---
base_model: mistralai/Ministral-3-3B-Instruct-2512-BF16
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
language:
  - en
  - ja
  - zh
  - es
  - fr
  - de
  - pt
  - ru
  - ko
tags:
  - mistral3
  - decision-model
  - typed-decisions
  - jev
  - jevbench
  - calibration
  - decode-free
  - multilingual
  - vision-language
---

# Standard One 3B

> **Updated weights (v2.1, 2026-09-27).** If you downloaded this model before, download it again.
> Earlier releases remain available under the `v1`, `v1.1` and `v2` tags.

**Version:** v2.1

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](https://huggingface.co/StandardThinking/StandardOne-8B).

Image input is supported; the results below measure text decisions.

## At a glance

- `choice` selects among labeled options, `noul` is yes/no and `score` uses an ordinal scale.
- The training mixture covers English, Japanese, Chinese, Spanish, French, German, Portuguese, Russian and a smaller Korean share. Performance varies by language.
- Probabilities are fitted by answer type; the measured endpoint configuration and calibration results are below.
- The base, adapter, merged weights and shared server code are Apache-2.0.

## Repositories

- [Merged BF16 checkpoint](https://huggingface.co/StandardThinking/StandardOne-3B)
- [LoRA adapter and merge recipe](https://huggingface.co/StandardThinking/StandardOne-3B-LoRA)
- [GGUF quantizations](https://huggingface.co/StandardThinking/StandardOne-3B-GGUF) (v2.1 when published)
- [Shared server code](https://huggingface.co/StandardThinking/StandardOne-8B/tree/main/server)

## Quick start

Use this merged checkpoint. Follow the [server setup](https://huggingface.co/StandardThinking/StandardOne-8B/blob/main/QUICKSTART.md) on a CUDA-capable Linux host to install SGLang 0.5.20 and the adapter in separate virtual environments. Start the engine and the adapter in separate terminals. Use the 3B commands below; the linked guide also covers another model size.

Engine:

```bash
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:

```bash
.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 0.90 --temperature-by-type choice=0.90,noul=1.15,score=1.20
```

The endpoint accepts `choice` (labeled options), `noul` (yes/no) and `score` (ordinal) questions. It returns probabilities for the caller's labels in one forward pass without decoding answer text. For example:

