Publish Apex model card with GLiClass 4 attribution and Apache 2.0 license

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README.md

@@ -1,601 +1,79 @@
 ---
 language:
 - en
+license: apache-2.0
 library_name: gliclass
 pipeline_tag: zero-shot-classification
+base_model: knowledgator/gliclass-v4-edge
+model_name: Apex-flash
 tags:
+- apex
 - gliclass
-- deberta
+- gliclass-v4
 - zero-shot-classification
 - text-classification
 - multi-label-classification
 - instruction-following
 - reranking
-- guardrails
-- content-moderation
-- knowledge-graph-reasoning
-license: apache-2.0
-base_model: microsoft/deberta-v3-large
 ---
-# GLiClass v4 Edge
+# Apex-flash
 
-**Instruction-following zero-shot classification in a 0.4B encoder, built for CPUs and edge devices.**
+**GLiClass 4 based decision model from the SCX Apex family.**
 
-`knowledgator/gliclass-v4-edge` is the 0.4B-parameter edge release of
-[GLiClass v4](https://github.com/Knowledgator/GLiClass). The labels you pass and an
-optional instruction tell it what to decide at inference time. It returns a score for
-every label in one forward pass and generates no text. It reaches a Decision Index public skill of 47.5 at a median
-latency of 11 ms per decision, ahead of every model up to 1B parameters on the public board (the best of them scores 36.9).
+A compact 0.4B text decision model for classification, routing, scoring, and high-volume applications on CPU or GPU. Supply the input, candidate labels, and an optional instruction to receive label scores without generating text.
 
+This release republishes [Knowledgator's gliclass-v4-edge](https://huggingface.co/knowledgator/gliclass-v4-edge) under the Apex name. The weights, configuration, tokenizer, and other inference assets are unchanged; SCX has simplified the model card and added the license and upstream documentation. No additional fine-tuning is claimed.
 
-## Usage
+| Property | Value |
+| --- | --- |
+| Parameters | Approximately 0.4B |
+| Inputs | Text |
+| Backbone | [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) |
+| Framework | [GLiClass](https://github.com/Knowledgator/GLiClass) |
+| License | Apache 2.0 |
 
-```bash
-pip install gliclass -U
-```
+## Quick start
 
-Or install from source in a Python 3.10+ environment:
+Use Python 3.10+ and an up-to-date GLiClass installation:
 
 ```bash
-git clone https://github.com/Knowledgator/GLiClass.git
-cd GLiClass
-pip install -e .
+pip install -U gliclass
 ```
 
-The following examples reuse `pipeline`:
+The example runs on a CUDA GPU when available and otherwise uses the CPU.
 
 ```python
 import torch
 from gliclass import GLiClassModel, ZeroShotClassificationPipeline
 
-model_id = "knowledgator/gliclass-v4-edge"
+model_id = "scx-admin/Apex-flash"
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
-
 model = GLiClassModel.from_pretrained(model_id)
 pipeline = ZeroShotClassificationPipeline(
     model,
-    model_id,  # loads the tokenizer
+    model_id,
     classification_type="multi-label",
     max_length=2048,
     device=device,
 )
-```
-
-For a local copy, replace the model ID with the checkpoint directory.
-
-### Multi-label classification
 
-```python
 results = pipeline(
-    "The battery lasts two days, but the screen scratches easily.",
-    ["battery", "display", "durability", "price", "shipping"],
+    "My parcel has not arrived. Please check the delivery status.",
+    ["delivery issue", "refund request", "technical support"],
     threshold=0.5,
 )[0]
 for result in results:
-    print(result["label"], round(result["score"], 3))
-```
-
-Each prediction contains `label` and `score`. Pass a list of texts and `batch_size=8` for batched inference.
-
-Scores are calibrated on held-out data; predictions also carry `initial_score`, the uncalibrated score.
-
-### Single-label decisions and scoring
-
-Use `classification_type="single-label"` to pick one answer: scores are a softmax over the labels.
-To score an input, use the levels of a scale as labels and read their probabilities:
-
-```python
-answer = pipeline(
-    "Our refund window is 30 days. The customer bought the item 45 days ago.",
-    ["refund approved", "refund denied", "escalate to a human"],
-    prompt="Apply the refund policy to the customer's request.",
-    classification_type="single-label",
-)[0]
-print(answer)
-
-quality = pipeline(
-    "Q: What causes tides? A: Mostly the Moon's gravity, with a smaller effect from the Sun.",
-    ["1 - wrong", "2 - poor", "3 - partly correct", "4 - good", "5 - excellent"],
-    prompt="Rate the factual quality of the answer.",
-    classification_type="single-label",
-    return_hierarchical=True,
-)[0]
-print(quality)  # every level with its probability
-```
-
-### Instruction following
-
-Use `prompt` for the instruction, policy, or rubric. Labels are interpreted in light of it:
-
-```python
-policy = (
-    "Flag a message only if it shares another person's contact details without consent. "
-    "Public business contact pages are allowed."
-)
-results = pipeline(
-    [
-        "You can reach our support team at help@acme.com.",
-        "Here is my neighbour's personal phone number: 555-0134, call her at night.",
-    ],
-    ["violates policy", "complies with policy"],
-    prompt=policy,
-    classification_type="single-label",
-)
-```
-
-Few-shot examples (`examples=[{"text": ..., "labels": [...]}]`) and hierarchical labels
-(`{"sentiment": ["positive", "negative"], "topic": ["product", "service"]}`) are also supported.
-
-### Knowledge graph reasoning
-
-Pass the graph as text next to the story, the question as the instruction, and the answers as labels:
-
-```python
-graph = (
-    "Alice -member_of-> Finance team\n"
-    "Finance team -can_read-> Reports folder\n"
-    "Reports folder -contains-> Salaries.xlsx\n"
-    "Alice -explicit_deny-> Salaries.xlsx\n"
-    "Rule: explicit restrictions override team permissions."
-)
-answer = pipeline(
-    graph,
-    ["yes", "no"],
-    prompt="Can Alice open Salaries.xlsx?",
-    classification_type="single-label",
-)[0]
-print(answer)  # [{'label': 'no', 'score': ...}]
-```
-
-The same pattern covers multi-hop questions, agent and web-browsing steps (which link, tool, or action
-comes next), and NLI (`entailment`, `neutral`, `contradiction`).
-
-### Reranking
-
-```python
-query = "How do I reset a forgotten router password?"
-passages = [
-    "Hold the reset button for 10 seconds to restore factory settings, then log in with the default password.",
-    "Our routers support Wi-Fi 7 and dual-band operation.",
-    "To change your Wi-Fi name, open the admin panel and edit the SSID field.",
-]
-scores = pipeline(
-    passages,
-    [query],
-    prompt="Does the passage answer the query?",
-    threshold=0.0,
-)
-ranked = sorted(zip(passages, scores), key=lambda p: -p[1][0]["score"])
+    print(result["label"], result["score"])
 ```
 
