OpenDecision-Large-Packed

📄 Technical report: OpenDecision: Fast Schema-Conditioned Decisions with Packed Candidate Encoding (PDF; arXiv version coming soon) · Code · Demo: fine-tuned Small plays Minesweeper

OpenDecision-Large-Packed is the low-cost variant of OpenDecision-Large: the same 433.9M-parameter DeBERTa-v3-large decision model, using a packed candidate encoder that scores up to 16 options in one forward pass. The input text is encoded once per pack; attention masks and per-sequence relative positions give every option the same view it would have alone, so answers do not depend on option order.

Usage

pip install tokz-opendecision

from opendecision import OpenDecision, Choice, Bool, MultiLabel

model = OpenDecision.from_pretrained("Tokz-labs/OpenDecision-Large-Packed")
out = model.decide(
    "I was charged twice for my May invoice and the app keeps logging me out.",
    {
        "queue": Choice(["billing", "technical", "account", "sales"]),
        "tags": MultiLabel(["double_charge", "login_issue", "refund_request", "outage"]),
        "urgent": Bool(),
    },
)
print(out["queue"].best, out["tags"].selected, out["urgent"].policy)

Efficiency

GPU-seconds per 1,000 decisions on the complete classification-suite test sets, one NVIDIA L4:

Packing pays off for questions with up to ~16 options; with many more (CLINC150's 151 intents) the cross-encoder is faster. Batch-size-1 latency (L4, bf16, p50): 66 ms for a short text with 10 options; 69 ms for a 512-token text with 10 options (cross-encoder: 248 ms).

Benchmarks

Classification suite, complete test sets, macro-F1 (open models, identical inputs; GLiNER2.5 with its best interface per dataset):

* NLI data is in the training data: XNLI is not zero-shot.

On label sets that no training data targeted, averaged over TREC question types, Emotion and Subjectivity, it scores 55.64 macro-F1 (OpenDecision-Large 57.00, Laya 62.11, SemIf 64.29, JevK5 72.07); its final training stage, which adds 15,942 LLM-invented classification tasks with these task families excluded, raised this from 45.70. GoEmotions multi-label exact match is low (13.30; the model selects too many labels). For a new kind of decision, fine-tune on a few hundred labeled examples (python -m opendecision.cli.train).

Training data

Two stages from DeBERTa-v3-large (the packed stage starts from the clean cross-encoder) on LLM-generated decisions and LLM-invented classification tasks (OpenAI models), the Fast Decisions public development split, MultiNLI, WANLI and samples of DBpedia-14, BANKING77, SNLI, WikiText-103 and 20 Newsgroups (Mitchell 1997, CC BY 4.0). Every source allows commercial use under its license; sources and licenses are listed in the technical report.

Scope and limitations

English only; inputs up to 512 tokens; options truncated to 48 tokens. Not a chat or reasoning model. The benchmarks above were also used to select training mixtures. Most training text was generated or labeled by OpenAI models.

Citation

@misc{alwarawreh2026opendecision,
  title  = {OpenDecision: Fast Schema-Conditioned Decisions with Packed Candidate Encoding},
  author = {Alwarawreh, Abdallah},
  year   = {2026},
  note   = {Tokz Labs technical report},
  url    = {https://github.com/tokz-labs/OpenDecision/releases/latest/download/OpenDecision.pdf}
}