Schemer, on-device text to structured JSON

Schemer is a schema-native extraction model: it takes free text and a developer-supplied schema, and returns a JSON object that matches the schema. The schema is the model's input, not a prompt suggestion, and every field is decoded by a head built for its type. It does not generate text.

211M parameters: 218 MB int8 (verified lossless) or 111 MB int4
13 languages, 0.075 per-language accuracy spread
Nested schemas: arrays of objects extract via deterministic segment-and-recurse in the harness (0.99 on the internal nested eval)
Documents up to ~1,100 tokens (roughly 3 pages); short inputs run a fast 256-token path
Offline, ~200 MB RAM; 8.7 ms per encoder pass on an Apple Neural Engine

Three guarantees no generative extractor offers:

Typed by construction. Labels are always one of your declared choices, numbers arrive clamped to your range, datetimes are ISO 8601, and relative expressions ("tomorrow at 3pm", "i morgen kl 15", misspellings included) resolve against device time.
Absence detection. Fields the text does not state come back null. Schemer scores 0.91 on this; every LLM and span extractor we benched, at any size, scores between 0.18 and 0.43.
Verbatim with offsets. Extracted strings are substrings of the input; the SDK surfaces character offsets, so hallucination auditing is a substring check.

Private pre-release. Weights are staged here ahead of the SDK release.

Try it

iOS / macOS: Swift SDK coming soon. The int4 Core ML encoders in coreml/ are ANE-validated (8.1 ms/forward at the 256-token shape) and eval-confirmed on-device.
Android / web: ONNX graphs are staged in onnx/ (int4 web encoder + fp32 support graphs, quality measured through the actual graphs); the JS harness port ships with the SDK.
The deterministic harness (relative dates, duration units, format gates, string trims, anchor injection) ships as data in harness/*.json; every SDK port must pass the bundled conformance fixtures bit-for-bit.

Files

File	Format	Size	Use
`model-int8.pt`	int8 per-row	218 MB	start here; full quality (0.800 overall)
`model-int4-awq.pt`	int4 AWQ	111 MB	when download or RAM is tight (0.793 overall)
`model-fp32.pt`	fp32	850 MB	converting to other formats; not for deployment
`onnx/encoder_web4e4.onnx`	int4 ONNX	111 MB	run in the browser or on Android
`onnx/*.onnx`	fp32 ONNX	80 MB	decoding graphs; always ship with the encoder
`coreml/*.mlpackage.zip`	int4 Core ML	84 MB each	run on iOS / macOS; 256 + 1216 token shapes, ANE-resident, eval-confirmed on-device
`tokenizer/`, `keep_ids.json`		11 MB	required by every runtime
`harness/*.json`		<1 MB	post-processing and nested-recursion rules every SDK port must implement
`manifest.json`			verify downloads; measured quality per artifact

Use

The schema is plain JSON (paste an OpenAI/Gemini responseSchema and it works). One call per document:

{
  "title":        {"type": "string",  "describe": "the task title", "max_chars": 60},
  "guest":        {"type": "string",  "describe": "person the meeting is with", "nullable": true},
  "start":        {"type": "datetime","describe": "start time", "nullable": true},
  "duration_min": {"type": "number",  "describe": "duration in minutes", "min": 0, "max": 480, "nullable": true},
  "is_recurring": {"type": "boolean", "describe": "is this a recurring task"}
}

Input "Meeting with Sarah tomorow at 3pm for an hour to review the Q3 deck. Recurring weekly." returns (with device time 2026-07-05):

{"title": "review the Q3 deck", "guest": "Sarah",
 "start": "2026-07-06T15:00", "duration_min": 60, "is_recurring": true}

Note the typo tolerance, the relative-date resolution, the unit conversion ("an hour" to 60), and the factoring: the title is the purpose of the meeting, the person lands in guest, and the temporal clutter lands in the temporal fields.

Model

Encoder: pruned mmBERT-base (22 layers, 103k-token vocabulary kept of 256k), jointly encoding [schema summary ||| document]. Two compiled input shapes: 256 tokens (records) and 1216 tokens (documents). The 1216-token shape is the input limit: schema summary + document together; longer inputs are truncated, so budget roughly 1,100 tokens of document.
Reader: one shared cross-attention reader over encoder states.
Heads: thin per-type decoders: label entailment, 3-way boolean (absent/false/true), BIO span tagging for strings and arrays with trained presence gates, datetime and number component decoders.
Harness: deterministic post-processing (locale number parsing, ISO composition, 13-language relative-date lexicons, format gates). Shipped as data plus a reference implementation; ports are conformance-tested.

Evaluation

9,021 held-out records across seven usage slices, 13 languages, schemas the model never saw, one order-insensitive, absence-aware scorer for every model. The overall score weights the slices by product usage: short records 0.30, long documents 0.15, long documents with same-type distractors 0.15, reviews and registers 0.15, calendar 0.10, factored domains 0.10, nested schemas 0.05. Every model ran in its documented configuration (tool calling for FunctionGemma, template prompts for NuExtract-2.0, JSON chat for the instruct models, in-process span extraction for GLiNER2); sizes are measured bytes of the artifact benched.