-## Serving a Jev-style decision API
-
-Decision engines such as Jev take a **state** (text or JSON) and a set of typed **questions**, and return a
-probability for every option of every question, with no generated text. GLiClass answers the same requests
-directly: each question is one GLiClass pass in which the state is the text, the question's `instructions`
-are the prompt, and every option is a label. This is the setup used for the Decision Index results below.
-
-| Question type | Request fields | GLiClass labels | Answer |
-| --- | --- | --- | --- |
-| `choice` | `instructions`, `criteria: {key: description}` | the option descriptions (the key when a description is missing) | `choice`, `confidence`, `probabilities` per key |
-| `noul` (yes / no) | `instructions`, optional `criteria: {"true": ..., "false": ...}` | the true and false descriptions (`yes` / `no` when missing) | `noul` = P(true) |
-| `score` | `instructions`, `criteria: [level 0, level 1, ...]` | the level descriptions, in order | `score` = expected level, `confidence`, `probabilities` per level |
-
-Probabilities are a softmax over the option logits. A JSON state is rendered as `key: value` lines, with string lists as `- item` lines.
-
-The server below exposes `POST /v1/systemone` and `GET /v1/models`, the request and response shape of
-TypeSafe's System One API, so clients written for it (for example the
-[WorkflowEvals](https://github.com/typesafe-ai/WorkflowEvals) runner) can point at a local GLiClass model.
-It needs only `gliclass`, `fastapi`, and `uvicorn`:
-
-```python
-# jev_api.py
-import json
-import os
-
-import torch
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-
-from gliclass import GLiClassModel, ZeroShotClassificationPipeline
-
-MODEL_ID = os.environ.get("MODEL", "knowledgator/gliclass-v4-edge")
-MAX_LENGTH = int(os.environ.get("MAX_LENGTH", "16384"))
-DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
-DTYPE = torch.bfloat16 if DEVICE != "cpu" else torch.float32
-
-model = GLiClassModel.from_pretrained(MODEL_ID, torch_dtype=DTYPE).to(DEVICE).eval()
-pipe = ZeroShotClassificationPipeline(model, MODEL_ID, max_length=10**9, classification_type="single-label",
-                                      device=DEVICE, progress_bar=False).pipe
-app = FastAPI(title="GLiClass System One API")
-
-
-class Request(BaseModel):
-    state: str | dict | list | None = None
-    questions: dict[str, dict]
-
-
-def as_text(value):
-    return value if isinstance(value, str) else json.dumps(value, ensure_ascii=False)
-
-
-def render_state(state):
-    if state in (None, "", {}, []):
-        return ""
-    if isinstance(state, str):
-        return state
-    if isinstance(state, list):
-        return "\n".join(f"- {as_text(item)}" for item in state)
-    lines = []
-    for key, value in state.items():
-        if isinstance(value, list) and all(isinstance(item, str) for item in value):
-            lines += [f"{key}:"] + [f"- {item}" for item in value]
-        else:
-            lines.append(f"{key}: {as_text(value)}")
-    return "\n".join(lines)
-
-
-def options(question):
-    kind, criteria = question.get("type"), question.get("criteria")
-    if kind == "choice":
-        if not criteria or len(criteria) < 2:
-            raise HTTPException(422, "a choice question needs at least two criteria")
-        return list(criteria), [as_text(v) if v is not None else k for k, v in criteria.items()]
-    if kind == "noul":
-        c = criteria or {}
-        return ["true", "false"], [as_text(c.get("true") or "yes"), as_text(c.get("false") or "no")]
-    if kind == "score":
-        if not criteria or len(criteria) < 2:
-            raise HTTPException(422, "a score question needs at least two levels")
-        return [str(i) for i in range(len(criteria))], [as_text(c) for c in criteria]
-    raise HTTPException(422, f"unsupported question type {kind!r}")
-
-
-@torch.no_grad()
-def probabilities(text, prompt, labels):
-    inputs = pipe.prepare_inputs([text], labels, same_labels=True, prompt=prompt)
-    length = inputs["input_ids"].shape[-1]
-    if length > MAX_LENGTH:  # refuse instead of truncating
-        raise HTTPException(422, f"input of {length} tokens exceeds the {MAX_LENGTH}-token limit")
-    logits = model(**inputs, max_num_classes=len(labels)).logits[0, : len(labels)].float()
-    return torch.softmax(logits, dim=-1).tolist(), length
-
-
-@app.get("/v1/models")
-def models():
-    return {"models": [{"name": MODEL_ID, "description": "GLiClass"}]}
-
-
-@app.post("/v1/systemone")
-def systemone(request: Request):
-    text, answers, tokens = render_state(request.state), {}, 0
-    for name, question in request.questions.items():
-        keys, labels = options(question)
-        prompt = as_text(question.get("instructions") or "") or None
-        probs, length = probabilities(text, prompt, labels)
-        tokens += length
-        p = dict(zip(keys, probs))
-        best = max(keys, key=p.get)
-        if question["type"] == "choice":
-            answers[name] = {"type": "choice", "choice": best, "confidence": p[best], "probabilities": p}
-        elif question["type"] == "noul":
-            answers[name] = {"type": "noul", "noul": p["true"]}
-        else:
-            answers[name] = {"type": "score", "score": sum(int(k) * v for k, v in p.items()),
-                             "confidence": p[best], "probabilities": p}
-    return {"model": MODEL_ID, "answers": answers, "usage": {"input_tokens": tokens, "output_tokens": 0}}
-```
-
-```bash
