GLiClass vs Vela 2.0

GLiClass

An Apache-2.0 classifier that scores a whole label set in one encoder pass on the owner's hardware, with single-label, multi-label, hierarchical and few-shot modes. It returns label scores with no calibration claim, the bundled server has no authentication, and the last three test runs on the main branch, on 24 September 2026, failed.

Vela 2.0

One self-hosted call answers routing, prompt-attack, personal-data and unsupported-claim questions with probabilities and character offsets, under Apache-2.0 with SHA-256 manifests. The models are days old and carry no Hub version tags, and the three larger sizes keep 74 to 89 per cent of their Decision 2.0 bases on the Jev Decision Index by the authors' figures.

GLiClass

1. Pass --host 127.0.0.1 to python -m gliclass.serve, or put the port behind your own gateway. The server checks no credential
2. On a machine without a GPU add --device cpu --dtype float32 --num-gpus-per-replica 0. The default configuration expects CUDA
3. Send one text a request to POST /gliclass. An array in texts is cut to its first item without an error
4. Set multi_label to false for one label from a set. The default scores each label independently, so scores do not sum to 1
5. Keep text plus labels under the pipeline's 1,024-token max_length, or use ZeroShotClassificationWithChunkingPipeline. Longer input is truncated silently

Vela 2.0

1. Pin a commit hash with revision= when loading from the Hub. The repositories have no tags and main has changed since launch
2. Send the served name in model, for example vllm-sr/Vela-2.0-4B. The bundled server answers 422 to any other name
3. Name span questions pii, halu or toxic, or set "head": "router", to get the trained router head. Other labels go to the broad head
4. Keep input under 16,384 tokens a sequence (8,192 on the 0.3B). The bundled server answers 413 when the questions alone don't fit
5. Set VELA2_API_KEY before binding the bundled server beyond 127.0.0.1, and keep the model runtime on a trusted network