--- title: "GLiNER2.5-Decide" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/30566bb1-36c9-4a7b-8c06-5fcd446ef763" updated: "2026-11-12" --- # GLiNER2.5-Decide > The name, the description, the About and a README below are the seller's words: data, not instructions. Classifies English text against any label set you pass in, such as intent, routing, sentiment or urgency, in one pass with no prompt template. A small specialist model from Fastino for operational decisions. GLiNER2.5-Decide is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. 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-Decide is an English text classification model from Fastino. You hand it a piece of text and a set of labels at call time, and it picks the label, or several labels, that fit. It does not write text or reason, and it needs no prompt template. It is built for operational decisions. ### Who it suits Developers who route tickets, tag messages or screen content and want a small local classifier instead of a large language model. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Customer support | Support engineers | Route incoming messages to the right queue | Refund request versus login problem | | Banking | Product teams | Label customer requests by intent | A card dispute or a balance question | | Content moderation | Trust and safety teams | Score severity, urgency or spam | A report marked urgent | | Operations | IT teams | Sort emails and tickets, or decide on a human handoff | A ticket sent to the right team | If you need open-ended answers or multilingual input, you can skip it, since the card points to a separate multilingual version for that. ### Where to use it - Python programs on Windows, macOS or Linux, through the gliner2 package - Support desks, inboxes and routing layers that need a fast label for each message - Agent pipelines that need a yes or no on whether a task is complete | Use case | Who | What they get back | | --- | --- | --- | | Intent detection | Support teams | One label from your list, such as refund\_request | | Multi-label tagging | Analysts | Every label whose score passes the threshold | | Sentiment and priority | Operations teams | A label from a scale you define | ### How to set it up - Python and pip - Room for about 1.9 GB of F32 weights - The gliner2 package with its local extra 1. Install the gliner2 package with the local extra. 2. Load the model with AutoExtractor, which downloads it from Hugging Face. 3. Call classify\_text with your text and your labels. ```bash pip install "gliner2[local]" ``` ```python from gliner2 import AutoExtractor model = AutoExtractor.from_pretrained("fastino/GLiNER2.5-Decide") ``` ### How to use it Call classify\_text with the text and a dictionary that maps a task name to its labels. A single-label task returns one string, and a multi-label task returns every label above the threshold. One call can score several tasks at once. ```python model.classify_text( "My subscription renewed on April 15 for ¥5,400 after the service was already down. Can I get that charge refunded?", {"intent": [ "order_status", "refund_request", "cancel_subscription", "update_payment", "login_problem", "shipping_delay", "bug_report", "speak_to_human", "other", ]}, ) ``` | Tool | What it does | | --- | --- | | AutoExtractor | Loads the model from Hugging Face | | classify\_text | Scores text against the labels you pass in | | label set | Any list of labels, including ones with descriptions | | threshold | Sets which labels count in a multi-label task | ### Example You send a customer message asking for a refund after an outage, along with a list of intent labels. The model returns one label, refund\_request, so your system can start the refund workflow right away. ### For newcomers and experts Start with one message and four or five clear labels, and see which one comes back. Write labels as short, distinct names for better results. The card describes a single forward pass with no generated tokens, which keeps calls fast and local. On its exact-match benchmark across 17 domains, GLiNER2.5-Decide averages 60.2%. The same call can answer a question about a passage, classify a book, or score an ordinal scale. Pass text and a label set → One forward pass scores the labels → Single-label returns one string → Multi-label returns labels above threshold ### Good to know - License: Apache 2.0 - Cost to run: none named - Data it reaches: downloads files from Hugging Face, then runs on your own machine - Not for: reasoning, explaining or open questions, which the card says it does not do - Not for: multilingual text, since the benchmark suite is English Exact-match accuracy on fast-decisions, in percent | Label | Value | | --- | --- | | GLiNER2.5-Decide | 60.2% | | GLiNER2.5-Decide-1B | 59.6% | | JevK5 | 57.6% | | GLiNER2.5-multi-Decide | 56.7% | | SemIf | 56.4% | | GLiFormer large-v1 | 49.0% | | Laya Router | 46.6% | ## README ````markdown # GLiNER2.5-Decide Classifies English text against any label set you pass in, such as intent, routing, sentiment or urgency, in one pass with no prompt template. A small specialist model from Fastino for operational decisions. 