StandardThinking/StandardOne-3B

Standard One 3B

Updated weights (v2, 2026-09-26). If you downloaded this model before, download it again or pin revision="v2". Earlier versions stay available under the tags v1 and v1.1.

Version: v2

Standard One scores a bounded set of answers for a supplied scenario and returns probabilities through POST /v1/systemone. It does not generate free-form response text. This repository contains the merged BF16 3B checkpoint; the server code is in StandardOne-8B.

If you need	Repository
Merged 3B checkpoint	StandardOne-3B (this repository)
3B adapter weights and merge recipe	StandardOne-3B-LoRA
Larger merged checkpoint and server code	StandardOne-8B
8B adapter weights and merge recipe	StandardOne-8B-LoRA

In the reported served evaluations, 3B has a lower median latency on the measured short-request profile; 8B scores higher on the public standard and hard tiers. See Benchmarks for the measurement conditions and limitations.

The figure combines results from different measurement paths. See Benchmarks for served versus offline conditions; measured 24–26 September 2026.

At a glance

• Send a state and a bounded rubric to receive probabilities for the supplied labels: choice selects among labeled options, noul is yes/no, and score uses an ordinal scale. The endpoint scores the labels in one forward pass without decoding answer text.
• In the same-run offline comparison with its untuned base, 3B improves on all six suites. These are not served-endpoint results.
• Probabilities are temperature-scaled and calibration-checked (hard-tier ECE, distribution total-variation) — see Benchmarks below.
• The shared training mixture covers English, Japanese, Chinese, Spanish, French, German, Portuguese, Russian, and a smaller Korean share. See the nine-language MASSIVE intent results in docs/public-classification-suites.md; performance varies by language. The retained Pixtral vision tower accepts image data URLs; no separate image-input decision benchmark is reported.
• Apache-2.0: base model, adapter, merged weights, and shared server code hosted in the 8B repository.

Quantized versions

Format	Repository
FP8 (compressed-tensors, validated with SGLang)	StandardOne-3B-FP8
GGUF for llama.cpp (BF16, Q8_0 down to IQ2_M, vision projector)	StandardOne-3B-GGUF

Validation numbers are in each repository's card.

Quick start

Follow the 3B setup in StandardOne-8B's QUICKSTART.md first: on a CUDA-capable Linux host, clone both checkpoints' repositories, then create the SGLang and adapter virtual environments with uv. The server code (jev-adapter) is in StandardOne-8B/server/, not this repository. The commands below assume the working directory and installations from that guide. Run the engine and adapter in separate terminals.

Engine (stock SGLang 0.5.20):

CUDA_VISIBLE_DEVICES=0 SGLANG_VLM_CACHE_SIZE_MB=0 .venv-sglang/bin/python -m sglang.launch_server \
  --model-path ./StandardOne-3B --served-model-name standard-one-3b \
  --host 127.0.0.1 --port 30000 --tp-size 1 --model-impl sglang --dtype bfloat16 \
  --context-length 8192 --max-running-requests 32 --mem-fraction-static 0.8 \
  --chunked-prefill-size -1 --disable-radix-cache --mm-preprocess-cache-size-mb 0 \
  --model-config-parser hf --load-format safetensors

Adapter:

.venv-native/bin/jev-adapter --engine-url http://127.0.0.1:30000 --model standard-one-3b --alias jev-latest \
  --host 0.0.0.0 --port 30120 --max-concurrency 1 \
  --tokenizer-model mistralai/Ministral-3-3B-Instruct-2512-BF16 \
  --tokenizer-revision b6d637bef2393152b3da2b2fde72eecdee30557e \
  --prompt-wording native --native-system-prompt none --default-temperature 0.95 \
  --temperature-by-type choice=0.95,noul=1.05,score=0.85

Prompt wording

jev-adapter can phrase a request in two ways. Both were measured on the same served endpoint (no system prompt) and each has its own fitted temperatures; the recommended default is native unless served scores at least 1.0 percentage point higher on the suites below and an offline check agrees.

--prompt-wording	What the prompt looks like	Temperatures (default; choice / noul / score)	Mean accuracy, 10 suites
native (recommended default)	State: / Question: / Options: headers, options as A. name: description	0.95; 0.95 / 1.05 / 0.85	69.9 %
served	Context: / Question: / Options: headers, options as A: name: description	0.90; 0.90 / 1.05 / 0.95	69.8 %

The 10 suites: judge proxy, hard proxy, stated-distribution probability, realistic transfer set, MuSiQue (multiple choice), SQuAD 2.0 unanswerable questions, ContractNLI, PAWS-X (English), a held-out hard decision set and a consistency set. None of them is a JevBench tier, and no JevBench item was used to choose the wording or the temperatures. To use served, pass --prompt-wording served --default-temperature 0.90 --temperature-by-type choice=0.90,noul=1.05,score=0.95.

