> ## Documentation Index
> Fetch the complete documentation index at: https://docs.datafog.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# 0.4.5 beta.4

> Caller-selected GLiNER2 models, verified model manifests, and custom entity labels.

This prerelease builds on `0.4.5-beta.3`. Install `0.4.5-beta.4` for Cargo and
npm, or `0.4.5b4` for Python. Node and WASM remain on npm's `beta` tag;
`latest` remains `0.4.4`.

## PII and general-purpose GLiNER2

Use the existing pinned PII checkpoint or select a compatible general-purpose
GLiNER2 span model through a local model manifest. The release checks include
`fastino/gliner2-privacy-filter-PII-multi` and `fastino/gliner2-base-v1`.

The provisioner can generate a versioned manifest and file hashes from explicitly
downloaded local assets using `--model-id` and `--revision`, or verify an existing
manifest using `--manifest`. Core verifies the selected manifest and listed
assets before starting inference. Results report the selected model identity;
its revision is included in adapter evidence. Existing legacy PII bundles remain
supported with their original compiled asset pins.

## Custom entity schemas

GLiNER2 labels are caller-defined, with per-label thresholds and optional
descriptions. For example, `current_employer` produces the typed entity
`CURRENT_EMPLOYER`. The existing `phone_number` to `PHONE` mapping is preserved.
Empty, padded, duplicate labels and labels mapping to the same output type are
rejected. Schemas remain fixed for each session.

Custom findings retain byte and codepoint offsets, plus UTF-16 offsets in Node,
and work with the existing transformations. `scan` still routes rule-covered
entity types to rules and other selected labels to the model. Use `scan_model`
or Node's `scanModel` for raw model output.

Downloading and provisioning remain explicit. Inference uses local files through
the existing CPU worker and pinned runtime dependencies. This release supports
compatible GLiNER2 span models; it does not add classification, relation
extraction, GLiNER2.5, GLiNER1, or spaCy adapters. WASM remains rule-based.

See the [GLiNER2 setup guide](https://github.com/DataFog/datafog-core/blob/release/0.4.5-beta.4/docs/gliner2-model.md)
and [manifest contract](https://github.com/DataFog/datafog-core/blob/release/0.4.5-beta.4/docs/model-manifests.md).

## Release validation

Publication requires DataFog, PII GLiNER2, and general-purpose GLiNER2 to pass
packaged Rust and installed Python/Node checks on Linux x64, Linux ARM64, macOS
ARM64, and Windows for the release commit. Intel macOS model checks are advisory.
These checks validate loading, response contracts, routing, offsets and lifecycle
behavior; they do not make model-accuracy claims.


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