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GLiNER2.5 Multi is Fastino’s multilingual, multi-task information-extraction model. Use one schema to extract entities, classify text, build structured records, identify relations, and attach attributes to spans.

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When to use GLiNER 2.5

Use GLiNER2.5 Multi when extraction or multilingual coverage is central to the task:
  • Entity extraction - pull people, organizations, products, locations, or domain-specific entities (medications, dosages, clauses) out of free text, with character offsets and confidence scores.
  • Structured records - turn “Alice bought apples and Bob bought oranges” into [{"buyer": "Alice", "item": "apples"}, {"buyer": "Bob", "item": "oranges"}], keeping each instance’s fields linked.
  • Relation and knowledge-graph extraction - extract typed entity-relation graphs (works_for, located_in) for agent memory or a knowledge base.
  • Span attributes - tag each extracted entity with an attribute, such as per-person sentiment, in the same pass as extraction.
  • Long documents - process an encoded context of up to 4,096 tokens, with chunking and offset remapping for longer documents.
  • Multiple tasks at once - combine entities, classification, structures, and relations in a single schema-constrained call instead of chaining several model requests.
  • Consistent outputs - use constrained classification or joint entity-relation decoding when labels and relation endpoints must agree.
GLiNER2.5 Multi also supports classification. Choose GLiNER-2.5-Decide when the input is English and classification itself—intent, routing, sentiment, priority, moderation, or scoring—is the primary workload.

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How to use GLiNER 2.5

Call the multilingual checkpoint through Fastino’s OpenAI-compatible inference endpoint. Put the text in a user message and describe the task in schema:
Add classifications, structures, or relations keys to the same schema to run several tasks over the same text in one call. See the Inference API reference for the full request and response contract, including structured records and relation schemas.

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Constrained classification with GLiNER2.5 Multi

Use constrained classification when several answers must form one valid decision. Unlike classic classifications, which selects each task independently, the constrained decoder searches for a joint assignment that satisfies declared rules. GLiNER2.5 Multi supports three task shapes: Each task can include answer descriptions, an instruction, and labeled examples. These fields give the encoder context for scoring while keeping output restricted to the declared answers. Constraints connect those task results: The result contains each task’s selected answer, answer probabilities, confidence, and _meta.feasible. Constraints can select a lower-probability answer when the independent top choices would violate a rule. They enforce consistency; they do not increase or recalibrate probabilities or confidence. For single-answer and ordinal tasks, confidence is the selected answer’s probability. For multiple-answer tasks, it is the geometric mean of each selected answer’s probability and each unselected answer’s complement (1 - probability). An empty multiple-answer result without a default has no confidence value. See the constrained-classification API example for the complete hosted request, supported schema fields, constraints, and response.

Explore GLiNER2.5

Read about the architecture, benchmarks, and use cases.

GLiNER-2.5-Decide

Use the classification-tuned English model for operational decisions.

View the model catalog

Browse Fastino models and their model IDs.

Inference API reference

Full request and response contract, including structured records and relations.