OpenSML-JSONLite-175M-Instruct

Author: William Zebrowski · 175,611,648 parameters

Run locally · Technical report · Results · Provenance · Inference verification

A small language model that generates JSON directly from source text and a supplied JSON Schema. It produces the entire object in one native autoregressive request. There is no fieldwise JSON assembler, output repair or constrained decoder in this release. Schema validation happens after generation and cannot establish that extracted values are correct.

This update replaces the earlier extraction-plus-assembly weights and runner with a native source/schema → JSON fine-tune. The pretrained ancestor is OpenSML-Base-175M; the model adds evidence QA, exact field extraction, focused extraction and a final directly supervised JSON stage. Full lineage and exact artifact hashes are in the report.

Run locally

The repository contains BF16 model.safetensors, tokenizer/configuration, native MLX source archive, runnable entry point, examples and verification/results records. This is a custom architecture; use the supplied runner rather than Transformers AutoModel or mlx-lm auto-loading. Verified platform: Apple Silicon macOS, Python 3.11 and stock MLX 0.32.3.

python3 -m venv .venv
source .venv/bin/activate
python -m pip install huggingface_hub
# Authenticate if access requires it: hf auth login
hf download OpenSML/OpenSML-JSONLite-175M --local-dir OpenSML-JSONLite-175M
cd OpenSML-JSONLite-175M
python -m pip install -r requirements.txt
python inference.py --example 29
python inference.py --text-file request.txt --schema-file schema.json
# Optional: raw text plus validation/stop metadata and example correctness
python inference.py --example 29 --json

Default stdout is the model's unmodified generated text. The fixed extraction instruction and native prompt template are supplied by the runner. Example answers are checked afterward and never passed to generation. After downloading and installing dependencies, inference runs offline. The root runner verifies the source archive and selected weight SHA-256. Tokenizer companions are in the bundle; optimizer state is not needed for inference.

Model and inputs

Setting	Value
Parameters	175,611,648
Layers / hidden / FFN width	24 / 768 / 2,048
Attention	GQA, 12 query heads / 4 KV heads
Normalization / MLP / positions	RMSNorm + Q/K norm / SwiGLU / RoPE 10,000
Vocabulary / context	Frozen 32,000-token byte-BPE / 2,048 shared tokens
Embeddings / biases	Tied input-output / no linear biases
Weights	BF16, unquantized safetensors
Output	One native JSON object per request
Default decoding	Temperature 0, repetition penalty 1, max 256 output tokens

Supply source text containing the facts and a closed flat object schema with 1–8 required properties and additionalProperties: false. Supported property types are strings, nullable strings (["string","null"]) and arrays of strings. Clear field descriptions help specify semantics. Missing arrays target []; missing nullable strings target null. Strings preserve source case/spelling and arrays preserve source order; number/date normalization is not trained. Overlong inputs reject without truncation.

The run is trained primarily on seven SNIPS domains and 39 field definitions. New names/descriptions are passed to the model directly, but reliable unfamiliar-domain extraction has not been established. Nested schemas, numeric/boolean outputs, enums, references and advanced constraints are outside the verified runner contract.

Verified interaction examples

These four examples passed the playground reference check with the selected native JSON weights. Their raw outputs also matched the standalone MLX runner. They demonstrate observed successes, not a guarantee on other inputs. Decode greedily: temperature 0, repetition penalty 1, at most 256 output tokens.

Instruction and input format

Use this exact instruction for every example (the provided runner inserts it automatically):

Extract information from the supplied source_text according to the supplied JSON Schema. Return exactly one JSON object, with no explanation or Markdown. Copy values exactly, preserving case and spelling; do not normalize dates or numbers. Include every required key and no other keys. For string arrays, include all matching source spans in source order, or [] when absent. For nullable strings, use the exact source span or null when absent.

The user message is that instruction, a blank line, then the compact JSON object {"source_text": <query>, "schema": <schema>}. Both inputs are provided to one model generation; no expected answer is included. The runner applies the native SFT role tokens and validates the result afterward.

Example 1: Track and artist

Query / source text

Play the track Blue Lantern by Mira Sol.

JSON Schema

{
  "type": "object",
  "properties": {
    "track": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "track name"
    },
    "artist": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "performer or musical artist"
    },
    "album": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "album name"
    }
  },
  "required": [
    "track",
    "artist",
    "album"
  ],
  "additionalProperties": false
}

Actual model output

{"album":[],"artist":["Mira Sol"],"track":["Blue Lantern"]}

Run the packaged example: python inference.py --example 29.

Example 2: Restaurant booking

Query / source text

Book River Moon in Denver for four people tomorrow.

JSON Schema

{
  "type": "object",
  "properties": {
    "restaurant_name": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "restaurant name"
    },
    "city": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "city or town"
    },
    "party_size_number": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "explicit party size"
    },
    "timeRange": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "date, time, or time-range expression"
    }
  },
  "required": [
    "restaurant_name",
    "city",
    "party_size_number",
    "timeRange"
  ],
  "additionalProperties": false
}

Actual model output

{"restaurant_name":["River Moon"],"city":["Denver"],"party_size_number":["four"],"timeRange":["tomorrow"]}

Run the packaged example: python inference.py --example 30.

Example 3: Missing fields as empty arrays

Query / source text

Please play some music.