-pip install gliclass fastapi uvicorn
-uvicorn jev_api:app --host 0.0.0.0 --port 8765
-```
-
-```bash
-curl -s localhost:8765/v1/systemone -H 'Content-Type: application/json' -d '{
-  "state": {"ticket": "Checkout errors hit 12% after the 14:05 deploy.",
-            "facts": ["Rolling back takes two minutes.", "No incident is open yet."]},
-  "questions": {
-    "action": {"type": "choice", "instructions": "What should the on-call engineer do right now?",
-               "criteria": {"rollback": "Roll back to the last healthy build.",
-                            "wait": "Watch error rates for another hour.",
-                            "escalate": "Page the payments team lead."}},
-    "urgent": {"type": "noul", "instructions": "Does this need action within the next hour?",
-               "criteria": {"true": "Yes, customers are affected right now.", "false": "No, it can wait."}},
-    "severity": {"type": "score", "instructions": "Rate the incident severity.",
-                 "criteria": ["minor", "moderate", "major", "critical"]}
-  }}'
-```
-
-The response has one answer per question. This one comes from gliclass-v4-edge (rounded):
-
-```json
-{
-  "model": "knowledgator/gliclass-v4-edge",
-  "answers": {
-    "action": {"type": "choice", "choice": "rollback", "confidence": 0.938, "probabilities": {"rollback": 0.938, "wait": 0.059, "escalate": 0.003}},
-    "urgent": {"type": "noul", "noul": 0.300},
-    "severity": {"type": "score", "score": 0.24, "confidence": 0.832, "probabilities": {"0": 0.832, "1": 0.124, "2": 0.013, "3": 0.031}}
-  },
-  "usage": {"input_tokens": 172, "output_tokens": 0}
-}
-```
-
-Here the edge model picks the rollback but rates the incident as minor and not urgent; check judgment-style
-questions such as severity on your own data before relying on them.
-
-- **Instructions matter.** Put the decision rule or rubric in `instructions` and give `noul` questions explicit `true` / `false` descriptions; bare yes / no labels are less reliable.
-- **Throughput.** The server answers questions one at a time. For batch jobs, group questions that share a state with `pipe.prepare_inputs([text] * n, labels_per_question, same_labels=False, prompt=prompts)` and run one forward pass.
-- **Long inputs** over `MAX_LENGTH` tokens are refused with HTTP 422, never truncated, as the Decision Index rules require.
-- **Calibration.** For calibrated checkpoints `logits` are the calibrated logits; `uncalibrated_logits` holds the raw ones.
-- The Decision Index results below come from a kit engine (`gliclass_engine.GLiClassEngine`) that applies exactly this mapping, run with `python -m decision_index run --engine gliclass_engine:GLiClassEngine --model knowledgator/gliclass-v4-edge`.
-
-## Reported evaluation results
-
-Columns **Edge 0.4B**, **9B**, and **27B** are [gliclass-v4-edge](https://huggingface.co/knowledgator/gliclass-v4-edge),
-[gliclass-v4-9b](https://huggingface.co/knowledgator/gliclass-v4-9b), and
-[gliclass-v4-27b](https://huggingface.co/knowledgator/gliclass-v4-27b); this card is **Edge 0.4B**.
-
-### Decision Index 0.3
-
-Public part of the [Decision Index](https://github.com/apolinario/decision-index) 0.3 (37 benchmarks in five
-areas, 140,178 requests), scored with the kit's own scorers. Skill is chance-corrected (0 = random guessing,
-100 = perfect) and coverage-adjusted (unanswered = wrong); the public index weighs the areas 25.8 / 25.8 / 20.0 /
-18.3 / 10.0%.
-
-GLiClass models: one pass per question through the Jev-style mapping above, on one RTX PRO 6000
-(this model: bf16, `max_length=16384`, batch size 8). Edge 0.4B, 9B, and 27B take a median of 11.0, 34.0, and 73.7 ms per request. Other models: the per-benchmark public results in the kit's board snapshot, with area scores recomputed using the kit's aggregation, which reproduces each published index
-exactly. The main board ranking (the Full score) also includes private tests; this table covers the public
-part only.
-
-| Model | Public index (skill) | Raw index | Breadth | Knowledge | Language | Retrieval | Tools | Arts |
-| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| **Edge 0.4B** | 47.49 | 59.70 | 44.54 | 21.4 | 54.6 | 66.7 | 58.1 | 38.6 |
-| **9B** | 55.56 | 66.10 | 53.99 | 36.6 | 61.6 | 61.6 | 74.8 | 41.5 |
-| **27B** | 61.74 | 70.76 | 60.66 | 45.7 | 67.6 | 63.0 | 81.0 | 50.3 |
-| Torchcast 27B | 65.10 | 73.64 | 63.86 | 46.7 | 72.0 | 67.5 | 84.6 | 54.4 |
-| pplx-decider 27B | 62.25 | 71.66 | 61.52 | 52.0 | 67.5 | 61.3 | 78.9 | 47.0 |
-| clef | 61.71 | 71.07 | 61.04 | 52.8 | 61.3 | 62.8 | 80.7 | 48.9 |
-| GLiDE 28B | 59.06 | 69.61 | 57.96 | 45.8 | 59.7 | 58.4 | 83.6 | 48.2 |
-| Jev 1.13 | 57.96 | 68.55 | 57.27 | 53.9 | 59.2 | 55.4 | 75.1 | 39.1 |
-| Kev 27B | 56.69 | 67.55 | 55.91 | 45.7 | 62.6 | 55.7 | 72.6 | 42.7 |
-
-Every benchmark, as skill (★ = gold benchmark, weight 1.2 inside its area):
-
-<details>
-<summary>Per-benchmark Decision Index results</summary>
-
-#### Knowledge & Reasoning
-
-| Benchmark | **Edge 0.4B** | **9B** | **27B** | Torchcast 27B | pplx-decider 27B | clef | GLiDE 28B | Jev 1.13 | Kev 27B |
-| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| GPQA Diamond ★ | 10.2 | 25.2 | 26.5 | 35.4 | 41.5 | 31.3 | 36.0 | 71.4 | 29.9 |
-| GSM8K | 2.1 | 42.3 | 60.1 | 58.1 | 72.0 | 52.1 | 61.2 | 66.2 | 65.5 |
-| ChessBench | 10.7 | 10.4 | 13.7 | 10.8 | 10.3 | 17.8 | 9.9 | 9.8 | 11.0 |
-| MuSR | 34.7 | 38.9 | 49.7 | 48.0 | 53.5 | 74.2 | 44.8 | 46.1 | 43.8 |
-| SATA-Bench | 24.5 | 26.0 | 34.4 | 13.9 | 27.2 | 32.6 | 28.6 | 25.4 | 26.9 |