Try it:

curl -s http://127.0.0.1:30120/v1/systemone -X POST -H 'content-type: application/json' -d '{
  "model": "jev-latest",
  "state": "Policy: refunds require a receipt and purchase within 30 days. A customer bought 12 days ago but has no receipt. Issue a refund.",
  "questions": {
    "decision": {
      "type": "noul",
      "instructions": "Under the stated policy, is the requested action permitted? Treat unproved required conditions as not satisfied.",
      "criteria": {"true": "Every required condition is established and no prohibition applies.", "false": "A condition is missing or a prohibition applies."}
    }
  }
}'

Response shape (example values, default temperature applied):

{
  "model": "standard-one-3b",
  "answers": {"decision": {"type": "noul", "noul": 0.09}},
  "usage": {"input_tokens": 96, "output_tokens": 0},
  "metadata": {
    "confidence_method": "1 - normalized_entropy",
    "temperature": 0.95,
    "temperature_by_type": {"choice": 0.95, "noul": 1.05, "score": 0.85},
    "evaluations": 1,
    "adapter_elapsed_ms": 22.4
  }
}

More (client command, 8B variant, request format): see StandardOne-8B's QUICKSTART.md.

Benchmarks

Served endpoint results (the release configuration). Merged BF16 weights through SGLang 0.5.20 and jev-adapter, native wording, no system prompt, one option order, per-answer-type temperatures (choice 0.95, noul 1.05, score 0.85). The wording and the temperatures were chosen on non-JevBench data. Jev 1.13 was measured on the same items through its hosted endpoint; its probabilities are raw, with no temperature applied. These are our measurements, not official sealed-set JevBench scores.

Suite	Standard One 3B	Jev 1.13
JevBench public easy (48)	100.00 %	100.00 %
JevBench public standard (72)	88.89 %	98.61 %
JevBench public hard (111)	46.85 %	72.07 %
judge proxy (600: routing + answer adequacy)	88.17 %	90.50 %
realistic transfer set (600)	90.67 %	86.67 %
stated-distribution probability (1,036)	78.76 %	72.97 %
hard proxy (600)	44.67 %	54.83 %

Offline comparison with the untuned base. This separate transformers runner used native wording, no system prompt, one option order, T=1. The base and tuned checkpoint were scored by the same offline path; these numbers are indicative of the base-model change, not the served scores above.

Suite	Untuned base	Standard One 3B
JevBench public easy (48)	97.92 %	100.00 %
JevBench public standard (72)	70.83 %	93.06 %
JevBench public hard (111)	47.75 %	49.55 %
judge proxy (600: routing + answer adequacy)	63.17 %	88.00 %
realistic transfer set (600)	62.50 %	88.50 %
stated-distribution probability (1,036)	31.56 %	79.25 %

Against the untuned base, all six suites improve on this offline run. Served and offline probabilities differ even on identical prompts, so use the served table for expected endpoint behavior. Hard-tier ECE at the served temperatures is 0.225 against Jev 1.13's 0.099 raw, and mean TV to the stated distributions is 0.117 at served T against Jev's 0.192 raw; full calibration table: docs/BENCHMARKS.md.

Speed — raw serial latency on one H200 with SGLang 0.5.20, using a 242-decision profile averaging about 280 input tokens per decision. The 22.6 ms figure is p50 for this profile, not a latency guarantee for other request lengths, concurrency or hardware. Qwen checkpoints are untuned and shown for speed only; no accuracy comparison is implied.

Model	p50	p95	Input tokens/decision
Standard One 3B	22.6 ms	33.2 ms	≈280
Standard One 8B	25.8 ms	41.9 ms	≈280
Qwen3-8B (untuned)	28.5 ms	57.2 ms	278
Qwen3.5-4B (untuned)	48.8 ms	72.7 ms	283

Throughput has been measured for the 8B only (see docs/BENCHMARKS.md); no equivalent sweep has been run for the 3B.

On the public classification and decision suites (400 cases/suite, seed 13, served endpoints): AG News 84.2 %, typed decisions 67.8 %, MASSIVE intent mean 82.9 %, email spam 93.2 %, phishing 89.5 %. Full table, per-language and per-workflow breakdown: docs/BENCHMARKS.md and docs/public-classification-suites.md.

A JevBench v1.4.1 run has been requested; the sealed-set result is not yet available. Full report: docs/BENCHMARKS.md.

Model details

• Base model: mistralai/Ministral-3-3B-Instruct-2512-BF16, revision b6d637bef2393152b3da2b2fde72eecdee30557e (Apache-2.0).
• Adapter: LoRA r=16, α=32, dropout 0, on q_proj k_proj v_proj o_proj gate_proj up_proj down_proj of the language-model projections only (vision tower and multimodal projector excluded), 24,707,072 trainable parameters, PEFT 0.21.0. Adapter file adapter_model.safetensors, 135,113,048 bytes, sha256 a8e3eb341e27c1a49a282e327c2a2038906abb0eee771e80debbb4a4c45f7afc.
• Merged BF16 checkpoint: merging the adapter into the base changed 182 tensors, none of them outside the language-model projections, maximum absolute weight change 0.0023.
• Serving details: native chat-template wording, served without a system prompt, at fixed per-answer-type temperatures (choice 0.95, noul 1.05, score 0.85; fitted on held-out and public-train calibration data, no JevBench item); served model name standard-one-3b behind stock SGLang 0.5.20 via jev-adapter (POST /v1/systemone); single, caller-supplied option order, no rotation ensemble; 8,192-token context.