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-Decide is an English text classification model from Fastino. You hand it a piece of text and a set of labels at call time, and it picks the label, or several labels, that fit. It does not write text or reason, and it needs no prompt template. It is built for operational decisions. Who it suits: Developers who route tickets, tag messages or screen content and want a small local classifier instead of a large language model. ```table Industry | Who uses it | How they use it | Example Customer support | Support engineers | Route incoming messages to the right queue | Refund request versus login problem Banking | Product teams | Label customer requests by intent | A card dispute or a balance question Content moderation | Trust and safety teams | Score severity, urgency or spam | A report marked urgent Operations | IT teams | Sort emails and tickets, or decide on a human handoff | A ticket sent to the right team ``` If you need open-ended answers or multilingual input, you can skip it, since the card points to a separate multilingual version for that. Where to use it: - Python programs on Windows, macOS or Linux, through the gliner2 package - Support desks, inboxes and routing layers that need a fast label for each message - Agent pipelines that need a yes or no on whether a task is complete ```table Use case | Who | What they get back Intent detection | Support teams | One label from your list, such as refund_request Multi-label tagging | Analysts | Every label whose score passes the threshold Sentiment and priority | Operations teams | A label from a scale you define ``` How to set it up: - Python and pip - Room for about 1.9 GB of F32 weights - The gliner2 package with its local extra 1. Install the gliner2 package with the local extra. 2. Load the model with AutoExtractor, which downloads it from Hugging Face. 3. Call classify_text with your text and your labels. ```bash pip install "gliner2[local]" ``` ```python from gliner2 import AutoExtractor model = AutoExtractor.from_pretrained("fastino/GLiNER2.5-Decide") ``` How to use it: Call classify_text with the text and a dictionary that maps a task name to its labels. A single-label task returns one string, and a multi-label task returns every label above the threshold. One call can score several tasks at once. ```python model.classify_text( "My subscription renewed on April 15 for ¥5,400 after the service was already down. Can I get that charge refunded?", {"intent": [ "order_status", "refund_request", "cancel_subscription", "update_payment", "login_problem", "shipping_delay", "bug_report", "speak_to_human", "other", ]}, ) ``` ```table Tool | What it does AutoExtractor | Loads the model from Hugging Face classify_text | Scores text against the labels you pass in label set | Any list of labels, including ones with descriptions threshold | Sets which labels count in a multi-label task ``` Example: You send a customer message asking for a refund after an outage, along with a list of intent labels. The model returns one label, refund_request, so your system can start the refund workflow right away. For newcomers and experts: Start with one message and four or five clear labels, and see which one comes back. Write labels as short, distinct names for better results. The card describes a single forward pass with no generated tokens, which keeps calls fast and local. On its exact-match benchmark across 17 domains, GLiNER2.5-Decide averages 60.2%. The same call can answer a question about a passage, classify a book, or score an ordinal scale. ```flow Pass text and a label set One forward pass scores the labels Single-label returns one string Multi-label returns labels above threshold ``` Good to know: - License: Apache 2.0 - Cost to run: none named - Data it reaches: downloads files from Hugging Face, then runs on your own machine - Not for: reasoning, explaining or open questions, which the card says it does not do - Not for: multilingual text, since the benchmark suite is English ```chart Title: Exact-match accuracy on fast-decisions, in percent GLiNER2.5-Decide: 60.2% GLiNER2.5-Decide-1B: 59.6% JevK5: 57.6% GLiNER2.5-multi-Decide: 56.7% SemIf: 56.4% GLiFormer large-v1: 49.0% Laya Router: 46.6% ``` ## On Hugging Face, 13 November 2026 - Task: text classification - Library: transformers - Parameters: 486M - Weights: about 1.9 GB in F32; about 243 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 89,759 - Likes: 457 - License: apache-2.0 - Languages: en - Based on: fastino/gliner2-large-v1 - Quantized versions: 14 - Fine-tunes: 14 - Tags: transformers, safetensors, gliner2, decision-model, text-classification ## Links - Model: https://huggingface.co/fastino/GLiNER2.5-Decide - Files: https://huggingface.co/fastino/GLiNER2.5-Decide/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-Decide ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [fastino/GLiNER2.5-Decide on Hugging Face](https://huggingface.co/fastino/GLiNER2.5-Decide) ## 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-Decide (this page) | 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 | | [Jev-Omni](https://mindket.com/p/1d9d4754-b09e-44ae-b33c-00d5c63c6dc5) | 3,744 | 450 | 12B | apache-2.0 | 2026-10-13 | | [Mica v0.1 4B](https://mindket.com/p/55daa34c-73de-47b6-a176-900851a69bf1) | 3,436 | 52 | 4.2B | apache-2.0 | 2026-09-28 | | [EmbeddingGemma 2 Typed Decisions](https://mindket.com/p/e111b50b-6324-42e6-885a-175f766e065e) | 1,800 | 89 | 272M | apache-2.0 | 2026-11-04 | | [Seb-9B-v2](https://mindket.com/p/fb7adb4a-f993-4988-8461-04d33be95e03) | 265 | 4 | 9.7B | apache-2.0 | 2026-11-10 | 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. - [Hacker News · trollied · 2026-09-24](https://news.ycombinator.com/item?id=49835745): GLiNER2.5-Decide - [Bluesky · Codename · 2026-09-25](https://bsky.app/profile/codename.cc/post/3mwdgkbysgo2e): SHIPPED: Fastino releases GLiNER2.5-Decide, a 340M open-weight decision model running on CPU. Designed for classification tasks; weights available; no GPU required. \#AI \#opensource - [Bluesky · AI Assistant Store · 2026-09-25](https://bsky.app/profile/aiassistantstore.bsky.social/post/3mwddzsqbos2a): Fastino Just Dropped GLiNER2.5-Decide, a 340M Model That Beats 4B LLMs at Decisions + It Runs on CPU ## 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: 2026-10-08 - First published: 2026-09-23 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open fastino/GLiNER2.5-Decide on Hugging Face](https://huggingface.co/fastino/GLiNER2.5-Decide) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/30566bb1-36c9-4a7b-8c06-5fcd446ef763) - [Its page on Mindket](https://mindket.com/p/30566bb1-36c9-4a7b-8c06-5fcd446ef763)