-| CRUXEval | 0.0 | 28.7 | 57.4 | 66.0 | 77.2 | 78.6 | 57.4 | 57.1 | 63.8 |
-| CLadder | 42.2 | 54.4 | 63.8 | 49.0 | 58.6 | 88.2 | 54.9 | 45.3 | 48.3 |
-| HLE ★ | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.7 | 0.0 |
-| MMLU-Pro ★ | 20.5 | 46.4 | 57.5 | 66.7 | 67.4 | 61.7 | 66.9 | 80.5 | 63.2 |
-| BBH fixed-option tasks ★ | 28.1 | 55.8 | 66.9 | 72.0 | 76.8 | 61.8 | 68.6 | 89.7 | 70.8 |
-| WinoGrande ★ | 60.4 | 71.4 | 73.6 | 87.7 | 84.8 | 87.1 | 71.9 | 83.9 | 76.6 |
-
-#### Language Understanding
-
-| Benchmark | **Edge 0.4B** | **9B** | **27B** | Torchcast 27B | pplx-decider 27B | clef | GLiDE 28B | Jev 1.13 | Kev 27B |
-| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| ContractNLI | 69.6 | 77.0 | 76.8 | 80.4 | 68.7 | 73.0 | 69.5 | 59.1 | 68.0 |
-| ANLI ★ | 42.6 | 52.8 | 62.5 | 68.2 | 61.5 | 55.0 | 60.0 | 62.2 | 56.5 |
-| HellaSwag ★ | 77.1 | 89.8 | 93.6 | 96.8 | 91.0 | 97.6 | 93.4 | 92.7 | 92.5 |
-| ACOS | 13.3 | 28.2 | 21.3 | 55.9 | 25.9 | 31.0 | 22.1 | 27.3 | 20.8 |
-| FinEntity | 86.4 | 83.3 | 92.2 | 90.0 | 93.3 | 94.2 | 88.3 | 80.8 | 91.2 |
-| iSarcasmEval | 37.1 | 38.9 | 54.5 | 35.8 | 62.7 | 31.0 | 22.2 | 36.3 | 50.2 |
-| VAST | 59.6 | 61.7 | 66.5 | 72.9 | 65.1 | 39.0 | 54.4 | 46.9 | 52.4 |
-| NLI4CT | 45.2 | 56.9 | 71.0 | 73.2 | 70.6 | 66.9 | 69.7 | 69.0 | 67.2 |
-| RAGTruth response-level hallucination | 58.6 | 62.2 | 65.8 | 71.0 | 65.0 | 57.6 | 51.4 | 51.3 | 59.8 |
-
-#### Retrieval & Classification
-
-| Benchmark | **Edge 0.4B** | **9B** | **27B** | Torchcast 27B | pplx-decider 27B | clef | GLiDE 28B | Jev 1.13 | Kev 27B |
-| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| BANKING77 ★ | 90.7 | 80.1 | 80.0 | 91.0 | 80.3 | 94.0 | 79.4 | 79.5 | 85.9 |
-| CLINC150+OOS ★ | 92.2 | 85.0 | 88.9 | 93.4 | 91.5 | 97.4 | 87.3 | 89.2 | 86.5 |
-| BRIGHT ★ | 22.4 | 31.5 | 33.3 | 44.3 | 44.4 | 40.6 | 43.6 | 40.6 | 41.7 |
-| Amazon ESCI | 39.3 | 45.4 | 50.3 | 51.9 | 50.3 | 46.5 | 47.9 | 43.8 | 46.5 |
-| PhishNChips phishing decisions | 86.8 | 55.8 | 47.9 | 40.7 | 46.3 | 58.7 | 37.7 | 25.1 | 24.8 |
-| HoVer claim verification | 67.6 | 69.2 | 74.8 | 78.8 | 48.2 | 31.2 | 47.7 | 45.7 | 39.7 |
-
-#### Tools & Automation
-
-| Benchmark | **Edge 0.4B** | **9B** | **27B** | Torchcast 27B | pplx-decider 27B | clef | GLiDE 28B | Jev 1.13 | Kev 27B |
-| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| BFCL ★ | 87.2 | 94.4 | 96.2 | 96.9 | 96.2 | 97.9 | 97.0 | 94.3 | 97.0 |
-| ToolRet | 47.9 | 56.4 | 60.8 | 63.5 | 62.6 | 64.3 | 63.4 | 59.9 | 63.5 |
-| API-Bank ★ | 82.5 | 87.8 | 86.8 | 85.0 | 85.2 | 91.8 | 84.8 | 88.0 | 82.7 |
-| Home appliance simulator | 12.5 | 58.0 | 77.3 | 89.8 | 80.7 | 80.7 | 92.0 | 52.3 | 63.6 |
-| When2Call MCQ | 49.7 | 71.2 | 79.7 | 85.3 | 65.1 | 63.3 | 77.9 | 74.6 | 49.4 |
-
-#### Arts & Human Taste
-
-| Benchmark | **Edge 0.4B** | **9B** | **27B** | Torchcast 27B | pplx-decider 27B | clef | GLiDE 28B | Jev 1.13 | Kev 27B |
-| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| BPoMP | 82.4 | 83.2 | 90.9 | 91.1 | 90.5 | 94.1 | 88.0 | 81.8 | 90.6 |
-| Humicroedit | 27.6 | 27.0 | 28.8 | 25.2 | 22.3 | 33.1 | 21.7 | 23.7 | 24.9 |
-| POP909-CL | 23.1 | 11.6 | 46.0 | 50.0 | 55.5 | 14.8 | 70.0 | 15.9 | 35.2 |
-| cfcolor | 12.1 | 25.5 | 30.2 | 43.8 | 28.1 | 34.0 | 30.3 | 28.8 | 28.8 |
-| Habermas Machine | 33.8 | 38.6 | 36.6 | 42.3 | 15.8 | 54.5 | 14.5 | 21.5 | 13.7 |
-| New Yorker caption matching | 52.6 | 63.1 | 69.7 | 73.7 | 69.5 | 62.8 | 64.5 | 62.6 | 62.8 |
-
-</details>
-
-#### Models up to 1B parameters
-
-Edge 0.4B against the sub-1B entries of the live board (the jev-decision-index Space, data generated 2026-10-15): the
-eight highest-scoring ones, plus LiquidAI d1-omni-600M and GLiNER2.5-Decide. Sizes are the board's served
-parameters; models over 1B served parameters are excluded even when their name says 0.8B. Board rank is the
-position on the public index among the 168 board entries plus Edge 0.4B. Area scores are recomputed from each model's
-per-benchmark public results with the kit's aggregation and match every published index.
-
-| Model | Parameters | Public index (skill) | Board rank | Raw index | Breadth | Knowledge | Language | Retrieval | Tools | Arts | Coverage |
-| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| **Edge 0.4B** | 435M | 47.49 | 71 / 169 | 59.70 | 44.54 | 21.4 | 54.6 | 66.7 | 58.1 | 38.6 | 100.0% |
-| vLLM-SR d3-lite | 853M | 36.93 | 106 / 169 | 52.58 | 34.41 | 18.6 | 34.2 | 53.7 | 56.3 | 22.3 | 100.0% |
-| Crivo-150M | 150M | 36.39 | 107 / 169 | 52.45 | 31.50 | 11.8 | 41.8 | 52.2 | 60.7 | 10.1 | 100.0% |
-| Sieve 0.8B | 590M | 32.26 | 114 / 169 | 49.40 | 28.90 | 14.3 | 26.6 | 54.8 | 51.2 | 13.5 | 100.0% |
-| tasksource nano v4 | 149M | 30.90 | 115 / 169 | 47.86 | 26.81 | 7.8 | 37.7 | 49.4 | 43.6 | 12.8 | 100.0% |
-| Saina Helm 2.0 | 853M | 30.47 | 119 / 169 | 48.35 | 25.67 | 8.7 | 31.6 | 43.8 | 58.3 | 6.3 | 100.0% |
-| jiwo 0.8B v2 | 752M | 29.58 | 123 / 169 | 47.20 | 26.75 | 10.9 | 36.1 | 44.2 | 40.3 | 12.2 | 100.0% |
-| DecisionTune 1.0 | 395M | 28.85 | 125 / 169 | 46.84 | 25.43 | 11.7 | 29.9 | 45.0 | 46.5 | 5.7 | 100.0% |
-| vLLM-SR d3-edge | 594M | 28.82 | 126 / 169 | 46.79 | 25.44 | 11.1 | 24.1 | 53.6 | 42.7 | 11.8 | 100.0% |
-| d1-omni-600M | 587M | 17.90 | 146 / 169 | 37.58 | 16.11 | 6.5 | 19.3 | 35.0 | 19.5 | 6.5 | 100.0% |
-| GLiNER2.5-Decide | 486M | 11.48 | 155 / 169 | 33.08 | 10.13 | 2.3 | 8.3 | 25.3 | 14.8 | 9.8 | 100.0% |
-
-<details>
-<summary>Per-benchmark Decision Index results, models up to 1B</summary>
-
-#### Knowledge & Reasoning (models up to 1B)
-
