributed with its source code under a license that meets the Open Source Initiative's Open Source Definition (external site: opensource.org) (version 1.9). Its criteria include free redistribution, access to the source code, permission to make derived works, and no discrimination against persons, groups, or fields of endeavor. Code that is merely readable on a public website is not open source on that basis alone.

In this catalog: each project lists its license with an SPDX identifier where one exists and a link to the license file; an identifier alone never replaces reading the terms (methodology).

Example: llama.cpp is published under the MIT License.

Open-source AI (OSI definition)

An AI system made available under terms that grant the freedoms to use, study, modify, and share it, as set out in the Open Source Initiative's Open Source AI Definition (external site: opensource.org), version 1.0. The definition requires detailed information about the training data, the complete code used to train and run the system, and the parameters, each under OSI-approved terms. It does not require every piece of training data to be downloadable.

In this catalog: USASI does not certify releases against this definition. Its highest model tier, Open system (reviewed), is an editorial label that requires a sourced review specific to the release (methodology).

Example: Olmo 3 7B publishes its weights, training scripts, and training-data mixes. The catalog labels it Open-stack, which describes published materials and is not a finding that it meets the OSI definition.

Open-stack

A USASI tier for a model release that is Open-weight and also has published inference code, training code, and a training recipe, plus at least a documented composition of its training data. It is an editorial category of rubric v0.2, not an outside standard, and it does not require the training data itself to be downloadable.

In this catalog: the tier is computed from the release's checklist. Tiers are cumulative, so every Open-stack release also counts as Open-weight; the higher tier, Open system (reviewed), also requires complete training-data information, a complete training pipeline, and a sourced review of reuse rights (methodology).

Example: Olmo 3 7B is labeled Open-stack: Ai2 publishes its weights, the OLMo-core scripts for its pretraining, mid-training, and long-context stages, a technical report with its training configuration, and links to the data mixes used at each stage.

Open weight

A model whose trained parameters can be downloaded by the public. The term describes availability: the license that comes with the weights may still limit commercial use, redistribution, or particular uses. Others use the term with stricter requirements; the OSI's explainer on open weights (external site: opensource.org), for example, describes final parameters shared under an OSI-approved license, without the training code or data.

In this catalog: the Open-weight tier requires only publicly obtainable weights. The license is recorded separately and shown next to the label, with a caveat when it is not on the rubric's list of OSI-approved licenses (methodology).

Example: gpt-oss-20b is labeled Open-weight; its weights download without a gate under the Apache License 2.0.

Parent company and documented control

A parent company controls another organization, its subsidiary, either directly or through other subsidiaries. In the SEC's definitions in Rule 405 (external site: ecfr.gov), control means the power to direct an entity's management and policies, whether through owning voting securities, by contract, or otherwise, so it can rest on more than share ownership.

In this catalog: documented U.S. control is one of the four eligibility bases. It applies when filings or official pages show that a U.S. entity primarily controls an organization, for example a research unit wholly owned by a U.S.-headquartered company, and each such case gets a written assessment (methodology).

Example: The Google DeepMind record names no headquarters, because the official pages reviewed do not state one. It is assessed under U.S. control because Google and Alphabet document it as part of Google, and Alphabet's principal executive offices are in Mountain View, California.

Power and energy

Power is the rate at which electricity is supplied or used at a given moment, measured in watts (W): a kilowatt (kW) is 1,000 watts, a megawatt (MW) is 1,000 kW, and a gigawatt (GW) is 1,000 MW. Energy is power used over time, measured in watthours (Wh): a kilowatthour (kWh) is one kilowatt for one hour, and a megawatthour (MWh) is one megawatt for one hour. A figure in megawatts or gigawatts therefore describes capacity at a moment, not an amount of electricity used. The U.S. Energy Information Administration explains these units in Measuring electricity (external site: eia.gov).

In this catalog: power-capacity figures are not published, even when an organization's own announcements state them (methodology).

