---
license: apache-2.0
base_model: knowledgator/gliner-relex-base-v1.0
pipeline_tag: token-classification
library_name: openmed
tags:
  - openmed
  - mlx
  - apple-silicon
  - swift
  - ios
  - zero-shot
  - relation-extraction
  - gliner-relex
  - medical
  - clinical
---

# OpenMed GLiNER-Relex Base v1.0 MLX

This is a public [OpenMed](https://github.com/maziyarpanahi/openmed) MLX artifact for zero-shot entity and relation extraction on Apple Silicon. It packages [`knowledgator/gliner-relex-base-v1.0`](https://huggingface.co/knowledgator/gliner-relex-base-v1.0) in the OpenMed MLX format so it can run locally in Python, macOS, and iOS apps through OpenMedKit.

OpenMed is the main developer experience:

- Website: [openmed.life](https://openmed.life)
- GitHub: [github.com/maziyarpanahi/openmed](https://github.com/maziyarpanahi/openmed)
- Python: `pip install "openmed[mlx]"`
- Swift: add the OpenMed GitHub repository as a Swift Package and use `OpenMedKit`

## What This Model Does

`OpenMed/gliner-relex-base-v1.0-mlx` performs experimental zero-shot relation extraction. You provide:

- entity labels, such as `medication`, `condition`, `symptom`, or `procedure`
- relation labels, such as `treats`, `causes`, `associated with`, or `contraindicated for`
- input text

The model returns extracted entities plus directed relation triples between entity pairs.

Artifact metadata:

- OpenMed MLX task: `zero-shot-relation-extraction`
- OpenMed MLX family: `gliner-uni-encoder-token-relex`
- Backbone: DeBERTa-v3 / `deberta-v2` config family
- Max sequence length: `512`
- Weight format: `weights.safetensors`
- Runtime APIs: `GLiNERRelexMLXPipeline` in Python and `OpenMedRelationExtractor` in Swift
- Runtime status: experimental

## Python Quick Start

Install OpenMed with MLX support:

```bash
pip install -U "openmed[mlx]"
```

Run relation extraction from the Hub snapshot:

```python
from huggingface_hub import snapshot_download
from openmed.mlx.inference import GLiNERRelexMLXPipeline

model_path = snapshot_download("OpenMed/gliner-relex-base-v1.0-mlx")
extractor = GLiNERRelexMLXPipeline(model_path)

result = extractor.inference(
    "Aspirin was prescribed for headache after the patient reported migraine symptoms.",
    labels=["medication", "condition", "symptom"],
    relations=["treats", "associated with"],
    threshold=0.5,
    relation_threshold=0.9,
)

print(result["entities"])
print(result["relations"])
```

You can also load a local snapshot directory:

```python
from openmed.mlx.inference import create_mlx_pipeline

extractor = create_mlx_pipeline("/path/to/gliner-relex-base-v1.0-mlx")
```

## Swift And iOS Quick Start

Add OpenMedKit to your Xcode project:

1. In Xcode, choose `File > Add Package Dependencies`.
2. Paste `https://github.com/maziyarpanahi/openmed`.
3. Add the `OpenMedKit` package product to your app target.
4. Download this MLX artifact once, then reuse the cached local model for offline inference.

Example:

```swift
import OpenMedKit

let modelURL = try await OpenMedModelStore.downloadMLXModel(
    repoID: "OpenMed/gliner-relex-base-v1.0-mlx"
)

let extractor = try OpenMedRelationExtractor(modelDirectoryURL: modelURL)

let result = try extractor.extract(
    "Aspirin was prescribed for headache after the patient reported migraine symptoms.",
    entityLabels: ["medication", "condition", "symptom"],
    relationLabels: ["treats", "associated with"],
    threshold: 0.5,
    relationThreshold: 0.9
)

for entity in result.entities {
    print(entity.label, entity.text, entity.score)
}

for relation in result.relations {
    print(relation.head.text, relation.label, relation.tail.text, relation.score)
}
```

Notes for Apple apps:

- MLX inference runs on Apple Silicon and supported Apple devices.
- The iOS Simulator is not the acceptance target for MLX inference; test on a physical iPhone or iPad.
- After the first model download, inference is local and does not require a server.

## Prompt Format

The artifact includes OpenMed prompt-packing metadata so Python and Swift build the same input sequence:

```json
{
  "kind": "gliner-relex",
  "entity_token": "<<ENT>>",
  "relation_token": "<<REL>>",
  "separator_token": "<<SEP>>",
  "class_token_index": 128001,
  "rel_token_index": 128003,
  "embed_marker_token": true,
  "split_mode": "words"
}
```

## Practical Guidance

- Start with a conservative `relation_threshold`, such as `0.9`, for cleaner demos.
- Use concise labels. For example, prefer `medication` over a long natural-language phrase.
- Tune `threshold` and `relation_threshold` separately because entity recall and relation precision behave differently.
- Keep inputs under the artifact's `512` token context window or chunk text before inference.
- Treat this runtime as experimental while OpenMed continues parity testing across Python MLX and Swift MLX.

## Credits

- Base checkpoint: [`knowledgator/gliner-relex-base-v1.0`](https://huggingface.co/knowledgator/gliner-relex-base-v1.0)
- OpenMed GitHub: [github.com/maziyarpanahi/openmed](https://github.com/maziyarpanahi/openmed)
- OpenMed website: [openmed.life](https://openmed.life)
- MLX packaging, Python runtime, and Swift runtime: OpenMed