-| Benchmark | **Edge 0.4B** | vLLM-SR d3-lite | Crivo-150M | Sieve 0.8B | tasksource nano v4 | Saina Helm 2.0 | jiwo 0.8B v2 | DecisionTune 1.0 | vLLM-SR d3-edge | d1-omni-600M | GLiNER2.5-Decide |
-| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| GPQA Diamond ★ | 10.2 | 10.9 | 0.0 | **15.0** | 0.0 | 3.4 | 1.4 | 0.0 | 4.1 | 0.0 | 0.0 |
-| GSM8K | 2.1 | **29.9** | 2.0 | 24.0 | 7.5 | 13.5 | 7.1 | 2.0 | 21.6 | 1.1 | 0.2 |
-| ChessBench | **10.7** | 0.2 | 0.0 | 1.1 | 1.6 | 9.6 | 0.0 | 0.0 | 0.0 | 5.2 | 0.0 |
-| MuSR | **34.7** | 33.0 | 26.7 | 29.2 | 32.4 | 18.0 | 31.5 | 20.5 | 20.3 | 1.5 | 12.9 |
-| SATA-Bench | **24.5** | 18.6 | 0.0 | 13.0 | 8.4 | 14.7 | 21.0 | 1.7 | 10.2 | 6.2 | 0.0 |
-| CRUXEval | 0.0 | 0.0 | 7.3 | 0.0 | 8.1 | 0.0 | 0.0 | 5.9 | **9.8** | 6.2 | 5.1 |
-| CLadder | 42.2 | 22.1 | **61.0** | 6.5 | 0.6 | 3.2 | 2.9 | 44.8 | 8.1 | 0.0 | 0.2 |
-| HLE ★ | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | **0.4** | 0.0 | **0.4** |
-| MMLU-Pro ★ | 20.5 | **24.0** | 3.4 | 17.8 | 5.7 | 9.8 | 20.2 | 1.7 | 13.5 | 10.1 | 5.6 |
-| BBH fixed-option tasks ★ | **28.1** | 22.5 | 11.6 | 20.4 | 6.8 | 16.2 | 24.4 | 9.4 | 10.6 | 17.7 | 0.2 |
-| WinoGrande ★ | **60.4** | 41.8 | 21.9 | 28.5 | 16.8 | 8.3 | 10.7 | 43.8 | 23.6 | 20.9 | 1.3 |
-
-#### Language Understanding (models up to 1B)
-
-| Benchmark | **Edge 0.4B** | vLLM-SR d3-lite | Crivo-150M | Sieve 0.8B | tasksource nano v4 | Saina Helm 2.0 | jiwo 0.8B v2 | DecisionTune 1.0 | vLLM-SR d3-edge | d1-omni-600M | GLiNER2.5-Decide |
-| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| ContractNLI | 69.6 | 33.8 | 74.7 | 62.6 | 63.7 | **78.2** | 69.0 | 72.2 | 37.1 | 51.1 | 0.0 |
-| ANLI ★ | **42.6** | 24.1 | 19.0 | 15.0 | 18.7 | 4.7 | 20.8 | 0.0 | 11.3 | 4.1 | 0.0 |
-| HellaSwag ★ | 77.1 | 76.9 | 48.9 | 67.5 | 52.4 | 25.9 | 65.8 | **78.5** | 45.3 | 46.6 | 9.7 |
-| ACOS | 13.3 | 3.5 | **38.9** | 7.2 | 30.9 | 5.0 | 6.9 | 3.3 | 4.8 | 0.0 | 0.0 |
-| FinEntity | **86.4** | 82.5 | 76.7 | 48.9 | 62.5 | 63.5 | 72.9 | 77.7 | 71.3 | 45.5 | 50.2 |
-| iSarcasmEval | **37.1** | 13.7 | 0.0 | 0.0 | 0.0 | 21.0 | 4.6 | 0.0 | 12.2 | 6.7 | 13.0 |
-| VAST | 59.6 | 33.1 | **61.4** | 5.7 | 59.9 | 15.5 | 13.5 | 4.0 | 17.9 | 8.2 | 2.9 |
-| NLI4CT | **45.2** | 34.0 | 15.1 | 26.6 | 17.0 | 21.8 | 27.6 | 0.8 | 15.8 | 9.0 | 0.0 |
-| RAGTruth response-level hallucination | **58.6** | 0.0 | 44.3 | 0.0 | 35.5 | 55.7 | 41.1 | 28.9 | 0.0 | 0.0 | 0.0 |
-
-#### Retrieval & Classification (models up to 1B)
-
-| Benchmark | **Edge 0.4B** | vLLM-SR d3-lite | Crivo-150M | Sieve 0.8B | tasksource nano v4 | Saina Helm 2.0 | jiwo 0.8B v2 | DecisionTune 1.0 | vLLM-SR d3-edge | d1-omni-600M | GLiNER2.5-Decide |
-| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| BANKING77 ★ | 90.7 | 90.2 | 90.6 | 85.5 | 88.8 | 84.4 | 60.2 | **92.1** | 89.8 | 83.5 | 65.2 |
-| CLINC150+OOS ★ | 92.2 | **93.9** | 91.8 | 90.5 | 86.6 | 75.9 | 81.5 | 91.8 | 91.9 | 85.2 | 60.2 |
-| BRIGHT ★ | 22.4 | 28.4 | 18.6 | 18.0 | 18.3 | 5.1 | **30.7** | 7.8 | 22.1 | 0.0 | 9.6 |
-| Amazon ESCI | **39.3** | 26.4 | 29.8 | 26.3 | 22.6 | 32.1 | 36.5 | 37.3 | 11.6 | 8.8 | 3.8 |
-| PhishNChips phishing decisions | **86.8** | 9.7 | 4.0 | 85.3 | 2.9 | 0.0 | 31.2 | 0.2 | 35.7 | 0.0 | 0.0 |
-| HoVer claim verification | 67.6 | 63.5 | **69.4** | 17.1 | 67.9 | 58.3 | 17.2 | 29.6 | 62.1 | 19.6 | 1.4 |
-
-#### Tools & Automation (models up to 1B)
-
-| Benchmark | **Edge 0.4B** | vLLM-SR d3-lite | Crivo-150M | Sieve 0.8B | tasksource nano v4 | Saina Helm 2.0 | jiwo 0.8B v2 | DecisionTune 1.0 | vLLM-SR d3-edge | d1-omni-600M | GLiNER2.5-Decide |
-| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| BFCL ★ | **87.2** | 81.1 | 79.0 | 78.8 | 65.0 | 83.9 | 78.2 | 73.6 | 62.9 | 29.3 | 27.4 |
-| ToolRet | 47.9 | 55.5 | **55.7** | 55.6 | 55.1 | 53.2 | 55.0 | 49.7 | 50.4 | 2.6 | 28.6 |
-| API-Bank ★ | **82.5** | 56.1 | 29.4 | 54.0 | 25.6 | 74.7 | 17.5 | 45.2 | 39.0 | 38.8 | 5.9 |
-| Home appliance simulator | 12.5 | 11.4 | **79.5** | 0.0 | 0.0 | 0.0 | 8.0 | 0.0 | 0.0 | 0.0 | 0.0 |
-| When2Call MCQ | 49.7 | **72.8** | 62.5 | 61.6 | 71.4 | 71.2 | 39.5 | 58.7 | 57.8 | 21.0 | 11.2 |
-
-#### Arts & Human Taste (models up to 1B)
-
-| Benchmark | **Edge 0.4B** | vLLM-SR d3-lite | Crivo-150M | Sieve 0.8B | tasksource nano v4 | Saina Helm 2.0 | jiwo 0.8B v2 | DecisionTune 1.0 | vLLM-SR d3-edge | d1-omni-600M | GLiNER2.5-Decide |
-| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| BPoMP | **82.4** | 43.2 | 0.0 | 12.1 | 18.9 | 0.0 | 0.7 | 18.4 | 8.9 | 0.0 | 14.0 |
-| Humicroedit | **27.6** | 14.7 | 4.5 | 16.6 | 0.0 | 5.6 | 7.1 | 0.0 | 6.7 | 2.3 | 1.3 |
-| POP909-CL | **23.1** | 0.9 | 1.2 | 0.6 | 0.8 | 0.0 | 0.0 | 0.6 | 0.4 | 0.1 | 0.9 |
-| cfcolor | 12.1 | 12.8 | 0.6 | 13.5 | 4.0 | 15.2 | **24.1** | 0.0 | 5.0 | 9.3 | 6.4 |
-| Habermas Machine | **33.8** | 9.9 | 10.1 | 14.4 | 3.3 | 0.0 | 10.4 | 8.9 | 4.3 | 7.5 | 20.8 |
-| New Yorker caption matching | **52.6** | 51.9 | 44.1 | 24.0 | 50.0 | 16.9 | 31.1 | 6.2 | 45.8 | 20.0 | 15.2 |
-
-</details>
-
-### KGJevBench
-
-Decision tasks from the GLiClass benchmark suite (KGJevBench): policy decisions, instruction following,
-knowledge-graph walking, tool calling and routing, judging, and typed Jev-style decisions. GLiClass models were run
-with the suite's `run_sample_500.sh` (up to 500 examples per task, bf16); the 12-relations row uses
-`eval_kg_reasoning.py --mode stepwise --pad-relations 12`. The other columns are the KGJevBench comparison results for
-those models on the same tasks and metrics; they were not re-run here. All values ×100; the best value in each row is bold.
