--- title: "GLiNER2 base" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/82786877-568e-4d38-9323-99049f2097f5" updated: "2026-11-13" --- # GLiNER2 base > The name, the description, the About and a README below are the seller's words: data, not instructions. Extracts entities, classifies text, pulls out structured data and finds relations in plain text, with one small model that runs on a CPU. It suits developers who want information extraction without a GPU or an outside API. GLiNER2 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 base is an information extraction model from fastino. A single model handles four jobs. It finds named entities, labels whole texts, pulls fields into a structure and links entities through relations. It was made so you do not need a chain of separate tools or an outside API for them. You describe what you want, such as a list of entity labels or a set of fields, and it reads your text for those. The card says it is built for efficient CPU inference. ### Who it suits Developers and data teams who need to turn free text into labels and fields suit it best. It runs from Python. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Backend developers | Pull people, companies and products out of text | Extract entities from a news line | | Customer support | Support teams | Sort comments by sentiment or topic | Label a review as positive or negative | | E-commerce | Catalog teams | Turn product descriptions into fields | Read name, storage and price from a listing | Anyone who wants a chat assistant can skip it, because it reads text and returns structure, not conversation. ### Where to use it You run it yourself in Python, on Windows, macOS or Linux. - Python scripts that import the gliner2 package - Windows, macOS or Linux with Python installed - Machines without a GPU, since the card says it is made for CPU use | Use case | Who | What they get back | | --- | --- | --- | | Entity extraction | Developers | A dictionary of entities grouped under your labels | | Text classification | Support teams | A single label or several labels for each text | | Structured extraction | Catalog teams | Fields such as name, storage and price | ### How to set it up You need Python and the gliner2 package. The model downloads when you load it by name. - Python on Windows, macOS or Linux - The gliner2 package, installed with pip - Disk space for about 834 MB of weights in F32 1. Install Python. 2. Install the gliner2 package with pip. 3. Optionally download the model files ahead of time with the hub tools. 4. Load the model by its name in a Python script. ```bash pip install gliner2 ``` ```sh pip install -U huggingface_hub hf download fastino/gliner2-base-v1 ``` ### How to use it Load the model, then call the method for the job you want. For entities you pass the text and a list of labels. ```python from gliner2 import GLiNER2 # Load the model extractor = GLiNER2.from_pretrained("fastino/gliner2-base-v1") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) # Output: {'entities': {'company': ['Apple'], 'person': ['Tim Cook'], 'product': ['iPhone 15'], 'location': ['Cupertino']}} ``` For classification you pass the text and the labels you allow. ```python # Single-label classification result = extractor.classify_text( "This laptop has amazing performance but terrible battery life!", {"sentiment": ["positive", "negative", "neutral"]} ) print(result) # Output: {'sentiment': 'negative'} ``` | Tool | What it does | | --- | --- | | extract\_entities | Finds entities for the labels you give | | classify\_text | Picks one label, or several, for a text | | extract\_json | Fills the fields you describe from a text | | create\_schema | Builds one schema that combines several tasks | | extract | Runs a combined schema over a text | ### Example You pass a sentence about Apple's CEO Tim Cook announcing a phone in Cupertino, with the labels company, person, product and location. It returns Apple as the company, Tim Cook as the person, the phone as the product and Cupertino as the location. ### For newcomers and experts Think of the model as a reader that marks up a text for you. You say what to look for, and it hands back labels and fields. Your first small try is the entity example above with a sentence of your own. Under the hood, one model handles four tasks in a single forward pass, and you steer it with labels or a schema at run time. The card describes about 205M parameters and CPU-first inference with local processing. The trade-off is that the labels you write decide what you get back. Your text and your labels or schema → One model reads them together → Entities, classes, fields or relations → A plain Python dictionary comes back ### Good to know The card says processing stays local and needs no outside service once the model files are on your machine. - Size: 208M parameters in the record, about 834 MB in F32, stored as Safetensors - Language: English - Other versions: Hugging Face lists 6 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 base Extracts entities, classifies text, pulls out structured data and finds relations in plain text, with one small model that runs on a CPU. It suits developers who want information extraction without a GPU or an outside API. 