docs: download snapshot before installing its requirements

README.md

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 ---
 
 
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 # Standard One 3B SH
 
 
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 > **Updated weights (v3, 2026-10-13).** v3 is trained further on decision data, mostly our own synthetic items. Decision Index 0.3 public index 51.53 (self-scored with the official kit; v2 42.90).
 > Earlier releases stay available under the tags `v2` and `v1`.
 
 
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 **Version:** v3
 
 
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 **Standard One 3B SH** is Standard One 3B with a **joint schema head** (SH). It reads the scenario once and
 scores every option of every question in a single forward pass, then returns probabilities through the same
 `POST /v1/systemone` contract as Standard One. It does not generate text.
 
 
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 | | |
 |---|---|
 | Backbone | Standard One 3B v2.2 (`mistral3`, Ministral 3 3B text + Pixtral vision tower), fine-tuned for the head and merged; weights in this repository |
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 | License | Apache-2.0 |
 
 
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 ## Why a schema head
 
 
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 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:
 
 
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 - **Many options.** Intent and routing questions with 100–255 options keep nearly the accuracy of small option sets.
 - **Many questions per scenario.** All questions of a request share one pass over the scenario.
 - **Long scenarios.** Decisions buried in long documents are found far more reliably than with Standard One v2.2.
 
 
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 ## Results
 
 
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 | Benchmark | SH v2 | **SH v3 (this release)** |
 |---|---:|---:|
 | Decision Index 0.3, public index | 42.90 | **51.53** |
@@ -70,6 +90,8 @@ graph, and returns a calibrated distribution for each question at once. In pract
 | JevBench public: easy / original / hard (48 / 72 / 111 items) | 100.00 / 98.61 / 63.96 | 100.00 / 98.61 / 63.96 |
 
 
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 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-3b-sh-v2](https://huggingface.co/datasets/StandardThinking/StandardOne-SH-decision-index/tree/main/runs/standardone-3b-sh-v2)
 and [standardone-3b-sh-v3](https://huggingface.co/datasets/StandardThinking/StandardOne-SH-decision-index/tree/main/runs/standardone-3b-sh-v3). For some DI benchmarks the public train split or the benchmark's public
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 on identical items: the v2 column with the reference server in `code/`, the v3 column with the Standard One engine.
 
 
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 ## Benchmark charts
 
 
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 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](https://standardthinking.ai/resources/guides/standard-one-sh-decision-index/).
 
 
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 ![Public index against model size](https://huggingface.co/StandardThinking/StandardOne-3B-SH/resolve/main/assets/charts/01-index-vs-size.png)
 
 
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 ![Retrieval & Classification](https://huggingface.co/StandardThinking/StandardOne-3B-SH/resolve/main/assets/charts/02-retrieval-classification.png)
 
 
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 ![Tools & Automation](https://huggingface.co/StandardThinking/StandardOne-3B-SH/resolve/main/assets/charts/03-tools-automation.png)
 
 
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 ![Product search relevance (Amazon ESCI)](https://huggingface.co/StandardThinking/StandardOne-3B-SH/resolve/main/assets/charts/04-product-search.png)
 
 
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 ![Multi-hop claim verification (HoVer)](https://huggingface.co/StandardThinking/StandardOne-3B-SH/resolve/main/assets/charts/05-claim-verification.png)
 
 
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 ![Smart-home request handling (Home appliance simulator)](https://huggingface.co/StandardThinking/StandardOne-3B-SH/resolve/main/assets/charts/06-home-appliance.png)
 
 
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 ![Contract understanding (ContractNLI)](https://huggingface.co/StandardThinking/StandardOne-3B-SH/resolve/main/assets/charts/07-contract-nli.png)
 
 
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 ![Reading chords from a score (POP909-CL)](https://huggingface.co/StandardThinking/StandardOne-3B-SH/resolve/main/assets/charts/08-chords.png)
 
 
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 ## Changes in v3
 
 
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 - **Further training on synthetic decision data.** v3 continues from v2 on decision items built for this round, mostly our own
   synthetic items (code-checked, LLM-written in varied styles), with replay of earlier data. See "Results" for the
   Decision Index 0.3 and the long-context checks.
 
 
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 ## Changes in v2
 
 
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 - **Hard decision tasks.** v2 is fine-tuned on about 2,200 new decision items written in the families where v1 was
   weakest: long policies with exceptions and overrides, multi-criteria trade-offs, judging a response against a
   rubric, and date and number reasoning. The items were authored by large language models, every gold answer was
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   label sets are within one point of v1. Keep `v1` if policy-judgement repair matters most to you.
 
