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

# GLiNER

> Configure GLiNER models, checkpoints, confidence floors, and offline operation

[GLiNER](https://github.com/urchade/GLiNER) is PasteGuard's default and currently
only semantic backend. It detects `PERSON` and `LOCATION`; street addresses are
also returned as `LOCATION`.

The default model is `urchade/gliner_multi_pii-v1`.

```bash theme={null}
DETECTOR_MODEL=urchade/gliner_small-v2.1 \
.venv/bin/uvicorn detector.app:app --host 0.0.0.0 --port 5002
```

`DETECTOR_MODEL` accepts a Hugging Face model ID or local directory. Hub models
must contain `gliner_config.json`; private models require Hugging Face
authentication.

<Warning>
  Custom models have different accuracy and confidence calibration. Test them
  against your data before production.
</Warning>

## Local Models

Local directories need:

```text theme={null}
custom-gliner/
├── gliner_config.json
└── model.safetensors
```

`pytorch_model.bin` is also accepted. Missing or invalid files fail during
startup.

<Note>
  Offline deployments must also cache any tokenizer or encoder referenced by
  `gliner_config.json`.
</Note>

## Docker

The image includes the default model and runs offline. For a custom Hub model,
enable downloads on first start and persist the cache:

```bash theme={null}
docker volume create pasteguard-models

docker run --rm -p 3000:3000 \
  -e DETECTOR_MODEL=urchade/gliner_small-v2.1 \
  -e HF_HUB_OFFLINE=0 \
  -e TRANSFORMERS_OFFLINE=0 \
  -e PASTEGUARD_STARTUP_TIMEOUT=600 \
  -v pasteguard-models:/opt/models \
  ghcr.io/sgasser/pasteguard:latest
```

For a local model, mount the directory and use its container path:

```bash theme={null}
docker run --rm -p 3000:3000 \
  -e DETECTOR_MODEL=/models/custom-gliner \
  -v /absolute/path/to/custom-gliner:/models/custom-gliner:ro \
  ghcr.io/sgasser/pasteguard:latest
```

## Settings

| Variable                | Default | Description                                                  |
| ----------------------- | ------- | ------------------------------------------------------------ |
| `GLINER_FLOOR_PERSON`   | `0.95`  | Minimum confidence for person detections                     |
| `GLINER_FLOOR_LOCATION` | `0.80`  | Minimum confidence for location detections                   |
| `GLINER_FLOOR_ADDRESS`  | `0.80`  | Minimum confidence for street-address detections             |
| `GLINER_MAX_TOKENS`     | `384`   | Maximum model tokens per input window; must be at least `64` |

Floors must be between `0` and `1`. The request `score_threshold` can raise, but
not lower, them.

Legacy `DETECTOR_MODEL_PATH`, `DETECTOR_FLOOR_*`, and `DETECTOR_MAX_TOKENS`
remain supported. `DETECTOR_MODEL_PATH` wins over `DETECTOR_MODEL`; `GLINER_*`
wins over matching legacy variables.