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Fastino's open-weight decision model. A DeBERTa-v3-large encoder that scores label sets supplied at runtime, answers single-label, multi-label, yes/no and ordinal questions in one pass, and extracts spans and relations.
GLiNER2.5-Decide joins classification, extraction and cross-decision rules in one constrained decode; nothing is generated. Fastino reports 60.2% on its own fast-decisions suite of 17 datasets (the launch post says 60.1%) and 38 ms median on a V100, 167 ms on a 48-vCPU CPU. The card says 340M parameters; the safetensors header counts 486M. Two Apache-2.0 siblings share the recipe: GLiNER2.5-Decide-1B (59.6%) and GLiNER2.5-multi-Decide (287M, multilingual, 56.7%). It loads through the gliner2 package rather than the /v1/systemone wire format, and is also served through Fastino's API.
| Parameters | 340M |
| Base model | fastino/gliner2-large-v1 |
| Maker | Fastino Labs |
| Released | 2026-09-24 |
| License | apache-2.0 |
| Reported accuracy | 60.2% |
| Reported latency | 38 ms p50 on V100; 43–47 ms on T4/L4/A100; 167 ms on a 48-vCPU CPU |
Served by Fastino. Get access.
pip install systemonemodels
systemone pull fastino-labs/gliner2-5-decide
The files are served from the maker's Hugging Face repository, fastino/GLiNER2.5-Decide, and verified against the checksums recorded here.
This page was opened by System One for Fastino Labs, who can claim the organisation and take it over at any time.
Build a request for fastino-labs/gliner2-5-decide. The form and the JSON are the same object; the response panel shows the shape every System One server returns.
Placeholder values, in the shape a run returns. Inference through System One is coming.
safenoul7%Inference through System One is coming. Until then, send this request to Fastino's endpoint or your own server for this model; every System One server speaks this shape.
curl https://api.systemonemodels.tech/v1/systemone \
-H "Authorization: Bearer $SYSTEMONE_TOKEN" \
-H "Content-Type: application/json" \
-d '{"model":"fastino-labs/gliner2-5-decide","state":"Task: clean up inactive accounts before the quarterly report.\nProposed tool call: delete_rows(table=\"customers\", where=\"last_login < 2023-01-01\")\nContext: the customers table is production; there is no backup newer than Friday.","questions":{"safe":{"type":"noul","instructions":"Is this action safe to run without a human approving it first?"}}}'