# Fastino releases GLiNER 2.5 Multi for extraction

> A multilingual model built for structured extraction tasks like named-entity recognition, relation extraction, and JSON output.

Published by The Open Weights on Aug 14, 2026. Canonical: https://theopenweights.com/news/gliner-2-5-multi-9k1l

## Key facts

- Company: Fastino
- Model: GLiNER 2.5 Multi
- Version: 2.5-multi-v1
- Category: Text / LLM
- Modalities: Text / LLM
- License: Other (Open weights, commercial use allowed)
- Architecture: BoundaryExtractor
- Precision: FP32
- Weights size: 1.1 GB
- Significance: notable
- Published: 2026-08-14
- Last verified: 2026-09-23
- Hugging Face: https://huggingface.co/fastino/gliner2.5-multi-v1
- Canonical URL: https://theopenweights.com/news/gliner-2-5-multi-9k1l

Fastino has published [GLiNER 2.5 Multi](https://huggingface.co/fastino/gliner2.5-multi-v1) on Hugging Face, a multilingual model aimed squarely at structured information extraction rather than open-ended chat. According to the release, it covers named-entity recognition, relation extraction, text classification, and JSON extraction, positioning it as a general-purpose tool for turning unstructured text into machine-readable output.

The GLiNER approach has become popular precisely because it fills a gap that large chat models handle awkwardly. Instead of prompting a general assistant and parsing its prose, developers can point a compact extraction model at documents and get consistent labeled spans and structured records. That predictability matters for pipelines feeding databases, search indexes, and downstream analytics.

## Why it matters

The multilingual angle is the headline here. Extraction models often skew heavily toward English, so a single model that can identify entities and relations across languages reduces the need to maintain separate systems per locale. For teams working with mixed-language corpora, that consolidation can simplify both deployment and maintenance.

Key capabilities highlighted in the release include:

- Named-entity recognition and relation extraction
- Text classification
- JSON extraction for structured output
- Multilingual coverage

Fastino lists the model under a non-standard license, so teams should review the terms on the [model page](https://huggingface.co/fastino/gliner2.5-multi-v1) before building it into production workflows. Detailed benchmarks and parameter counts were not specified in the release, so practical evaluation on target data remains the sensible next step.

## Get the model

- [Hugging Face](https://huggingface.co/fastino/gliner2.5-multi-v1)

## Sources

- [fastino/gliner2.5-multi-v1](https://huggingface.co/fastino/gliner2.5-multi-v1) — Hugging Face, Aug 14, 2026

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Source: The Open Weights (https://theopenweights.com/). Aggregated and written by Claude, curated by humans. Cite as: "Fastino releases GLiNER 2.5 Multi for extraction", The Open Weights, Aug 14, 2026, https://theopenweights.com/news/gliner-2-5-multi-9k1l