--- title: "GLiNER2.5 Base" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/27d90d13-829d-46e1-9d9b-8d31fdb17184" updated: "2026-11-13" --- # GLiNER2.5 Base > The name, the description, the About and a README below are the seller's words: data, not instructions. Pulls entities, relations and structured records out of English text, and classifies it, from one schema, running locally on CPU, CUDA or MPS. It suits developers who build text extraction pipelines. GLiNER2.5 Base 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 Base is the English checkpoint of fastino's GLiNER2.5 family, built at DeBERTa-v3-base scale. One schema drives several jobs: named entities, text classification, structured records, relations and span attributes. It runs on your own machine and needs no external API. ### Who it suits Developers and data teams who turn English text into labels, records and relations suit it best. It runs from Python. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Backend developers | Extract people, companies and products from text | Mark entities in a news line with character offsets | | Data operations | Data teams | Turn free text into structured records | Fill fields from a product description | | Research | NLP researchers | Study schema-driven extraction with constrained decoding | Link entities and relations in one typed graph | Anyone who works in other languages can skip it, because this checkpoint is English only. A separate multilingual checkpoint exists. ### Where to use it You run it in Python on your own computer. - Python scripts that import the gliner2 package - Windows, macOS or Linux with Python 3.10 or newer - CPU, CUDA or MPS devices | Use case | Who | What they get back | | --- | --- | --- | | Entity extraction | Developers | Entities with text, offsets and confidence | | Classification | Data teams | Labels for each text | | Structured records | Data teams | Fields filled from free text | | Entity-relation graphs | Researchers | Typed links between entities | ### How to set it up You need Python 3.10 or newer and the gliner2 package with its local extra, which pulls in PyTorch. - Python 3.10 or newer - The gliner2 package with the local extra - Disk space for about 774 MB of weights in F32 1. Install Python 3.10 or newer. 2. Install the gliner2 package with the local extra. 3. Optionally download the model files ahead of time with the hub tools. 4. Load the model with AutoExtractor by its name. ```bash pip install "gliner2[local]" ``` ```sh pip install -U huggingface_hub hf download fastino/gliner2.5-base-v1 ``` ### How to use it Always load this checkpoint with AutoExtractor. The older GLiNER2 loader is for span models and will not dispatch it. ```python from gliner2 import AutoExtractor model = AutoExtractor.from_pretrained("fastino/gliner2.5-base-v1") print(type(model).__name__) print(model.config.architecture) # BoundaryExtractor # boundary ``` Then call extract\_entities with a text and your labels. The two options below add a confidence score and character spans. ```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, ) print(result) ``` | Tool | What it does | | --- | --- | | AutoExtractor | Loads the checkpoint and picks its architecture | | extract\_entities | Finds entities for the labels you give | | Classifier | Applies label constraints across tasks | | JointIE | Builds typed entity and relation graphs | ### Example You pass a sentence about a company CEO announcing a phone in a city, with the labels company, person, product and location. Each match comes back with its text, character offsets and a confidence score. ### For newcomers and experts Think of the model as a reader that marks up text for you. You say what to look for, and it returns the matches. Your first small try is the entity example above with a sentence of your own. Under the hood, the boundary architecture pairs start and end points sparsely instead of scoring a fixed grid of span widths. Offsets are half-open character spans into your string. Constrained decoding adds cross-task label limits and typed entity-relation graphs. The record lists 194M parameters. Your text and a schema or labels → The boundary extractor pairs start and end points → Constrained decoding applies label rules → Matches come back with offsets and scores ### Good to know Two sibling checkpoints share the same public API, and this card covers only the base one. The GLiNER2.5 family | Model | Parameters | Encoder | Use case | | --- | --- | --- | --- | | gliner2.5-small-v1 | 74M | DeBERTa-v3-xsmall | Quick extraction and classification on a CPU | | gliner2.5-base-v1 | 194M | DeBERTa-v3-base | The default English multi-task checkpoint | | gliner2.5-multi-v1 | 287M | mDeBERTa-v3-base | The default multilingual multi-task checkpoint | - Size: 194M parameters, about 774 MB in F32, stored as Safetensors - Language: English - Other versions: Hugging Face lists 4 quantized and 12 fine-tuned versions made by other people - License: apache-2.0 - Cost to run: none named - Data it reaches: the text you give it, and the model files downloaded from Hugging Face ## README ````markdown # GLiNER2.5 Base Pulls entities, relations and structured records out of English text, and classifies it, from one schema, running locally on CPU, CUDA or MPS. It suits developers who build text extraction 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 Base is the English checkpoint of fastino's GLiNER2.5 family, built at DeBERTa-v3-base scale. One schema drives several jobs: named entities, text classification, structured records, relations and span attributes. It runs on your own machine and needs no external API. Who it suits: Developers and data teams who turn English text into labels, records and relations suit it best. It runs from Python. ```table Industry | Who uses it | How they use it | Example Software | Backend developers | Extract