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StandardThinking
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Model Details
Model Provider
StandardThinking
Model Tree
StandardThinking/StandardOne-8B
Input Modalities
Output Modalities
Supported Functionality
Standard One reads one answer letter per option, so a question is limited by the label alphabet and every question in a request needs its own pass over the scenario. The schema head instead attends over the hidden states of the whole request (the final layer and one intermediate layer), places every question and option in one joint graph, and returns a calibrated distribution for each question at once. In practice:
| Benchmark | SH v3 | SH v4 (this release) |
|---|---|---|
| Decision Index 0.3, public index | 48.34 | 57.40 |
| Areas: Knowledge / Language / Retrieval / Tools / Arts | 32.1 / 51.0 / 65.8 / 60.9 / 25.5 | 36.9 / 57.9 / 66.4 / 84.0 / 42.5 |
| HoVer, multi-hop claim verification (DI) | 72.6 | 72.8 |
| BRIGHT, reasoning-heavy retrieval over long documents, ~12k tokens per request (DI) | 34.8 | 34.2 |
| Decision hidden in a long document (our set, 150 items) | 87.33 | 88.00 |
| JevBench public: easy / original / hard (48 / 72 / 111 items) | 100.00 / 95.83 / 74.77 | 100.00 / 94.44 / 75.68 |
Decision Index rows (DI) are chance-corrected skill scores (0 = random guessing, 100 = perfect) from our own runs of the public 0.3 suite with the official kit, all 140,178 scoreable requests; every benchmark of both runs is in standardone-8b-sh-v3 and standardone-8b-sh-v4. For some DI benchmarks the public train split or the benchmark's public generator was part of training (HoVer among them); evaluated items never were. The other rows are accuracy in percent, measured with the Standard One engine on identical items.
Decision Index 0.3 public index, board data of 2026-10-12; Standard One self-scored with the official kit (see the footnote of each chart). The same charts with notes: Standard One SH on the Decision Index.
v2 if those held-out suites matter most to you.The included chat_template.jinja has an empty default system message, and the reference server renders requests
without one (SH_SYSTEM_PROMPT=none, the default in code/render.py). Do not add the Ministral default system
prompt: the head was trained without it, and adding it changes the hidden states it reads.
bash
python -m pip install --upgrade huggingface_hubhf download StandardThinking/StandardOne-8B-SH --local-dir StandardOne-8B-SHpython -m pip install -r StandardOne-8B-SH/code/requirements.txt python StandardOne-8B-SH/code/serve_head.py \ --snapshot StandardOne-8B-SH --head StandardOne-8B-SH/head --port 30171bash
curl -s localhost:30171/v1/systemone -H 'content-type: application/json' -d '{ "state": {"message": "Please close my card, I lost it yesterday."}, "questions": { "intent": {"type": "choice", "instructions": "What does the customer want?", "criteria": {"cancel": "close the card", "limit": "change the limit", "other": null}}, "urgent": {"type": "noul", "instructions": "The request is urgent."}, "mood": {"type": "score", "instructions": "How upset is the customer?", "criteria": ["calm", "annoyed", "angry"]} }}'The server batches concurrent requests (--max-batch-requests, --max-batch-tokens) and binds 127.0.0.1 by
default. It runs on one GPU with the backbone in bfloat16 and the head in float32. On Blackwell GPUs (B200/B300)
install a CUDA 13 build of PyTorch first (pip install torch --index-url https://download.pytorch.org/whl/cu130); the
CUDA 12.8 build lacks kernels for those GPUs.
The reference server above is plain PyTorch. For production throughput and latency, serve this repository with the Standard One engine, our SGLang fork with a prefill-only decision mode and the schema-head readout (standard-one-sglang, standard-one-adapter, Apache-2.0). It is the engine we run in production. Build the image once (it replaces only the Python package of a stock SGLang image):
bash
mkdir ctx && cd ctxgit clone https://github.com/standardthinkingai/standard-one-sglang sglang-jevgit clone https://github.com/standardthinkingai/standard-one-adapter jev-adaptermkdir -p jit-cache/aiter jit-cache/root-cachedocker build -f sglang-jev/examples/runtime/jev/rocm/Dockerfile.decision \ --build-arg BASE_IMAGE=lmsysorg/sglang:nightly-dev-cu13-20260929-79cafec0 -t standard-one-engine:cu13 .Then serve the downloaded repository as it is (weights at the top level, head in head/):
bash
docker run -d --gpus all --network host --ipc host --shm-size 32g \ -v $PWD/StandardOne-8B-SH:/models/m:ro standard-one-engine:cu13 python3 -m sglang.launch_server \ --model-path /models/m --decision-readout-path /models/m/head --served-model-name standardthinking/standard-one-8b-sh \ --host 127.0.0.1 --port 30000 --tp-size 1 --model-impl sglang --dtype bfloat16 --attention-backend triton \ --context-length 32768 --max-running-requests 64 --mem-fraction-static 0.3 \ --chunked-prefill-size -1 --model-config-parser hf --load-format safetensors \ --disable-radix-cache --disable-decode-cuda-graph --disable-prefill-cuda-graph --decision-deadline-seconds 110It answers POST /v1/systemone on port 30000. Unlike the reference server, the engine requires the model field
(the served model name), as in the System One API:
bash
curl -s localhost:30000/v1/systemone -H 'content-type: application/json' -d '{ "model": "standardthinking/standard-one-8b-sh", "state": {"message": "Please close my card, I lost it yesterday."}, "questions": {"urgent": {"type": "noul", "instructions": "The request is urgent."}}}'Checked on one NVIDIA B300 against the reference server on 1,431 evaluation questions: 99.58 % the same answer, all
231 JevBench public answers identical. On AMD MI350X/MI355X build with
BASE_IMAGE=lmsysorg/sglang-rocm:v0.5.20-rocm720-mi35x-20260929 and run with
--device /dev/kfd --device /dev/dri --group-add video -e SGLANG_USE_AITER=0 instead of --gpus all.