-Averages are recomputed from the cells shown, so the reference columns can differ by up to 0.3 from averages computed
-on unrounded scores.
-
-| Benchmark (metric) | **Edge 0.4B** | **9B** | **27B** | pplx-decider-27b | glide | Jev-1.1B | clef | d1 | clef-flash | gpt-6-luna | Kev-4B | DeBERTa |
-| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| fast-decisions (domain avg) | 54.8 | 53.7 | 54.3 | 59.5 | 61.7 | 61.2 | 59.4 | **61.8** | 57.3 | 60.2 | 58.5 | 56.7 |
-| task routing (top-1 primary) | **93.3** | 88.3 | 88.3 | 82.0 | 73.0 | 74.0 | 85.0 | 75.0 | 77.3 | 53.7 | 65.7 | 68.7 |
-| tool calling (exact set) | **89.3** | 88.3 | 89.0 | 88.0 | 79.0 | 65.3 | 83.0 | 72.3 | 78.3 | 46.7 | 59.0 | 88.3 |
-| instruction following (group acc) | 86.5 | 85.6 | 87.4 | 92.8 | 94.6 | 93.7 | **95.5** | 93.7 | 91.9 | 92.8 | 82.9 | 42.3 |
-| KG step-by-step (path acc) | 98.3 | 97.7 | 96.3 | **98.7** | 91.0 | 95.7 | 92.7 | 97.0 | 91.0 | 90.7 | 86.7 | 67.0 |
-| KG step-by-step, 12 relations/step | 93.0 | 94.7 | 93.3 | **95.3** | 82.0 | 85.7 | 87.7 | 87.0 | 85.3 | 79.7 | 73.7 | 51.7 |
-| KG path ranking (Hits@1) | 98.7 | 98.7 | **100.0** | 95.0 | 94.0 | 87.7 | 87.0 | 81.3 | 56.0 | 92.0 | 67.7 | 50.0 |
-| triple relevance (F1, keyword) | **90.6** | 84.7 | 83.2 | 31.8 | 33.0 | 30.7 | 28.4 | 28.4 | 21.9 | 21.2 | 47.3 | 69.5 |
-| reranking (R@1) | 92.7 | 95.7 | 94.3 | 96.7 | 96.0 | 96.7 | 94.7 | 95.7 | 96.7 | **97.0** | 93.3 | 81.7 |
-| NLI (accuracy, yes/no) | 89.2 | 96.9 | **100.0** | 90.7 | 96.0 | 98.8 | 92.0 | 94.1 | 92.6 | 89.5 | 93.2 | 85.5 |
-| fact checking (acc, checkable) | 87.0 | 94.3 | 98.0 | **99.3** | 93.5 | 98.0 | 92.7 | 97.3 | 94.7 | 97.7 | 83.3 | 86.3 |
-| moderation rules (acc, yes/no) | 85.6 | 81.2 | 92.4 | 93.3 | **94.0** | 92.3 | 89.0 | 82.0 | 85.7 | 85.0 | 77.3 | 78.3 |
-| policy decisions (acc) | 64.0 | 94.0 | 96.3 | **97.3** | 97.0 | 96.7 | 95.7 | 95.3 | 84.0 | 89.7 | 81.3 | 72.0 |
-| logical reasoning MCQA (acc; chance 25) | 43.7 | 70.0 | 75.7 | **90.0** | 83.0 | 81.3 | 76.0 | 79.3 | 71.3 | 65.0 | 63.0 | 38.0 |
-| web actions (step acc) | 51.0 | 82.0 | **85.0** | 79.0 | 73.0 | 72.0 | 78.0 | 71.0 | 60.0 | 55.0 | 59.0 | 47.0 |
-| LogiQA (acc; chance 25) | 42.4 | 59.0 | 65.6 | 72.0 | 65.0 | **77.1** | 63.9 | 69.4 | 60.2 | 61.1 | 50.8 | 31.5 |
-| MedMCQA (acc; chance 25) | 34.8 | 60.2 | 67.2 | **74.6** | 67.0 | 74.5 | 69.1 | 72.5 | 66.5 | 72.3 | 54.0 | 28.0 |
-| OpenBookQA closed (acc; chance 25) | 75.6 | 88.2 | 89.6 | **97.0** | 93.0 | 95.2 | 95.6 | 95.0 | 93.6 | 84.8 | 82.0 | 47.0 |
-| CommonsenseQA (acc; chance 20) | 78.6 | 81.6 | 86.2 | 88.1 | 88.0 | 89.0 | 90.3 | 86.3 | **93.6** | 81.5 | 76.9 | 58.9 |
-| JevBench public (acc) | 77.9 | 82.3 | 87.4 | 88.7 | 91.0 | 85.7 | 88.7 | 86.6 | 82.3 | **91.8** | 72.3 | 65.8 |
-| JevBench HF gold questions (acc) | 86.8 | 86.0 | 92.4 | **92.5** | 88.0 | 91.3 | 92.4 | 90.6 | 89.3 | 91.0 | 89.6 | 88.4 |
-| JevJudge text (skill ×100) | -0.1 | 17.5 | 32.2 | 43.9 | **52.4** | 42.2 | 37.8 | 38.5 | 23.5 | 40.1 | 24.5 | 11.2 |
-| Open-Jev (agreement) | 59.6 | 75.1 | 82.4 | **89.6** | 85.9 | 78.3 | 78.3 | 81.0 | 74.1 | 78.1 | 71.2 | 57.8 |
-| tasksource-Jev (agreement) | 78.4 | 78.6 | 78.8 | **83.9** | 75.0 | 73.6 | 73.3 | 75.0 | 68.9 | 66.9 | 65.1 | 53.8 |
-| Jev Distill (agreement w/ teacher) | 51.5 | 54.8 | 61.0 | 63.7 | 57.9 | **72.7** | 62.0 | 65.1 | 62.3 | 67.0 | 57.4 | 42.7 |
-| Behavior (present-class F1) | 37.6 | 56.7 | 68.3 | 68.9 | **87.5** | 67.0 | 66.0 | 67.4 | 52.6 | 57.4 | 46.3 | 27.7 |
-| typed decisions (acc) | 49.3 | 64.2 | 68.5 | 73.9 | 71.8 | 74.1 | 71.4 | **74.2** | 70.2 | 69.3 | — | 51.6 |
-| agent tool decisions (macro acc) | 57.1 | 78.5 | **87.6** | 85.4 | 81.8 | 82.9 | 73.9 | 83.4 | 64.1 | 75.2 | — | 40.3 |
-| *Average, 26 rows all models have* | 70.8 | 78.7 | 82.5 | 82.8 | 80.4 | 79.9 | 79.0 | 78.4 | 73.5 | 73.4 | 68.5 | 57.5 |
-| *Average, all 28 rows* | 69.5 | 78.2 | 82.2 | 82.6 | 80.2 | 79.8 | 78.6 | 78.4 | 73.0 | 73.3 | — | 56.7 |
-
-Several KGJevBench sets are written in the formats GLiClass is trained on (for example triple relevance, task
-routing, and tool calling), and tasksource-Jev overlaps the training data; read those rows with that in mind.
-
-## Training and intended use
-
-Training had three stages:
-
-1. **Large-scale supervised training** on a mixture that recasts classification, NLI, QA, reranking,
-   extraction, tool calling, moderation, judging, and reasoning data as decisions over candidate labels,
-   with instructions, label descriptions where available. Single-label, multi-label, and scoring examples are mixed
-   in one run with a focal loss.
-2. **Reinforcement learning** with a PPO-style objective whose actions are the label decisions. Rewards
-   are task metrics (F1, accuracy, recall), with a value baseline, a clipped ratio, and optional KL and entropy terms.
-3. **Calibration**: with the backbone frozen, a small calibrator reads the text and label representations and logit statistics and predicts a per-pair temperature and bias, fitted by negative log-likelihood on held-out decision data.
-
-Use this model for zero-shot and instruction-following classification, scoring, reranking, natural language
-inference, knowledge graph and multi-step reasoning, guardrails, policy moderation, routing, and LLM-output
-judging. It returns label probabilities and does not generate text.
+Pass `prompt="your instruction or policy"` to guide the decision. Use `classification_type="single-label"` for mutually exclusive choices. Multi-label results may contain more than one label or no labels above the threshold.
 