people, companies and products from text | Mark entities in a news line with character offsets Data operations | Data teams | Turn free text into structured records | Fill fields from a product description Research | NLP researchers | Study schema-driven extraction with constrained decoding | Link entities and relations in one typed graph ``` Anyone who works in other languages can skip it, because this checkpoint is English only. A separate multilingual checkpoint exists. Where to use it: You run it in Python on your own computer. - Python scripts that import the gliner2 package - Windows, macOS or Linux with Python 3.10 or newer - CPU, CUDA or MPS devices ```table Use case | Who | What they get back Entity extraction | Developers | Entities with text, offsets and confidence Classification | Data teams | Labels for each text Structured records | Data teams | Fields filled from free text Entity-relation graphs | Researchers | Typed links between entities ``` How to set it up: You need Python 3.10 or newer and the gliner2 package with its local extra, which pulls in PyTorch. - Python 3.10 or newer - The gliner2 package with the local extra - Disk space for about 774 MB of weights in F32 1. Install Python 3.10 or newer. 2. Install the gliner2 package with the local extra. 3. Optionally download the model files ahead of time with the hub tools. 4. Load the model with AutoExtractor by its name. ```bash pip install "gliner2[local]" ``` ```sh pip install -U huggingface_hub hf download fastino/gliner2.5-base-v1 ``` How to use it: Always load this checkpoint with AutoExtractor. The older GLiNER2 loader is for span models and will not dispatch it. ```python from gliner2 import AutoExtractor model = AutoExtractor.from_pretrained("fastino/gliner2.5-base-v1") print(type(model).__name__) print(model.config.architecture) # BoundaryExtractor # boundary ``` Then call extract_entities with a text and your labels. The two options below add a confidence score and character spans. ```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, ) print(result) ``` ```table Tool | What it does AutoExtractor | Loads the checkpoint and picks its architecture extract_entities | Finds entities for the labels you give Classifier | Applies label constraints across tasks JointIE | Builds typed entity and relation graphs ``` Example: You pass a sentence about a company CEO announcing a phone in a city, with the labels company, person, product and location. Each match comes back with its text, character offsets and a confidence score. For newcomers and experts: Think of the model as a reader that marks up text for you. You say what to look for, and it returns the matches. Your first small try is the entity example above with a sentence of your own. Under the hood, the boundary architecture pairs start and end points sparsely instead of scoring a fixed grid of span widths. Offsets are half-open character spans into your string. Constrained decoding adds cross-task label limits and typed entity-relation graphs. The record lists 194M parameters. ```flow Your text and a schema or labels The boundary extractor pairs start and end points Constrained decoding applies label rules Matches come back with offsets and scores ``` Good to know: Two sibling checkpoints share the same public API, and this card covers only the base one. ```table Title: The GLiNER2.5 family Model | Parameters | Encoder | Use case gliner2.5-small-v1 | 74M | DeBERTa-v3-xsmall | Quick extraction and classification on a CPU gliner2.5-base-v1 | 194M | DeBERTa-v3-base | The default English multi-task checkpoint gliner2.5-multi-v1 | 287M | mDeBERTa-v3-base | The default multilingual multi-task checkpoint ``` - Size: 194M parameters, about 774 MB in F32, stored as Safetensors - Language: English - Other versions: Hugging Face lists 4 quantized and 12 fine-tuned versions made by other people - License: apache-2.0 - Cost to run: none named - Data it reaches: the text you give it, and the model files downloaded from Hugging Face ## On Hugging Face, 14 November 2026 - Library: transformers - Parameters: 194M - Weights: about 774 MB in F32; about 97 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 132,027 - Likes: 43 - License: apache-2.0 - Languages: en - Quantized versions: 4 - Fine-tunes: 12 - Tags: transformers, safetensors, gliner2, information-extraction, boundary-extraction ## Links - Model: https://huggingface.co/fastino/gliner2.5-base-v1 - Files: https://huggingface.co/fastino/gliner2.5-base-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-base-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-base-v1 on Hugging Face](https://huggingface.co/fastino/gliner2.5-base-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 Base (this page) | 132,027 | 43 | 194M | apache-2.0 | 2026-11-05 | | [GLiNER2.5 Multi](https://mindket.com/p/104163f8-931f-40aa-a5ac-31858f99be3d) | 88,821 | 300 | 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 | | [GLiNER2.5-multi-Decide](https://mindket.com/p/a09a69af-c16c-4b83-8368-25e9e22a71e0) | 12,037 | 74 | 287M | apache-2.0 | 2026-11-05 | | [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 · 0xAndrewShu · 2026-10-06](https://bsky.app/profile/ashu.co/post/3mx7uqdbl722x): Beyond Jev, what do folks think about GLiNER2.5 by Fastino. It's also a lightweight, encoder-only, open-source model. Similar inference pricing (cheaper, I think). But GLiNER can also do text extraction to JSON, not just finite decision outputs. Model: huggingface.co/fastino/glin... ## 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: 2026-08-18 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open fastino/gliner2.5-base-v1 on Hugging Face](https://huggingface.co/fastino/gliner2.5-base-v1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/27d90d13-829d-46e1-9d9b-8d31fdb17184) - [Its page on Mindket](https://mindket.com/p/27d90d13-829d-46e1-9d9b-8d31fdb17184)