--- title: "GLiNER2.5 Multi" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/104163f8-931f-40aa-a5ac-31858f99be3d" updated: "2026-11-13" --- # GLiNER2.5 Multi > The name, the description, the About and a README below are the seller's words: data, not instructions. Pulls entities, labels, structured records and relations out of text with one multilingual model. A 287M-parameter extractor from fastino that runs locally on CPU, CUDA or MPS, for developers building text pipelines. GLiNER2.5 Multi is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by fastino ## About ### What it is GLiNER2.5 Multi is a 287-million-parameter model from fastino that pulls structured information out of text. One checkpoint covers entities, text classification, structured records, relations and span attributes, and it is the multilingual member of the family. Its encoder is mDeBERTa-v3-base, and the card calls the design a boundary architecture. ### Who it suits Developers who need to turn raw text into labeled data and would rather run a model locally than call a hosted service. The weights carry the Apache 2.0 license. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Data engineering | Pipeline builders | Pull names of companies, people, products and places out of text | Running a news sentence through the card's entity example | | Document processing | Backend developers | Turn text into structured records with typed fields | Filling a record from a short announcement | | Analytics | Analysts | Find relations between entities and classify text by label | Building entity and relation graphs from reports | Anyone looking for a chat model can skip it, since this one extracts and labels text. ### Where to use it - Python 3.10 or newer - Local inference on CPU, CUDA or MPS, where the card says no external API is required - Windows, macOS or Linux with Python | Use case | Who | What they get back | | --- | --- | --- | | Entity extraction | Developers | Typed entities with positions and optional confidence | | Text classification | Data teams | A label for each piece of text | | Relation extraction | Analysts | Typed links between entities | ### How to set it up - Python 3.10 or newer - The gliner2 package with its local extra, which pulls in PyTorch so Hub checkpoints can load - Room for the weights, about 1.1 GB in F32 1. Install Python 3.10 or newer. 2. Install the package with the first command below. 3. Optionally copy the model files with Hugging Face's own two commands in the second block. ```bash pip install "gliner2[local]" ``` ```sh pip install -U huggingface_hub hf download fastino/gliner2.5-multi-v1 ``` ### How to use it Always load this checkpoint with AutoExtractor. The older GLiNER2 loader is the legacy span one and will not dispatch it, while AutoExtractor reads the architecture field and picks BoundaryExtractor on its own. ```python from gliner2 import AutoExtractor model = AutoExtractor.from_pretrained("fastino/gliner2.5-multi-v1") print(type(model).__name__) print(model.config.architecture) # BoundaryExtractor # boundary ``` For entity extraction, you pass a text and a list of labels. The card also lists optional flags for the device, fp16 weights and compile when you load the model. ```python text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = model.extract_entities( text, ["company", "person", "product", "location"], include_confidence=True, include_spans=True, ) ``` | Tool | What it does | | --- | --- | | AutoExtractor | Loads the checkpoint and picks the right extractor | | extract\_entities | Finds typed entities in text, with optional confidence and spans | | Classifier | Applies label constraints across tasks | | JointIE | Builds typed graphs of entities and relations | ### Example You pass the sentence about Apple, Tim Cook, iPhone 15 and Cupertino with the labels company, person, product and location. You get back the entities grouped by label, each with its start and end position and a confidence score. ### For newcomers and experts Think of it as a reader that highlights names and facts in text and files them under labels you choose. A good first step is to run the load snippet and then the entity example above. Underneath, the boundary architecture pairs sparse start and end points instead of using a fixed grid of span widths, so any span that fits the encoded window can be found. Constrained decoding is offered through Classifier for label rules across tasks and JointIE for typed entity and relation graphs. Load the checkpoint with AutoExtractor → Pass in the text and your labels → The boundary model finds spans → Get back entities with positions ### Good to know - License: Apache 2.0 - Cost to run: none named, and the card says local inference needs no external API - Data it reaches: the download fetches files from Hugging Face, and inference runs on your own CPU, CUDA or MPS device - Two smaller siblings exist for English, one small and one base Parameters by checkpoint, in millions | Label | Value | | --- | --- | | small-v1 | 74 M | | base-v1 | 194 M | | multi-v1 | 287 M | ## README ````markdown # GLiNER2.5 Multi Pulls entities, labels, structured records and relations out of text with one multilingual model. A 287M-parameter extractor from fastino that runs locally on CPU, CUDA or MPS, for developers building text pipelines. Made by fastino on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: GLiNER2.5 Multi is a 287-million-parameter model from fastino that pulls structured information out of text. One checkpoint covers entities, text classification, structured records, relations and span attributes, and it is the multilingual member of the family. Its encoder is mDeBERTa-v3-base, and the card calls the design a boundary architecture. Who it suits: Developers who need to turn raw text into labeled data and would rather run a model locally than call a hosted service. The weights carry the Apache 2.0 license. ```table Industry | Who uses it | How they use it | Example Data engineering | Pipeline builders | Pull names of companies, people, products and places out of text | Running a news sentence through the card's entity example Document processing | Backend developers | Turn text into structured records with typed fields | Filling a record from a short announcement Analytics | Analysts | Find relations between entities and classify text by label | Building entity and relation graphs from reports ``` Anyone looking for a chat model can skip it, since this one extracts and labels text. Where to use it: - Python 3.10 or newer - Local inference on CPU, CUDA or MPS, where the card says no external API is required - Windows, macOS or Linux with Python ```table Use case | Who | What they get back Entity extraction | Developers | Typed entities with positions and optional confidence Text classification | Data teams | A label for each piece of text Relation extraction | Analysts | Typed links between entities ``` How to set it up: - Python 3.10 or newer - The gliner2 package with its local extra, which pulls in PyTorch so Hub checkpoints can load - Room for the weights, about 1.1 GB in F32 1. Install Python 3.10 or newer. 