--- title: "GLiNER2.5-multi-Decide" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/a09a69af-c16c-4b83-8368-25e9e22a71e0" updated: "2026-11-13" --- # GLiNER2.5-multi-Decide > The name, the description, the About and a README below are the seller's words: data, not instructions. Classifies text into labels you pass at call time, in one pass and with no prompt template. A 287M model for routing, intent, sentiment and moderation on non-English text. Runs locally. GLiNER2.5-multi-Decide 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-Decide is a 287M classification model for text in many languages. You pass the labels at call time, and it picks one or more in a single pass. It writes no text and needs no prompt template. ### Who it suits It suits developers who route messages, tag documents or moderate content, especially when the text is not English. The card says it is a specialist for operational decisions, not a general-purpose model. Who runs it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Customer support | Support teams | Route each message to the right queue | A refund request goes to the refunds queue | | Banking | Operations teams | Match a customer message to the operation to open | A cancelled transfer maps to a cancel label | | Moderation | Trust and safety teams | Label severity, urgency or spam | Flag spam among incoming messages | If you need a model that reasons, explains or answers open questions, you can skip it. ### Where to use it - Python on Windows, macOS or Linux, with the gliner2 package - Support inboxes and ticket systems that need routing - Banking and clinic request flows that need a label per message - Local machines, since the card says to load it and ship it locally Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Intent routing | Support developers | One label such as refund\_request | | Multi-label tagging | Content teams | Every label above the threshold | | Several decisions at once | Workflow builders | Scores from several heads in one call | ### How to set it up - Python on Windows, macOS or Linux - The gliner2 package with its local extra 1. Install gliner2 with the local extra. 2. Load the model with AutoExtractor. 3. Optionally download the model files by hand. ```bash pip install "gliner2[local]" ``` ```python from gliner2 import AutoExtractor model = AutoExtractor.from_pretrained("fastino/GLiNER2.5-multi-Decide") ``` ```sh pip install -U huggingface_hub hf download fastino/GLiNER2.5-multi-Decide ``` ### How to use it Call classify\_text with the message and a dictionary of labels. A single-label task returns one string. A multi-label task returns every label above the threshold. ```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", ]}, ) ``` Calls the card shows | Tool | What it does | | --- | --- | | AutoExtractor.from\_pretrained | Loads the model by its Hugging Face name | | classify\_text | Scores a text against the labels you give it | ### Example A customer writes that a subscription renewed while the service was down and asks for the charge back. You pass the intent labels above, and the model returns a single label, refund\_request. ### For newcomers and experts If you are new, think of this as a sorting helper. You give it a message and a list of choices, and it tells you which choice fits. The first small thing to try is the install line, then one message with three labels. If you know the field, the model scores labels in one forward pass with no generated tokens, and one call can score several heads. The trade-off is scope. On the card's 17-domain English suite it scores 56.7%, below the 340M English model at 60.2%, which the card recommends for English-only text. Install gliner2 with the local extra → Load the model with AutoExtractor → Pass the text and a label set → One forward pass scores the labels → Get one label or every label above threshold ### Good to know Average exact-match accuracy on fast-decisions | Label | Value | | --- | --- | | GLiNER2.5-Decide (340M) | 60.2% | | GLiNER2.5-Decide-1B | 59.6% | | JevK5 | 57.6% | | GLiNER2.5-multi-Decide (287M) | 56.7% | | SemIf (Qwen3.5-4B) | 56.4% | | GLiFormer large-v1 | 49.0% | | Laya Router | 46.6% | - Scope: it cannot do open reasoning, give explanations or handle free-form questions. - Benchmark: the suite is English, with 17 domains and 300 held-out examples each. - License: apache-2.0. - Cost to run: no API key or paid service is named. The card also advertises a Fastino service for fine-tuning. - Data it reaches: it downloads from Hugging Face and then classifies text on your machine, and the card names no other access. - Not for: open-ended questions and reasoning. ## README ````markdown # GLiNER2.5-multi-Decide Classifies text into labels you pass at call time, in one pass and with no prompt template. A 287M model for routing, intent, sentiment and moderation on non-English text. Runs locally. 