```bash
curl -s http://127.0.0.1:30120/v1/systemone -X POST -H 'content-type: application/json' -d '{
  "model": "jev-latest",
  "state": "Refunds require a receipt. The customer has no receipt.",
  "questions": {"decision": {"type": "noul",
    "instructions": "Is a refund allowed under the rule?",
    "criteria": {"true": "The requirement is met.",
                 "false": "A requirement is missing."}}}
}'
```

`usage.output_tokens` is 0. The server applies the temperatures shown above unless a request supplies `options.temperature`. Each probability vector is normalized over exactly the caller's labels.

## Prompt wording and temperatures

Both wordings were evaluated through the same served endpoint. The recommended wording follows the preset rule: use `native` unless `served` leads by at least 1.0 percentage point across the 10 suites and an offline check agrees. Temperatures were fitted separately for each wording and answer type on three calibration suites only. No JevBench item was used for these decisions.

| Wording | Default temperature | Choice | Yes/no | Ordinal | Mean accuracy, 10 suites |
|---|---:|---:|---:|---:|---:|
| `native` (recommended) | 0.90 | 0.90 | 1.15 | 1.20 | 69.91 % |
| `served` | 1.00 | 1.00 | 0.90 | 0.80 | 70.21 % |

For the alternate wording, use `--prompt-wording served --default-temperature 1.00 --temperature-by-type choice=1.00,noul=0.90,score=0.80`.

## Benchmarks

### Served endpoint, v2.1

Merged BF16 weights through SGLang 0.5.20 and `jev-adapter`, one option order, `native` wording, no system prompt. Accuracy is independent of the fitted temperature.

| Suite | Questions | Accuracy |
|---|---:|---:|
| Judge proxy | 600 | 87.50 % |
| Hard proxy | 600 | 44.17 % |
| Stated-distribution probability | 1,036 | 78.38 % |
| Realistic transfer | 600 | 90.17 % |
| MuSiQue multiple choice | 500 | 69.80 % |
| SQuAD 2.0 unanswerable | 500 | 79.80 % |
| ContractNLI | 500 | 62.20 % |
| PAWS-X English | 500 | 74.40 % |
| Hard decisions | 800 | 51.50 % |
| Consistency | 1,200 | 61.17 % |

The following public tiers are reported separately and were excluded from wording, temperature, checkpoint and quantization decisions. These are our measurements, not official sealed-set scores.

| Suite | Questions | Accuracy |
|---|---:|---:|
| JevBench public easy | 48 | 100.00 % |
| JevBench public standard | 72 | 86.11 % |
| JevBench public hard | 111 | 47.75 % |

At the fitted temperatures, public-hard 10-bin ECE is 0.212 and mean total-variation distance on the stated-distribution validation set is 0.124.

### Offline six-suite result, v2.1

This separate `transformers` run used native wording, no system prompt and T=1. It is not the served endpoint result. The three public JevBench tiers below are report-only and were excluded from wording, temperature, checkpoint and quantization decisions.

| Suite | Questions | Accuracy |
|---|---:|---:|
| JevBench public easy | 48 | 100.00 % |
| JevBench public standard | 72 | 93.06 % |
| JevBench public hard | 111 | 47.75 % |
| Judge proxy | 600 | 88.00 % |
| Realistic transfer | 600 | 89.33 % |
| Stated-distribution probability | 1,036 | 77.99 % |

### Public classification and decision suites, v2.1

Served endpoint, 400 cases per suite, seed 13: AG News 84.5 %, typed decisions 67.3 %, nine-language MASSIVE intent mean 83.4 %, email spam 92.0 %, phishing 88.8 %. Results vary by language and task.

The full report in [`docs/BENCHMARKS.md`](docs/BENCHMARKS.md) and the classification tables in [`docs/public-classification-suites.md`](docs/public-classification-suites.md) are historical; use the v2.1 results above for this checkpoint. The [historical v2 benchmark chart](docs/assets/00-benchmark-card.png) shows earlier-version measurements.

## Model details

- **Base model:** `mistralai/Ministral-3-3B-Instruct-2512-BF16`, revision `b6d637bef2393152b3da2b2fde72eecdee30557e` (Apache-2.0).
- **Adapter architecture:** LoRA r=16, α=32, dropout 0 on `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj` and `down_proj` of the language model.
- **Adapter file:** `adapter_model.safetensors`, `135,113,048` bytes, SHA-256 `e903d55404c9a2311b7188ca2f71e7dc6333247c20662606168dc4b61075d93b`.
- **Merge:** 182 language-model tensors changed, none outside the language-model projections; maximum absolute weight change 0.0023.
- **Serving:** `native` wording, no system prompt, temperatures (choice 0.90, yes/no 1.15, ordinal 1.20), SGLang 0.5.20 and `jev-adapter`; 8,192-token context, one caller-supplied option order and no rotation ensemble.

## Training data

The public source inventory shared with Standard One 8B is listed below. 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. The source inventory below is retained from the previous card; the linked full report is historical.

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 |

Historical upstream-id mapping: [`docs/BENCHMARKS.md`](docs/BENCHMARKS.md#training-data-provenance).

## Limitations

- Results depend on the task distribution and the serving path. Refit temperatures when using a materially different distribution.
- At most 26 options per question (one uppercase letter per option, `A`–`Z`).
- Served and offline probabilities can differ on identical prompts. Use the served results to estimate endpoint behavior.
- Korean is a small share of the multilingual training mixture.
- The card reports text benchmarks; it does not establish accuracy on image inputs.
- The sealed JevBench set has not been measured for this version.

## 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).