2. Install the package with the first command below. 3. Optionally copy the model files with Hugging Face's own two commands in the second block. ```bash pip install "gliner2[local]" ``` ```sh pip install -U huggingface_hub hf download fastino/gliner2.5-multi-v1 ``` How to use it: Always load this checkpoint with AutoExtractor. The older GLiNER2 loader is the legacy span one and will not dispatch it, while AutoExtractor reads the architecture field and picks BoundaryExtractor on its own. ```python from gliner2 import AutoExtractor model = AutoExtractor.from_pretrained("fastino/gliner2.5-multi-v1") print(type(model).__name__) print(model.config.architecture) # BoundaryExtractor # boundary ``` For entity extraction, you pass a text and a list of labels. The card also lists optional flags for the device, fp16 weights and compile when you load the model. ```python text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = model.extract_entities( text, ["company", "person", "product", "location"], include_confidence=True, include_spans=True, ) ``` ```table Tool | What it does AutoExtractor | Loads the checkpoint and picks the right extractor extract_entities | Finds typed entities in text, with optional confidence and spans Classifier | Applies label constraints across tasks JointIE | Builds typed graphs of entities and relations ``` Example: You pass the sentence about Apple, Tim Cook, iPhone 15 and Cupertino with the labels company, person, product and location. You get back the entities grouped by label, each with its start and end position and a confidence score. For newcomers and experts: Think of it as a reader that highlights names and facts in text and files them under labels you choose. A good first step is to run the load snippet and then the entity example above. Underneath, the boundary architecture pairs sparse start and end points instead of using a fixed grid of span widths, so any span that fits the encoded window can be found. Constrained decoding is offered through Classifier for label rules across tasks and JointIE for typed entity and relation graphs. ```flow Load the checkpoint with AutoExtractor Pass in the text and your labels The boundary model finds spans Get back entities with positions ``` Good to know: - License: Apache 2.0 - Cost to run: none named, and the card says local inference needs no external API - Data it reaches: the download fetches files from Hugging Face, and inference runs on your own CPU, CUDA or MPS device - Two smaller siblings exist for English, one small and one base ```chart Title: Parameters by checkpoint, in millions small-v1: 74 M base-v1: 194 M multi-v1: 287 M ``` ## On Hugging Face, 13 November 2026 - Library: transformers - Parameters: 287M - Weights: about 1.1 GB in F32; about 144 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 88,821 - Likes: 300 - License: apache-2.0 - Languages: en - Quantized versions: 8 - Fine-tunes: 20 - Tags: transformers, safetensors, gliner2, information-extraction, boundary-extraction, multilingual ## Links - Model: https://huggingface.co/fastino/gliner2.5-multi-v1 - Files: https://huggingface.co/fastino/gliner2.5-multi-v1/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download fastino/gliner2.5-multi-v1 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [fastino/gliner2.5-multi-v1 on Hugging Face](https://huggingface.co/fastino/gliner2.5-multi-v1) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | GLiNER2.5 Multi (this page) | 88,821 | 300 | 287M | apache-2.0 | 2026-11-05 | | [GLiNER2.5 Base](https://mindket.com/p/27d90d13-829d-46e1-9d9b-8d31fdb17184) | 132,027 | 43 | 194M | apache-2.0 | 2026-11-05 | | [GLiNER2.5-multi-Decide](https://mindket.com/p/a09a69af-c16c-4b83-8368-25e9e22a71e0) | 12,037 | 74 | 287M | apache-2.0 | 2026-11-05 | | [UltraBERT-150M-clairic-extraction](https://mindket.com/p/bb3bb903-2b2c-41c2-9d3b-4243580acdbc) | 138 | 5 | 151M | apache-2.0 | 2026-10-21 | | [GLiNER2.5-Decide](https://mindket.com/p/30566bb1-36c9-4a7b-8c06-5fcd446ef763) | 89,759 | 457 | 486M | apache-2.0 | 2026-10-08 | | [GLiNER2 base](https://mindket.com/p/82786877-568e-4d38-9323-99049f2097f5) | 353,994 | 106 | 208M | apache-2.0 | 2026-09-28 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · opliko · 2026-09-17](https://bsky.app/profile/opliko.dev/post/3mvqd2rfhx22f): And things like huggingface.co/fastino/glin... did exist already, just weren't as good without finetuning and didn't have nearly as good marketing, so the concept isn't \*completely\* new but it seems like they might've created a new wave of models in this space - [Bluesky · Hemanth HM · 2026-09-20](https://bsky.app/profile/gnumanth.bsky.social/post/3mxpce7gwfd25): In case you missed GLiNER2.5 Multi (fastino/gliner2.5-multi-v1) is an open-source, multilingual, 0.3B parameter information extraction model. Extract entities, classify text, parse structured records, score span attributes, and extract relations all in one boundary architecture" ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-05 - First published: 2026-08-14 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open fastino/gliner2.5-multi-v1 on Hugging Face](https://huggingface.co/fastino/gliner2.5-multi-v1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/104163f8-931f-40aa-a5ac-31858f99be3d) - [Its page on Mindket](https://mindket.com/p/104163f8-931f-40aa-a5ac-31858f99be3d)