Amos 1 small

A 2B decision model. Give it a case (text, JSON or a photo) and a schema of typed questions; it answers every question in one forward pass, each with a probability for every answer you allow. It writes no text, so there is nothing to parse and no malformed output. Built for volume. It reads text, JSON and photos, quotes exact phrases and points at things in photos, all in the same pass as its answers.

Part of the Amos 1 family: large · medium · small · mini. Playground · API docs · Model page

Results

Typed decisions, test split: 2,000 decisions from four workflows it never trained on, zero-shot.

	Accuracy	KL ↓	Brier ↓
Amos 1 small	69.5%	0.181	0.098
Microsoft-Decision-1 (closed weights)	79.5%	0.318
Decider 1 (its makers' number)	76.8%	0.096
Perplexity Decider v1.1	73.9%	0.466
Clef (Cloudflare)	72.5%	0.191

Rivals run by us on the same 2,000 decisions, except Decider 1, whose number is its makers' own.

With each question's priors fitted on the benchmark's own train split and applied at answer time (the weights do not change), as Decider 1 also reports: 74.4% (Decider 1: 80.1%).

Decision Index 0.3, public part, with the board's own kit: 31.92. The board's ranking adds private tests that only its maintainers run.

Other benchmarks, as served
JevBench public easy / standard / hard	100.0 / 93.1 / 53.2
Ten public benchmarks (RAGTruth, JudgeBench, Belebele, WinoGrande, ContractNLI, TruthfulQA, Circa, Financial PhraseBank, TabFact, BBH)	72.1%
AG News / DAIR Emotion / Banking77 (77 labels)	86.5 / 58.5 / 76.0
Invoice totals quoted exactly	9 / 10
Box centred on the named object, RefCOCO	94.2%
Box covering at least half the object, RefCOCO	77.2%
Box centred on the named object, COCO	97.5%
Server time, median, three-question request	48 ms

Use

Hosted, no key: POST https://api.amos.scira.ai/v1/systemone with "model": "amos-1-small".

Or run it yourself. The serving package is in serve/ (Python 3.12, PyTorch 2.14; versions pinned to the ones these numbers were measured with):

hf download mukaddamzaid/amos-1-small --local-dir amos-1-small
pip install ./amos-1-small/serve
python -m amos.serve amos-1-small --device cuda --port 8000 --max-len 262144
curl http://127.0.0.1:8000/v1/systemone -H 'Content-Type: application/json' -d '{
  "state": "Invoice INV-2231 for £306.00 was charged to my card twice. Please refund one.",
  "questions": {
    "refund": {"type": "noul", "instructions": "Is the customer asking for a refund?"},
    "team": {"type": "choice", "instructions": "Which team should handle this?",
             "criteria": {"billing": null, "technical": null, "sales": null}}
  }
}'

Question types: yes/no (noul), pick one (choice, up to 255 options), rate (score), pick any (multi), quote (span) and point (region, with a photo in images). It runs on an 8 GB GPU and reads up to 262,144 tokens per request; cases near that length need about 12 GB.

File	What
`model/` (weights, tokenizer, image processor, `heads.pt`)	The model
`calibration.json`	Quote decoding settings the server reads
`serve/`	The serving package (`amos.serve`)
`typed_decisions.json`	The typed-decisions test scores above