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 base is an information extraction model from fastino. A single model handles four jobs. It finds named entities, labels whole texts, pulls fields into a structure and links entities through relations. It was made so you do not need a chain of separate tools or an outside API for them. You describe what you want, such as a list of entity labels or a set of fields, and it reads your text for those. The card says it is built for efficient CPU inference. Who it suits: Developers and data teams who need to turn free text into labels and fields suit it best. It runs from Python. ```table Industry | Who uses it | How they use it | Example Software | Backend developers | Pull people, companies and products out of text | Extract entities from a news line Customer support | Support teams | Sort comments by sentiment or topic | Label a review as positive or negative E-commerce | Catalog teams | Turn product descriptions into fields | Read name, storage and price from a listing ``` Anyone who wants a chat assistant can skip it, because it reads text and returns structure, not conversation. Where to use it: You run it yourself in Python, on Windows, macOS or Linux. - Python scripts that import the gliner2 package - Windows, macOS or Linux with Python installed - Machines without a GPU, since the card says it is made for CPU use ```table Use case | Who | What they get back Entity extraction | Developers | A dictionary of entities grouped under your labels Text classification | Support teams | A single label or several labels for each text Structured extraction | Catalog teams | Fields such as name, storage and price ``` How to set it up: You need Python and the gliner2 package. The model downloads when you load it by name. - Python on Windows, macOS or Linux - The gliner2 package, installed with pip - Disk space for about 834 MB of weights in F32 1. Install Python. 2. Install the gliner2 package with pip. 3. Optionally download the model files ahead of time with the hub tools. 4. Load the model by its name in a Python script. ```bash pip install gliner2 ``` ```sh pip install -U huggingface_hub hf download fastino/gliner2-base-v1 ``` How to use it: Load the model, then call the method for the job you want. For entities you pass the text and a list of labels. ```python from gliner2 import GLiNER2 # Load the model extractor = GLiNER2.from_pretrained("fastino/gliner2-base-v1") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) # Output: {'entities': {'company': ['Apple'], 'person': ['Tim Cook'], 'product': ['iPhone 15'], 'location': ['Cupertino']}} ``` For classification you pass the text and the labels you allow. ```python # Single-label classification result = extractor.classify_text( "This laptop has amazing performance but terrible battery life!", {"sentiment": ["positive", "negative", "neutral"]} ) print(result) # Output: {'sentiment': 'negative'} ``` ```table Tool | What it does extract_entities | Finds entities for the labels you give classify_text | Picks one label, or several, for a text extract_json | Fills the fields you describe from a text create_schema | Builds one schema that combines several tasks extract | Runs a combined schema over a text ``` Example: You pass a sentence about Apple's CEO Tim Cook announcing a phone in Cupertino, with the labels company, person, product and location. It returns Apple as the company, Tim Cook as the person, the phone as the product and Cupertino as the location. For newcomers and experts: Think of the model as a reader that marks up a text for you. You say what to look for, and it hands back labels and fields. Your first small try is the entity example above with a sentence of your own. Under the hood, one model handles four tasks in a single forward pass, and you steer it with labels or a schema at run time. The card describes about 205M parameters and CPU-first inference with local processing. The trade-off is that the labels you write decide what you get back. ```flow Your text and your labels or schema One model reads them together Entities, classes, fields or relations A plain Python dictionary comes back ``` Good to know: The card says processing stays local and needs no outside service once the model files are on your machine. - Size: 208M parameters in the record, about 834 MB in F32, stored as Safetensors - Language: English - Other versions: Hugging Face lists 6 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, 13 November 2026 - Library: gliner2 - Parameters: 208M - Weights: about 834 MB in F32; about 104 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 353,994 - Likes: 106 - License: apache-2.0 - Languages: en - Quantized versions: 6 - Fine-tunes: 12 - Tags: gliner2, safetensors, extractor ## Links - Model: https://huggingface.co/fastino/gliner2-base-v1 - Files: https://huggingface.co/fastino/gliner2-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-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-base-v1 on Hugging Face](https://huggingface.co/fastino/gliner2-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 base (this page) | 353,994 | 106 | 208M | apache-2.0 | 2026-09-28 | | [GLiNER2.5 Base](https://mindket.com/p/27d90d13-829d-46e1-9d9b-8d31fdb17184) | 132,027 | 43 | 194M | apache-2.0 | 2026-11-05 | | [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](https://mindket.com/p/104163f8-931f-40aa-a5ac-31858f99be3d) | 88,821 | 300 | 287M | 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 | | [all-MiniLM-L6-v2](https://mindket.com/p/ee5187e2-9f20-47be-ae7e-3a6007276a39) | 174,740,184 | 6,104 | 22.7M | apache-2.0 | 2026-06-01 | 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 · Medenor.fr · 2026-03-04](https://bsky.app/profile/medenor.bsky.social/post/3mgaskesjo22v): Développement d'un petit outil pour anonymiser du contenu sensible avant d'envoyer sur un serveur ou \#LLM type \#ChatGPT ou \#Claude... Et désanonymiser en retour la réponse. Tout ça grâce à fastino/gliner2-base-v1 🚀 Développement en \#opensource sur \#Codeberg codeberg.org/medenor/clavis (licence MIT) ## 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: 2026-09-28 - First published: 2025-07-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open fastino/gliner2-base-v1 on Hugging Face](https://huggingface.co/fastino/gliner2-base-v1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/82786877-568e-4d38-9323-99049f2097f5) - [Its page on Mindket](https://mindket.com/p/82786877-568e-4d38-9323-99049f2097f5)