# GLiNER2 for Swift: Unified Schema-Based Information Extraction on Apple Silicon

**Authors**: Hlib Koshurnykov  
**Published**: 2026-02-19  
**Topics**: artificial-intelligence  
**Type**: Tech Note  
**URL**: https://research.macpaw.com/publications/gliner2-swift

## Introduction

We present **GLiNER2Swift**, a native Swift/MLX implementation of *GLiNER2*, a unified schema-based information extraction framework. GLiNER was originally introduced by Zaratiana et al. as a *generalist and lightweight Named Entity Recognition (NER)* model. GLiNER2 subsequently extended this paradigm toward a broader schema-driven information extraction framework.


Our work brings this unified, schema-based extraction approach to the Apple ecosystem with first-class Apple Silicon support. Designed as a CPU-first, production-ready library for macOS, GLiNER2Swift enables developers and researchers to deploy advanced information extraction pipelines directly in Swift applications without relying on Python runtimes or GPU acceleration.

## Background

The GLiNER framework was introduced as a generalist, lightweight alternative to traditional NER task systems, allowing entity extraction without task-specific retraining. The original GLiNER paper:

> Zaratiana, U., Tomeh, N., Holat, P., & Charnois, T. (2023). *GLiNER: Generalist and Lightweight Model for Named Entity Recognition*. arXiv preprint arXiv:2311.08526.
> [https://arxiv.org/abs/2311.08526](https://arxiv.org/abs/2311.08526)

proposes a model that performs NER using label descriptions rather than fixed classification heads. As stated in the paper:

> "GLiNER is a generalist model for Named Entity Recognition that can generalize to arbitrary entity types defined at inference time without task-specific fine-tuning."
> — Zaratiana et al., 2023

GLiNER2 extends this idea into a broader **schema-based information extraction** framework, supporting not only NER but also classification and structured extraction under a unified architecture. The reference Python implementation is available at:
[https://github.com/fastino-ai/gliner2](https://github.com/fastino-ai/gliner2)

## Motivation

Despite rapid progress in NLP research, most modern frameworks remain Python-centric and GPU-dependent. This creates friction for macOS-native development, especially in contexts where:

- On-device inference is required for privacy or latency
- Deployment targets Apple Silicon hardware
- Swift is the primary programming language
- Tight integration with native macOS applications is needed

GLiNER2Swift addresses this gap by providing a numerically faithful port of GLiNER2 implemented fully in Swift and optimized for macOS 14+ on Apple Silicon (M1/M2/M3).

## Unified Schema-Based Extraction in Swift

GLiNER2Swift preserves the schema-driven paradigm introduced by GLiNER. Instead of training separate models for NER, classification, and structured extraction, developers define extraction tasks dynamically via label schemas.

The library currently supports:

- **Named Entity Recognition (NER)**
- **Text Classification**
- **Structured Data Extraction**

Relation extraction support is currently in progress.

Example usage:

```swift
let model = try await GLiNER2.fromPretrained("macpaw-research/gliner2-base-v1")

let entities = try model.extractEntities(
    from: "Tim Cook is CEO of Apple in Cupertino.",
    labels: ["person", "company", "location"]
)
```

This design enables rapid adaptation to new domains without retraining, aligning with the original GLiNER philosophy of inference-time label flexibility.

## Design Principles

GLiNER2Swift follows several core principles:

1. **Native-first:** pure Swift implementation without Python bridges
2. **On-device by default:** privacy-preserving local inference
3. **Reproducibility:** architectural fidelity to the reference implementation
4. **Developer ergonomics:** Swift Package Manager integration
5. **Extensibility:** foundation for future fine-tuning and adapter support

## Architecture and Implementation

GLiNER2Swift is a direct architectural port of the Python GLiNER2 implementation and aims to achieve numerical parity with it. The model architecture includes the following modules:

- **Encoder:** DeBERTa v3 with disentangled attention
- **Span Marker:** MLP-based span scoring
- **Count LSTM:** entity count prediction
- **Downscaled Transformer:** schema embedding

The implementation leverages:

- **MLX** ([https://github.com/ml-explore/mlx](https://github.com/ml-explore/mlx)) for tensor computation
- macOS-native [Swift concurrency mechanisms](https://developer.apple.com/documentation/swift/concurrency) for asynchronous loading and inference

The system is CPU-first and does not require GPU acceleration.

Currently supported models:

- `fastino/gliner2-base-v1` (205M parameters)
- `macpaw-research/gliner2-base-v1_mlx` (FP-16 instead of FP-32 to reduce model size)

Planned features include training loops, relation extraction, and additional model variants.

## Performance

We ran the benchmarks for our model on Apple M3 Pro, macOS 14+, CPU-only inference, using the `macpaw-research/gliner2-base-v1_mlx` model.

| Task | Mean | Min | Max |
|---|---|---|---|
| Entity Extraction | 334.8 ms | 324.9 ms | 339.6 ms |
| Classification | 59.6 ms | 58.9 ms | 60.1 ms |
| Structured Extraction | 288.3 ms | 284.5 ms | 291.0 ms |
| Combined (all 3 tasks) | 351.9 ms | 344.0 ms | 358.3 ms |

## Applications and Impact

GLiNER2Swift enables:

- Intelligent document parsing
- Flexible, on-device zero-shot NER
- Structured extraction from various document types
- Real-time zero-shot classification pipelines

By bringing GLiNER2 to Swift, we bridge modern NLP research and native Apple platform development. This work demonstrates that transformer-based schema-driven extraction can run efficiently on-device, without GPU dependency or server infrastructure.

GLiNER2Swift represents a step toward privacy-preserving, local-first AI tooling for macOS — bringing unified information extraction directly into Swift applications while staying faithful to the original GLiNER research vision.

## Code

- [GLiNER2Swift on GitHub](https://github.com/MacPaw/Gliner2Swift)