JSON Schema

{
  "type": "object",
  "properties": {
    "country": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "country"
    },
    "timeRange": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "date, time, or time-range expression"
    },
    "artist": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "performer or musical artist"
    }
  },
  "required": [
    "country",
    "timeRange",
    "artist"
  ],
  "additionalProperties": false
}

Actual model output

{"country":[],"timeRange":[],"artist":[]}

Run the packaged example: python inference.py --example 40.

Example 4: Nullable strings and requested scope

Query / source text

Play the last theme by Behzad Mirkhani.

JSON Schema

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "type": "object",
  "properties": {
    "service": {
      "type": [
        "string",
        "null"
      ],
      "description": "music service"
    },
    "music_item": {
      "type": [
        "string",
        "null"
      ],
      "description": "word specifying the requested music category, such as artist, track, song, or album"
    },
    "playlist": {
      "type": [
        "string",
        "null"
      ],
      "description": "playlist name"
    }
  },
  "required": [
    "service",
    "music_item",
    "playlist"
  ],
  "additionalProperties": false
}

Actual model output

{"playlist":null,"music_item":"theme","service":null}

Run the packaged example: python inference.py --example 48.

The performer is present in the source but is not requested by this schema. The output includes only the requested music category and missing service/playlist values.

Training

The 175.6M-parameter base was pretrained from random initialization on 17,300,111,360 lifetime token positions, using a five-Mac Apple Silicon cluster with Thunderbolt/RDMA.

Pretraining source	Intended sampling share
FineWeb-Edu	50%
DCLM-Edu	18%
English FineWiki	13%
Cosmopedia v2	9%
Educational Python	10%

These are configured token-sampling weights, not unique-document counts. Selected-base validation loss was 2.5563359552. Pretraining architecture, tokenizer, source revisions, filtering, scheduler and precision are documented in the report.

Training run

Training loss and held-out validation during pretraining. Left: raw training loss and exponential moving average. Right: held-out validation loss.

Supervised adaptation

Stage	What it teaches	Inherited training
Grounded QA	Answers from evidence, absence, contrasting questions, multipart/corrections	15,008 presentations through selected update 469
Requested-field extraction	Exact SNIPS annotated spans, field definitions, missing values	12,115 requests / 379 updates
Focused extraction	Single-field supervision plus labeled replay	5,120 presentations / 160 updates
Native JSON SFT	Source text + full schema → JSON assistant targets	15,000 presentations / 469 updates

The final JSON stage has 12,115 distinct source requests: 12,115 canonical-schema examples, 1,443 renamed-key views and 1,442 nullable-string views. Additional views reuse TRAIN-only sources. Original SNIPS chunk annotations are reconstructed, checked against source offsets and schema-validated; upstream annotation fidelity is not a guarantee of perfect semantic golds.

The final stage runs one full-weight pass on Mac1 with batch 32, microbatch 2, fresh AdamW, peak LR 5e-6/final 1e-6, 20-update warmup then cosine decay, betas (0.9,0.95), epsilon 1e-8, weight decay 0.01 and clipping 1. Only assistant JSON and stop tokens contribute to the loss. It processes 4,634,126 serialized positions; there is no RL or adapter-only training in this chain.

DEV selection happens at updates 0/100/200/300/469 on 252 fixed requests. The final artifact is selected before the 1,444-request test. TRAIN/DEV/TEST source groups are separate, but these held-out panels have prior research exposure and are not a new blind benchmark.

Native JSON evaluation

Metric	Adapted SNIPS held-out, 1,444 requests
Entire request correct	1,191/1,444 — 82.48%
Field accuracy	91.31%
Extracted-value precision / recall / F1	90.55% / 90.41% / 90.48%
Missing-field accuracy	92.46%
Schema-valid JSON	99.79%
False abstentions / unsupported values	111 / 30
Token-limited outputs	0

Canonical string-array schemas alone score 1,156/1,388 (83.29%), with 100% schema validity. Extra renamed-key and nullable-string views score 19/28 and 16/28. The full 54-case local playground scores 42/54 (77.78%), including 7/12 previously authored natural examples. These are different panels; do not pool or present them as an external leaderboard.

All outputs are raw, greedy native model generations: one whole-object request, no constrained decoder, assembly, repair, prompt truncation or reference answer in inputs. Complete correctness requires natural completion, strict JSON/schema validity and exact values. Structural validity is not factual correctness. Results and the report contain counts, per-domain performance, protocols and failures.

Limitations

The model remains a focused extraction research candidate. Failures include inventing a city, omitting second values, merging two names, missing rating/cuisine information, confusing entity types, incomplete spans, duplicate keys and malformed nullable strings. Broader natural language, corrections, associations, unfamiliar definitions, long documents and complex schemas need further validation. General dialogue, coding, multilingual quality, confidence calibration and prompt-injection robustness are not established. success means natural completion and structure validity, not verified factuality or release readiness.

Identity and packaging

Selected weight SHA-256:

656f45015aab548465d93bed0b57e687497163741885da7364872851fa17e9a4

Weights have 351,242,342 bytes. The frozen tokenizer is unchanged. Clean stock-MLX packaging verification checks raw output text and stop reasons against all 54 actual playground predictions; its 42/54 correctness is the same observed result, not an independent blind test or full-heldout runtime-parity claim. CUDA, CPU-only, quantized and converted exports remain unverified.

Source code uses Apache-2.0; upstream data/dependencies retain their terms. The source license does not automatically assign a model-weight license. See the included notices and provenance.