Example: Thinking Machines Lab records a March 2026 partnership with NVIDIA to deploy NVIDIA systems for its model training. The announcement, listed among the record's sources, calls the partnership gigawatt-scale in its title, which describes power capacity; the record's own text gives no capacity figure.

Quantization

Storing a model's weights, and sometimes its intermediate values, at lower numerical precision (for example 4-bit integers instead of 16-bit numbers) to reduce memory use and speed up inference, sometimes at some cost in output quality.

In this catalog: memory or hardware figures are recorded only when a publisher states them, together with the precision they assume.

Example: AWQ (Activation-aware Weight Quantization), from MIT HAN Lab, is a method and codebase for 4-bit and 3-bit weight-only quantization of language models.

Restricted weights

A USASI tier for a model release whose weights can be obtained only by request, with approval, or by some users, for example through a gated download that the publisher reviews. Such a release is not counted as Open-weight, however much else about it is published.

In this catalog: the tier follows from the Weights checklist item: weights recorded as Partial give Restricted weights, and the license is recorded separately (methodology).

Example: For DINOv3 ViT-7B/16, Meta publishes training code and stage-by-stage configuration files, but downloading the weights from Hugging Face requires an approved access request, so the release is labeled Restricted weights.

Reviewed date

The date an editor last checked a piece of evidence against its sources. It is different from the date a record was edited, the date an artifact was released, and the date the site was built.

In this catalog: each record shows a "Last reviewed" date, and individual facts, such as a license or an availability assessment, can carry their own reviewed date. An automated fetch is not a review (methodology).

Example: On gpt-oss-20b, the license entries carry their own reviewed dates, separate from the record's "Last reviewed" date.

Self-hosting / local inference

Running a model on hardware you control, such as a laptop, a workstation, or your own servers, instead of calling a provider's hosted service. It requires access to the weights and to software that can run them, and the weights' license still applies.

In this catalog: run notes on a release describe only documented ways to run it, and any memory figure is attributed to its source.

Example: Ollama is an MIT-licensed application for downloading and running open models on macOS, Windows, and Linux; the company behind it also offers cloud-hosted models.

Subsidiary

A company that is owned or controlled by another company, its parent. A research unit is a similar relationship within a single company.

In this catalog: a subsidiary or unit with its own record is also counted under "units and subsidiaries," so a parent and its subsidiary are never presented as two independent companies. Parent–subsidiary cases get a written eligibility assessment (methodology).

Example: Red Hat is documented in its record as a wholly owned subsidiary of IBM, with its own headquarters in Raleigh, North Carolina.

Tokenizer

The component that splits input text into tokens, such as words, parts of words, or single characters, and converts them into the numeric IDs a model works with. A model is normally run with the same tokenizer it was trained with. Hugging Face's guide to tokenization algorithms (external site: huggingface.co) describes the common subword methods.

In this catalog: a tokenizer that is distributed separately from a model's weights is noted in the release's run notes or provenance.

Example: OpenELM 3B Instruct is published without tokenizer files. Apple's example script loads Meta's Llama 2 tokenizer from a separate Hugging Face repository, which is gated, and the script requires a Hugging Face access token.

Unknown

A status meaning that an item has not been assessed yet, or that the available evidence is not enough to decide. It never stands in for "no."

In this catalog: "Not public" is used instead when the evidence shows that something is unavailable (methodology).

Example: On Gemma 4 31B, training code is Unknown: the record notes fine-tuning code in Google DeepMind's gemma library, but no published code used to pre-train Gemma 4.

Weights

The numerical parameters a model learns during training. Together with the model's architecture and inference code they make up the model, a breakdown the Open Source AI Definition (external site: opensource.org) also uses. Weights are usually distributed as large files, sometimes with checkpoints saved at intermediate points in training.

In this catalog: weights are the first checklist item for a model release, and their availability decides whether a release can be labeled Open-weight (methodology).

Example: Pythia 12B publishes its final weights and 154 intermediate checkpoints as branches of its Hugging Face repository.