 ## Limitations
 
-- Label wording, instructions, domain, input length, and thresholds affect predictions.
-- Knowledge-heavy and multi-step reasoning tasks are the main gap to the larger models: multi-step arithmetic (GSM8K), hard science and exam questions (GPQA, HLE, MMLU-Pro, MedMCQA), logic puzzles, and long agent-trace judging remain weak spots.
-- Results are strongest in English; quality on other languages is lower.
-- Calibrated scores are fitted on held-out data and can drift on domains far from training.
-- Treat outputs used for safety, moderation, legal, or medical decisions as signals for review, not final verdicts.
+Predictions depend on label wording, instructions, domain, input length, and thresholds. English is the primary language. Calibration can drift on new domains; evaluate scores and thresholds on representative data. See the [upstream model card](https://huggingface.co/knowledgator/gliclass-v4-edge) for training details, evaluations, and further limitations.
 
-## Citation
+## Attribution and license
 
-```bibtex
-@misc{stepanov2026scxrouter,
-  title  = {SCX Router: Streaming Zero-Shot Model Selection with a Decoder-KV Classifier and a Real-World Task Ontology},
-  author = {Stepanov, Ihor and Smechov, Aleksandr and Shtopko, Mykhailo and Vodianytskyi, Dmytro and Lukashov, Oleksandr},
-  year   = {2026},
-  eprint = {2609.02292},
-  archivePrefix = {arXiv}
-}
-```
+Based on **GLiClass 4**, developed by **Knowledgator**. Original checkpoint: [knowledgator/gliclass-v4-edge](https://huggingface.co/knowledgator/gliclass-v4-edge/tree/90be5550327b138635db1f63531f9dfa15e624da), revision `90be5550327b138635db1f63531f9dfa15e624da`. Distributed under the upstream **Apache License 2.0**; see [LICENSE](LICENSE). The original model card is preserved in [UPSTREAM_README.md](UPSTREAM_README.md) and at the upstream link.
+
+Related work: [GLiClass](https://arxiv.org/abs/2508.07662) and [SCX Router](https://arxiv.org/abs/2609.02292).