For CPU or Apple-silicon use, follow the download, build and server instructions in StandardOne-8B-SH-GGUF. The SH head reads hidden states from the backbone, so it requires the supplied state extractor and Python runner; stock llama-server -hf alone does not execute the schema head. Once the runner is listening on port 30171, use the requests below unchanged.
The following invented records illustrate four integrations: receiving inventory, diagnosing an import failure, evaluating an inspection note, and checking backup evidence. They are example inputs, not benchmark results. Start either the reference server or the SH GGUF runner on port 30171 before sending them.
An inventory application can ask several questions about one receiving note. Here, the model compares the recorded quantities and checks whether the paperwork supports closing the receipt.
bash
curl --fail-with-body http://127.0.0.1:30171/v1/systemone \ -H "Content-Type: application/json" --data-binary @- <<'JSON'{ "state": "Purchase order R-417 lists 48 cable reels. The receiving scan recorded 46 reels. Two pallet tags cannot be read, and the receiver has not signed the count sheet.", "questions": { "receipt_action": { "type": "choice", "instructions": "Select the next inventory step supported by the receiving note.", "criteria": { "close_receipt": "The delivered quantity is reconciled and the count sheet is signed.", "reconcile_count": "The recorded quantity differs from the order or pallet identification is unresolved.", "await_delivery": "The shipment has not reached the receiving area." } }, "quantities_match": { "type": "noul", "instructions": "Do the receiving scan and purchase order report the same number of reels?" }, "record_completeness": { "type": "score", "instructions": "Evaluate how complete the receiving evidence is.", "criteria": [ "No quantity or identification evidence is available.", "Some evidence is recorded, but discrepancies or missing sign-off remain.", "Quantities and identifiers are reconciled and sign-off is recorded." ] } }}JSONThis record describes an unresolved count, unequal quantities and partial paperwork. Use those facts to inspect the returned answers. A choice question compares the descriptions you provide, a noul question returns a yes/no probability, and a score question returns an expected position on its ordered, zero-based scale. The score can lie between two levels.
Use this small standard-library client for the remaining examples. It displays the response as received so that you can examine the selected values and probability estimates.
python
import jsonfrom urllib.request import Request, urlopen def evaluate_record(record, questions): payload = json.dumps({"state": record, "questions": questions}).encode("utf-8") request = Request( "http://127.0.0.1:30171/v1/systemone", data=payload, headers={"Content-Type": "application/json"}, method="POST", ) with urlopen(request, timeout=300) as response: print(json.dumps(json.load(response), indent=2))An import monitor can classify a diagnostic record before selecting a repair playbook. This input contains rows, but the expected timestamp column is absent.
python
evaluate_record( "Import job J-82 read 1,200 CSV rows. Required column event_time was not found. The file contains a column named timestamp_ms. UTF-8 decoding succeeded.", {"failure_kind": { "type": "choice", "instructions": "Identify the import failure described in this diagnostic record.", "criteria": { "column_mismatch": "The available column names differ from the required schema.", "empty_input": "The file contains no data rows.", "text_encoding": "The importer cannot decode the file contents.", "unresolved": "The record does not establish a failure category." } }})A maintenance log can map a written observation onto a defined condition scale. The criterion here is the gauge marking recorded by the inspector.
python
evaluate_record( "Inspection of ventilation unit V-12: the filter pressure gauge is above the marked replacement line. The fan guard is secured and the housing has no visible cracks.", {"filter_condition": { "type": "score", "instructions": "Assess the filter condition using the reported gauge position.", "criteria": [ "The gauge is inside the normal operating band.", "The gauge is above normal but below the replacement line.", "The gauge has reached or exceeded the replacement line." ] }})A backup catalog can check whether a verification record satisfies a supplied rule. This example requires both a matching checksum and a successful restore check.
python
evaluate_record( "Archive B-206 was written to storage. Verification reported a checksum mismatch. A restore check has not been attempted.", {"backup_verified": { "type": "noul", "instructions": "Does this record satisfy the rule that a verified backup must have a matching checksum AND a successful restore check?" }})These calls only request decisions; the surrounding application decides what to do with them. Measure accuracy and choose probability thresholds using records from your own workflow. All examples send text, which is the validated input path for these releases.
| Path | Contents |
|---|---|
model-0000{1..4}-of-00004.safetensors, config.json, tokenizer files | merged backbone, bfloat16 (Mistral3ForConditionalGeneration) |
chat_template.jinja | chat template with an empty default system message |
head/ | schema_head.safetensors (float32) and schema_head_config.json (taps: final layer and layer 25) |
code/ | reference server: request rendering, head, scoring |
Apache-2.0. Built on Standard One 8B (Apache-2.0), which is built on Ministral 3 8B (Apache-2.0).
/v1/systemoneSHA256SUMS, release-manifest.json | checksums; source and merge details |