Path	Contents
*.safetensors	Merged BF16 checkpoint (base + LoRA)
config.json, tokenizer*, chat_template*, preprocessor*	Base model's non-weight files
docs/, docs/public-classification-suites.md	Benchmark report and figures (shared with StandardOne-8B); per-language/per-workflow numbers
SHA256SUMS, release-manifest.json, MERGE_REPORT.json, evidence/, LICENSE, README.md	File hashes, training manifest, merge report, supporting artifacts, licence, this card

The server code and full quick-start guide live in StandardThinking/StandardOne-8B's server/ directory and QUICKSTART.md, and are reused for this checkpoint — see Quick start above for the 3B launch command.

Training data

Trains on the same data sources as StandardThinking/StandardOne-8B, not a reduced subset. Training data is synthetic and format-augmented decision data plus decision items converted from public datasets (listed below); the JevBench public tiers used only for evaluation carry MIT. Full per-cohort breakdown: docs/BENCHMARKS.md.

Public datasets used (train splits where the dataset has one; licence as stated by each dataset; labels come from the datasets, distractor options are generated by code):

Dataset	Licence
SQuAD 2.0	CC BY-SA 4.0
ARC	CC BY-SA 4.0
BoolQ	CC BY-SA 3.0
CommonsenseQA	MIT
HellaSwag	MIT
Banking77	CC BY 4.0
Bias in Bios	MIT
Bitext customer support	CDLA-Sharing-1.0
CLINC150	CC BY 3.0
Amazon Counterfactual	CC BY 4.0
DBpedia-14	CC BY-SA 3.0
Dolly 15k	CC BY-SA 3.0
GoEmotions	Apache-2.0
MASSIVE	CC BY 4.0
Twitter Financial News Sentiment	MIT
HelpSteer3	CC BY 4.0
HelpSteer2	CC BY 4.0
2WikiMultihopQA	Apache-2.0
HotpotQA	CC BY-SA 4.0
MuSiQue	CC BY 4.0
QASC	CC BY 4.0
DROP	CC BY-SA 4.0
GSM8K	MIT
TempReason	CC BY-SA 3.0
MultiNLI	OANC / CC BY-SA 3.0 / CC BY 3.0
PAWS	Google terms, free for any purpose
PAWS-X	Google terms, free for any purpose
SNLI	CC BY-SA 4.0
WANLI	CC BY 4.0
ContractNLI	CC BY 4.0
CUAD	CC BY 4.0
ShARC	CC BY-SA 3.0
Jailbreak classification	Apache-2.0
Prompt injections	Apache-2.0
Aegis AI Content Safety 2.0	CC BY 4.0
Jigsaw Toxic Comment Classification (mirror of the Kaggle data)	CC0 (data); comment text CC BY-SA 3.0 (Wikipedia)
Measuring Hate Speech	CC BY 4.0
Image safety classes	MIT

Upstream ids and the cohort each one feeds: docs/BENCHMARKS.md.

An exact-text overlap audit against the public JevBench tiers found 0 exact scenario matches and 181 exact instruction matches — rows in two adequacy-rubric cohorts whose entire instruction field, a generic 58-character adequacy question, is byte-identical to one public hard-tier instruction (0.03 % of the 520,754-row training mixture). These rows are kept and disclosed here rather than regenerated, since the overlap is limited to one rubric question's wording and never touches a scenario or an answer.

Limitations

• Public hard tier: the served 3B score is 46.85 %, versus 72.07 % for Jev 1.13. In the separate offline base comparison, Standard One 3B scores 49.55 %, 1.80 percentage points above the untuned base's 47.75 %.
• Served probabilities are temperature-scaled by one value per answer type; if you apply this model to a materially different question distribution, re-fitting that temperature is advisable rather than assuming these values transfer.
• At most 26 options per question (one uppercase letter per option, A–Z).
• The sealed JevBench set has not been measured for this model.
• Served and offline probabilities can differ on identical prompts (mean total-variation ≈0.08 on the hard tier); served numbers are treated as authoritative.
• Korean is a small share of multilingual training alongside English.
• The card reports text benchmarks; it does not establish decision accuracy on image inputs.
• Ten-way support triage (42 %) and RAG passage relevance (57 %) are weak zero-shot; fine-tune for those.

Licence

Adapter weights, merged weights and this card: Apache-2.0. Base model mistralai/Ministral-3-3B-Instruct-2512 (and -BF16): Apache-2.0 per its Hugging Face model card, which adds that the model must not be used in a way that infringes, misappropriates, or otherwise violates any third party's rights. jev-adapter and SGLang: Apache-2.0. The JevBench harness and public tiers used for evaluation: MIT; other benchmark items keep their own upstream terms.

Citation

StandardThinking/StandardOne-3B (merged weights) · StandardThinking/StandardOne-3B-LoRA (adapter + merge recipe).