Model	Overall	Absence (boolean)	Params	On disk
Gemma 4 26B A4B (int4 AWQ)	0.834	0.428	26.6B	17.2 GB
Claude Haiku 4.5 (API)	0.816	0.431	n/a	API only
Qwen 3.5 9B	0.812	0.434	9.1B	9.1 GB
Schemer	0.800	0.911	211M	218 MB int8
Schemer (int4)	0.793	0.908	211M	111 MB int4
Ministral 3 8B	0.790	0.430	8.0B	17.8 GB
Gemma 4 E2B (official int4 QAT)	0.780	0.427	5.1B	8.3 GB
Qwen 3.5 0.8B	0.651	0.351	0.87B	1.75 GB
NuExtract-2.0-2B	0.643	0.393	2.2B	4.4 GB
GLiNER2-multi	0.585	0.374	307M	309 MB int8*
GLiNER2-base	0.524	0.368	205M	834 MB
NuExtract-tiny-v1.5	0.334	0.222	0.5B	954 MB
FunctionGemma 270M (tool calling)	0.288	0.181	0.27B	0.54 GB

*GLiNER2-multi int8 size verified by us with the same quantize-and-rebench methodology as our own int8 claim (accuracy held within 0.004 of fp32).

Per slice

Model	Short	Long doc	Long + distractors	Register	Calendar	Factored	Nested
Gemma 4 26B A4B	0.844	0.844	0.693	0.815	0.829	0.954	1.000
Claude Haiku 4.5	0.838	0.759	0.762	0.831	0.802	0.817	1.000
Qwen 3.5 9B	0.828	0.807	0.717	0.791	0.774	0.891	1.000
Schemer	0.759	0.708	0.685	0.829	0.946	0.946	0.988
Schemer (int4)	0.754	0.689	0.671	0.820	0.943	0.967	0.985
Ministral 3 8B	0.818	0.811	0.711	0.720	0.796	0.790	0.998
Gemma 4 E2B	0.809	0.785	0.705	0.773	0.719	0.759	1.000
Qwen 3.5 0.8B	0.721	0.635	0.560	0.581	0.529	0.689	0.936
NuExtract-2.0-2B	0.763	0.704	0.542	0.648	0.496	0.805	0.000†
GLiNER2-multi	0.683	0.650	0.388	0.662	0.468	0.783	0.000†
GLiNER2-base	0.619	0.587	0.371	0.612	0.362	0.668	0.000†
NuExtract-tiny-v1.5	0.406	0.277	0.248	0.280	0.275	0.417	0.439
FunctionGemma 270M	0.339	0.254	0.232	0.274	0.299	0.421	0.000†

†The NuExtract-2.0, GLiNER2, and FunctionGemma interfaces cannot express arrays of objects; their nested cells score the resulting empty output. This is an interface limit, not an extraction failure.

Per field type

Bold marks the best score in each row. Schemer's architecture shows plainly: it owns the typed/absence rows and trails larger extraction LLMs on free-text rows.

Type	Schemer	Schemer (int4)	NuExtract-2.0-2B (2.2B)	Qwen 3.5 0.8B
boolean	0.911	0.908	0.393	0.351
datetime	0.769	0.752	0.641	0.652
array	0.766	0.750	0.731	0.686
number	0.750	0.743	0.670	0.679
string	0.648	0.637	0.774	0.716
label	0.629	0.609	0.636	0.616

Per language

Measured on the three slices that cover all 13 languages with identical composition (short records, long documents, long documents with distractors; ~600 records per language).

Language	Schemer	Qwen 3.5 0.8B	GLiNER2-multi
French	0.745	0.649	0.606
Spanish	0.734	0.662	0.610
Italian	0.731	0.653	0.603
Portuguese	0.729	0.640	0.601
German	0.728	0.635	0.591
Dutch	0.724	0.621	0.600
Danish	0.723	0.618	0.594
English	0.717	0.706	0.632
Norwegian	0.717	0.620	0.600
Swedish	0.715	0.633	0.624
Japanese	0.707	0.614	0.384
Chinese	0.683	0.640	0.408
Polish	0.670	0.609	0.597

Schemer scores 0.800 overall; every model above it runs 9.1B to 26.6B parameters or an API. NuExtract-2.0-2B, the closest task-specific extractor, scores 0.643 at ten times the parameters. Schemer wins all 13 languages against both the strongest sub-1B LLM and GLiNER2-multi, posts the top calendar score of any model at any size (0.946 next to the 26B's 0.829), and is the only model with reliable absence detection (0.911 next to a 0.18 to 0.43 field). Long documents: 0.708 at ~1,100 tokens; 0.685 when the document contains same-type distractor records.

Limitations

Nested schemas extract via the harness (segment and recurse), not the model itself: quality on messy real-world prose is not yet benched externally; the 0.99 figure is our internal template-based eval.
Extractive strings only: Schemer will not compose or rewrite text; abstractive titles from messy prose trail extraction LLMs (string 0.641 vs 0.774 for NuExtract-2.0-2B and ~0.86 for the 8B+ class). It does factor titles extractively (purpose clause, people and times split into their fields), but the title is always a substring of the input.
Judgment labels requiring world knowledge (severity triage) trail the 8B+ class (0.629 vs ~0.82).
Not a NER model: it fills your fields; it does not enumerate every entity mention (CrossNER span-F1 0.24 vs GLiNER2's 0.59).
English is the weakest of the 13 languages relative to competitors.

Citation

@software{schemer_2026,
  title  = {Schemer: on-device text to structured JSON},
  author = {Desert Ant Labs},
  year   = {2026},
  url    = {https://huggingface.co/desert-ant-labs/schemer}
}

License

Desert Ant Labs Source-Available License, Version 1.0. Free below 100,000 monthly active devices per platform, for each model; above that a commercial license is required (licensing@desertant.ai). You may embed the model in your application; you may not use it, its outputs, or its logs to train a competing on-device model. Credit Desert Ant Labs in your app: https://license.desertant.ai/attribution.

Third-party upstream components retain their original licenses; see THIRD_PARTY_NOTICES.md.

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