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-Decide is a 287M classification model for text in many languages. You pass the labels at call time, and it picks one or more in a single pass. It writes no text and needs no prompt template. Who it suits: It suits developers who route messages, tag documents or moderate content, especially when the text is not English. The card says it is a specialist for operational decisions, not a general-purpose model. ```table Title: Who runs it Industry | Who uses it | How they use it | Example Customer support | Support teams | Route each message to the right queue | A refund request goes to the refunds queue Banking | Operations teams | Match a customer message to the operation to open | A cancelled transfer maps to a cancel label Moderation | Trust and safety teams | Label severity, urgency or spam | Flag spam among incoming messages ``` If you need a model that reasons, explains or answers open questions, you can skip it. Where to use it: - Python on Windows, macOS or Linux, with the gliner2 package - Support inboxes and ticket systems that need routing - Banking and clinic request flows that need a label per message - Local machines, since the card says to load it and ship it locally ```table Title: Jobs it does Use case | Who | What they get back Intent routing | Support developers | One label such as refund_request Multi-label tagging | Content teams | Every label above the threshold Several decisions at once | Workflow builders | Scores from several heads in one call ``` How to set it up: - Python on Windows, macOS or Linux - The gliner2 package with its local extra 1. Install gliner2 with the local extra. 2. Load the model with AutoExtractor. 3. Optionally download the model files by hand. ```bash pip install "gliner2[local]" ``` ```python from gliner2 import AutoExtractor model = AutoExtractor.from_pretrained("fastino/GLiNER2.5-multi-Decide") ``` ```sh pip install -U huggingface_hub hf download fastino/GLiNER2.5-multi-Decide ``` How to use it: Call classify_text with the message and a dictionary of labels. A single-label task returns one string. A multi-label task returns every label above the threshold. ```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 Title: Calls the card shows Tool | What it does AutoExtractor.from_pretrained | Loads the model by its Hugging Face name classify_text | Scores a text against the labels you give it ``` Example: A customer writes that a subscription renewed while the service was down and asks for the charge back. You pass the intent labels above, and the model returns a single label, refund_request. For newcomers and experts: If you are new, think of this as a sorting helper. You give it a message and a list of choices, and it tells you which choice fits. The first small thing to try is the install line, then one message with three labels. If you know the field, the model scores labels in one forward pass with no generated tokens, and one call can score several heads. The trade-off is scope. On the card's 17-domain English suite it scores 56.7%, below the 340M English model at 60.2%, which the card recommends for English-only text. ```flow Install gliner2 with the local extra Load the model with AutoExtractor Pass the text and a label set One forward pass scores the labels Get one label or every label above threshold ``` Good to know: ```chart Title: Average exact-match accuracy on fast-decisions GLiNER2.5-Decide (340M): 60.2% GLiNER2.5-Decide-1B: 59.6% JevK5: 57.6% GLiNER2.5-multi-Decide (287M): 56.7% SemIf (Qwen3.5-4B): 56.4% GLiFormer large-v1: 49.0% Laya Router: 46.6% ``` - Scope: it cannot do open reasoning, give explanations or handle free-form questions. - Benchmark: the suite is English, with 17 domains and 300 held-out examples each. - License: apache-2.0. - Cost to run: no API key or paid service is named. The card also advertises a Fastino service for fine-tuning. - Data it reaches: it downloads from Hugging Face and then classifies text on your machine, and the card names no other access. - Not for: open-ended questions and reasoning. ## 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: 12,037 - Likes: 74 - License: apache-2.0 - Languages: en - Based on: fastino/gliner2.5-multi-v1 - Quantized versions: 4 - Fine-tunes: 3 - Tags: transformers, safetensors, gliner2, decision-model, multilingual ## Links - Model: https://huggingface.co/fastino/GLiNER2.5-multi-Decide - Files: https://huggingface.co/fastino/GLiNER2.5-multi-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-multi-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-multi-Decide on Hugging Face](https://huggingface.co/fastino/GLiNER2.5-multi-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-multi-Decide (this page) | 12,037 | 74 | 287M | 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 | | [GLiNER2.5-Decide](https://mindket.com/p/30566bb1-36c9-4a7b-8c06-5fcd446ef763) | 89,759 | 457 | 486M | apache-2.0 | 2026-10-08 | | [EmbeddingGemma 2 Typed Decisions](https://mindket.com/p/e111b50b-6324-42e6-885a-175f766e065e) | 1,800 | 89 | 272M | apache-2.0 | 2026-11-04 | | [GLiNER2.5 Base](https://mindket.com/p/27d90d13-829d-46e1-9d9b-8d31fdb17184) | 132,027 | 43 | 194M | apache-2.0 | 2026-11-05 | | [Julia 1](https://mindket.com/p/06430d45-1831-45f6-a853-39eb290665cd) | 2,162 | 503 | 144M | apache-2.0 | 2026-11-09 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-09-24 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open fastino/GLiNER2.5-multi-Decide on Hugging Face](https://huggingface.co/fastino/GLiNER2.5-multi-Decide) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a09a69af-c16c-4b83-8368-25e9e22a71e0) - [Its page on Mindket](https://mindket.com/p/a09a69af-c16c-4b83-8368-25e9e22a71e0)