 
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 ## Prompt format
 
 
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 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.
 
 
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 ## Quick start
 
 
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 ```bash
-pip install -r code/requirements.txt   # torch, transformers, safetensors
+python -m pip install --upgrade huggingface_hub
 hf download StandardThinking/StandardOne-3B-SH --local-dir StandardOne-3B-SH
+python -m pip install -r StandardOne-3B-SH/code/requirements.txt
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 python StandardOne-3B-SH/code/serve_head.py \
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 ```
 
 
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 ```bash
 curl -s localhost:30171/v1/systemone -H 'content-type: application/json' -d '{
   "state": {"message": "Please close my card, I lost it yesterday."},
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 ```
 
 
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 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`).
@@ -174,18 +239,27 @@ For CPU or Apple-silicon use, see [StandardOne-3B-SH-GGUF](https://huggingface.c
 
 
 
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 ## Run locally with llama.cpp
 
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 For CPU or Apple-silicon use, follow the download, build and server instructions in [StandardOne-3B-SH-GGUF](https://huggingface.co/StandardThinking/StandardOne-3B-SH-GGUF#quick-start-with-llamacpp). 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.
 
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 ## Practical examples
 
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 Use the running server at `http://127.0.0.1:30171/v1/systemone`. SH scores all questions jointly without generating text. These examples are illustrative workloads, not measured accuracy results; inspect the actual response on your chosen model and quantization.
 
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 ### Customer support: route, handoff and priority
 
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 Run this in a second terminal after starting the server:
 
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 ```bash
 curl --fail-with-body http://127.0.0.1:30171/v1/systemone \
   -H "Content-Type: application/json" --data-binary @- <<'JSON'
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 JSON
 ```
 
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 The intended routing label here is `billing`, with a human handoff. This describes the task, not a promised model output. `choice` selects from supplied categories; `noul` scores a yes/no question; `score` uses an ordered scale and returns an expected zero-based level that can be fractional.
 
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 ### Ticket routing, incident severity, agent completion and document routing
 
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 This Python client uses only the standard library and prints the complete server response. The requests work with the BF16 reference server and the SH GGUF runner on port 30171.
 
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 ```python
 import json
 from urllib.request import Request, urlopen
 
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 def decide(state, questions):
     request = Request(
         "http://127.0.0.1:30171/v1/systemone",
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     with urlopen(request, timeout=300) as response:
         return json.load(response)
 
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 # Route a ticket to one queue from your own taxonomy.
 print(json.dumps(decide(
     "I cannot sign in after resetting my password. The reset link has expired.",
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                             "other": "None of these queues"}}}
 ), indent=2))
 
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 # Score an ordered incident severity scale.
 print(json.dumps(decide(
     "Production checkout is unavailable for all customers. The incident is ongoing.",
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                                "Critical outage affecting all customers"]}}
 ), indent=2))
 
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 # Check an observed result against the original goal.
 print(json.dumps(decide(
     "Goal: send the weekly report to the team. Observed state: draft saved; the message has not been sent.",
     {"finished": {"type": "noul", "instructions": "Has the agent completed the goal?"}}
 ), indent=2))
 
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 # Classify a document before selecting an extraction workflow.
 print(json.dumps(decide(
     "INVOICE 2048. Amount due: USD 240. Payment due within 30 days.",
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 ), indent=2))
 ```
 
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 Each `choice` question selects one category. For independent tags, submit one `noul` question per tag in the same request. Choose probability thresholds on representative validation data before automating routing or handoff. These examples use text input, the validated path for this release.
 
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 ## Files
 
 
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 | Path | Contents |
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 | `model-0000{1,2}-of-00002.safetensors`, `config.json`, tokenizer files | merged backbone, bfloat16 (`Mistral3ForConditionalGeneration`) |
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 | `SHA256SUMS`, `release-manifest.json` | checksums; source and merge details |
 
 
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 ## Limitations
 
 
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 - Requests longer than 32,768 tokens are refused, not truncated.
 - On our hard held-out decision suite (600 items; long policies, multi-step lookups, date and number reasoning)
   it scores 41.67 against 44.67 for Standard One 3B v2.2. Prefer Standard One 3B for that kind of workload.
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 - Probabilities are calibrated on our held-out data; refit thresholds on your own distribution before relying on them.
 
 
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 ## License
 
 
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 Apache-2.0. Built on [Standard One 3B](https://huggingface.co/StandardThinking/StandardOne-3B) (Apache-2.0), which is
 built on [Ministral 3 3B](https://huggingface.co/mistralai/Ministral-3-3B-Instruct-2512) (Apache-2.0).
 
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