UPSTREAM_README.md

@@ -0,0 +1,601 @@
+---
+language:
+- en
+library_name: gliclass
+pipeline_tag: zero-shot-classification
+tags:
+- gliclass
+- deberta
+- zero-shot-classification
+- text-classification
+- multi-label-classification
+- instruction-following
+- reranking
+- guardrails
+- content-moderation
+- knowledge-graph-reasoning
+license: apache-2.0
+base_model: microsoft/deberta-v3-large
+---
+# GLiClass v4 Edge
+
+**Instruction-following zero-shot classification in a 0.4B encoder, built for CPUs and edge devices.**
+
+`knowledgator/gliclass-v4-edge` is the 0.4B-parameter edge release of
+[GLiClass v4](https://github.com/Knowledgator/GLiClass). The labels you pass and an
+optional instruction tell it what to decide at inference time. It returns a score for
+every label in one forward pass and generates no text. It reaches a Decision Index public skill of 47.5 at a median
+latency of 11 ms per decision, ahead of every model up to 1B parameters on the public board (the best of them scores 36.9).
+
+
+## Usage
+
+```bash
+pip install gliclass -U
+```
+
+Or install from source in a Python 3.10+ environment:
+
+```bash
+git clone https://github.com/Knowledgator/GLiClass.git
+cd GLiClass
+pip install -e .
+```
+
+The following examples reuse `pipeline`:
+
+```python
+import torch
+from gliclass import GLiClassModel, ZeroShotClassificationPipeline
+
+model_id = "knowledgator/gliclass-v4-edge"
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+model = GLiClassModel.from_pretrained(model_id)
+pipeline = ZeroShotClassificationPipeline(
+    model,
+    model_id,  # loads the tokenizer
+    classification_type="multi-label",
+    max_length=2048,
+    device=device,
+)
+```
+
+For a local copy, replace the model ID with the checkpoint directory.
+
+### Multi-label classification
+
+```python
+results = pipeline(
+    "The battery lasts two days, but the screen scratches easily.",
+    ["battery", "display", "durability", "price", "shipping"],
+    threshold=0.5,
+)[0]
+for result in results:
+    print(result["label"], round(result["score"], 3))
+```
+
+Each prediction contains `label` and `score`. Pass a list of texts and `batch_size=8` for batched inference.
+
+Scores are calibrated on held-out data; predictions also carry `initial_score`, the uncalibrated score.
+
+### Single-label decisions and scoring
+
+Use `classification_type="single-label"` to pick one answer: scores are a softmax over the labels.
+To score an input, use the levels of a scale as labels and read their probabilities:
+
+```python
+answer = pipeline(
+    "Our refund window is 30 days. The customer bought the item 45 days ago.",
+    ["refund approved", "refund denied", "escalate to a human"],
+    prompt="Apply the refund policy to the customer's request.",
+    classification_type="single-label",
+)[0]
+print(answer)
+
+quality = pipeline(
+    "Q: What causes tides? A: Mostly the Moon's gravity, with a smaller effect from the Sun.",
+    ["1 - wrong", "2 - poor", "3 - partly correct", "4 - good", "5 - excellent"],
+    prompt="Rate the factual quality of the answer.",
+    classification_type="single-label",
+    return_hierarchical=True,
+)[0]
+print(quality)  # every level with its probability
+```
+
+### Instruction following
+
+Use `prompt` for the instruction, policy, or rubric. Labels are interpreted in light of it:
+
+```python
+policy = (
+    "Flag a message only if it shares another person's contact details without consent. "
+    "Public business contact pages are allowed."
+)
+results = pipeline(
+    [
+        "You can reach our support team at help@acme.com.",
+        "Here is my neighbour's personal phone number: 555-0134, call her at night.",
+    ],
+    ["violates policy", "complies with policy"],
+    prompt=policy,
+    classification_type="single-label",
+)
+```
+
+Few-shot examples (`examples=[{"text": ..., "labels": [...]}]`) and hierarchical labels
+(`{"sentiment": ["positive", "negative"], "topic": ["product", "service"]}`) are also supported.
+
+### Knowledge graph reasoning
+
+Pass the graph as text next to the story, the question as the instruction, and the answers as labels:
+
+```python
+graph = (
+    "Alice -member_of-> Finance team\n"
+    "Finance team -can_read-> Reports folder\n"
+    "Reports folder -contains-> Salaries.xlsx\n"
+    "Alice -explicit_deny-> Salaries.xlsx\n"
+    "Rule: explicit restrictions override team permissions."
+)
+answer = pipeline(
+    graph,
+    ["yes", "no"],
+    prompt="Can Alice open Salaries.xlsx?",
+    classification_type="single-label",
+)[0]
+print(answer)  # [{'label': 'no', 'score': ...}]
+```
+
+The same pattern covers multi-hop questions, agent and web-browsing steps (which link, tool, or action
+comes next), and NLI (`entailment`, `neutral`, `contradiction`).
+
+### Reranking
+
+```python
+query = "How do I reset a forgotten router password?"
+passages = [
+    "Hold the reset button for 10 seconds to restore factory settings, then log in with the default password.",
+    "Our routers support Wi-Fi 7 and dual-band operation.",
+    "To change your Wi-Fi name, open the admin panel and edit the SSID field.",
+]
+scores = pipeline(
+    passages,
+    [query],
+    prompt="Does the passage answer the query?",
+    threshold=0.0,
+)
+ranked = sorted(zip(passages, scores), key=lambda p: -p[1][0]["score"])
+```
+
+## Serving a Jev-style decision API
+
+Decision engines such as Jev take a **state** (text or JSON) and a set of typed **questions**, and return a
+probability for every option of every question, with no generated text. GLiClass answers the same requests
+directly: each question is one GLiClass pass in which the state is the text, the question's `instructions`
+are the prompt, and every option is a label. This is the setup used for the Decision Index results below.
+
+| Question type | Request fields | GLiClass labels | Answer |
+| --- | --- | --- | --- |
+| `choice` | `instructions`, `criteria: {key: description}` | the option descriptions (the key when a description is missing) | `choice`, `confidence`, `probabilities` per key |
+| `noul` (yes / no) | `instructions`, optional `criteria: {"true": ..., "false": ...}` | the true and false descriptions (`yes` / `no` when missing) | `noul` = P(true) |
+| `score` | `instructions`, `criteria: [level 0, level 1, ...]` | the level descriptions, in order | `score` = expected level, `confidence`, `probabilities` per level |
+
+Probabilities are a softmax over the option logits. A JSON state is rendered as `key: value` lines, with string lists as `- item` lines.
+
+The server below exposes `POST /v1/systemone` and `GET /v1/models`, the request and response shape of
+TypeSafe's System One API, so clients written for it (for example the
+[WorkflowEvals](https://github.com/typesafe-ai/WorkflowEvals) runner) can point at a local GLiClass model.
+It needs only `gliclass`, `fastapi`, and `uvicorn`:
+
+```python
+# jev_api.py
+import json
+import os
+
+import torch
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+
+from gliclass import GLiClassModel, ZeroShotClassificationPipeline
+
+MODEL_ID = os.environ.get("MODEL", "knowledgator/gliclass-v4-edge")
+MAX_LENGTH = int(os.environ.get("MAX_LENGTH", "16384"))
+DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
+DTYPE = torch.bfloat16 if DEVICE != "cpu" else torch.float32
+
+model = GLiClassModel.from_pretrained(MODEL_ID, torch_dtype=DTYPE).to(DEVICE).eval()
+pipe = ZeroShotClassificationPipeline(model, MODEL_ID, max_length=10**9, classification_type="single-label",
+                                      device=DEVICE, progress_bar=False).pipe
+app = FastAPI(title="GLiClass System One API")
+
+
+class Request(BaseModel):
+    state: str | dict | list | None = None
+    questions: dict[str, dict]
+
+
+def as_text(value):
+    return value if isinstance(value, str) else json.dumps(value, ensure_ascii=False)
+
+
+def render_state(state):
+    if state in (None, "", {}, []):
+        return ""
+    if isinstance(state, str):
+        return state
+    if isinstance(state, list):
+        return "\n".join(f"- {as_text(item)}" for item in state)
+    lines = []
+    for key, value in state.items():
+        if isinstance(value, list) and all(isinstance(item, str) for item in value):
+            lines += [f"{key}:"] + [f"- {item}" for item in value]
+        else:
+            lines.append(f"{key}: {as_text(value)}")
+    return "\n".join(lines)
+
+
+def options(question):
+    kind, criteria = question.get("type"), question.get("criteria")
+    if kind == "choice":
+        if not criteria or len(criteria) < 2:
+            raise HTTPException(422, "a choice question needs at least two criteria")
+        return list(criteria), [as_text(v) if v is not None else k for k, v in criteria.items()]
+    if kind == "noul":
+        c = criteria or {}
+        return ["true", "false"], [as_text(c.get("true") or "yes"), as_text(c.get("false") or "no")]
+    if kind == "score":
+        if not criteria or len(criteria) < 2:
+            raise HTTPException(422, "a score question needs at least two levels")
+        return [str(i) for i in range(len(criteria))], [as_text(c) for c in criteria]
+    raise HTTPException(422, f"unsupported question type {kind!r}")
+
+
+@torch.no_grad()
+def probabilities(text, prompt, labels):
+    inputs = pipe.prepare_inputs([text], labels, same_labels=True, prompt=prompt)
+    length = inputs["input_ids"].shape[-1]
+    if length > MAX_LENGTH:  # refuse instead of truncating
+        raise HTTPException(422, f"input of {length} tokens exceeds the {MAX_LENGTH}-token limit")
+    logits = model(**inputs, max_num_classes=len(labels)).logits[0, : len(labels)].float()
+    return torch.softmax(logits, dim=-1).tolist(), length
+
+
+@app.get("/v1/models")
+def models():
+    return {"models": [{"name": MODEL_ID, "description": "GLiClass"}]}
+
+
+@app.post("/v1/systemone")
+def systemone(request: Request):
+    text, answers, tokens = render_state(request.state), {}, 0
+    for name, question in request.questions.items():
+        keys, labels = options(question)
+        prompt = as_text(question.get("instructions") or "") or None
+        probs, length = probabilities(text, prompt, labels)
+        tokens += length
+        p = dict(zip(keys, probs))
+        best = max(keys, key=p.get)
+        if question["type"] == "choice":
+            answers[name] = {"type": "choice", "choice": best, "confidence": p[best], "probabilities": p}
+        elif question["type"] == "noul":
+            answers[name] = {"type": "noul", "noul": p["true"]}
+        else:
+            answers[name] = {"type": "score", "score": sum(int(k) * v for k, v in p.items()),
+                             "confidence": p[best], "probabilities": p}
+    return {"model": MODEL_ID, "answers": answers, "usage": {"input_tokens": tokens, "output_tokens": 0}}
+```
+
+```bash
+pip install gliclass fastapi uvicorn
+uvicorn jev_api:app --host 0.0.0.0 --port 8765
+```
+
+```bash
+curl -s localhost:8765/v1/systemone -H 'Content-Type: application/json' -d '{
+  "state": {"ticket": "Checkout errors hit 12% after the 14:05 deploy.",
+            "facts": ["Rolling back takes two minutes.", "No incident is open yet."]},
+  "questions": {
+    "action": {"type": "choice", "instructions": "What should the on-call engineer do right now?",
+               "criteria": {"rollback": "Roll back to the last healthy build.",
+                            "wait": "Watch error rates for another hour.",
+                            "escalate": "Page the payments team lead."}},
+    "urgent": {"type": "noul", "instructions": "Does this need action within the next hour?",
+               "criteria": {"true": "Yes, customers are affected right now.", "false": "No, it can wait."}},
+    "severity": {"type": "score", "instructions": "Rate the incident severity.",
+                 "criteria": ["minor", "moderate", "major", "critical"]}
+  }}'
+```
+
+The response has one answer per question. This one comes from gliclass-v4-edge (rounded):
+
+```json
+{
+  "model": "knowledgator/gliclass-v4-edge",
+  "answers": {
+    "action": {"type": "choice", "choice": "rollback", "confidence": 0.938, "probabilities": {"rollback": 0.938, "wait": 0.059, "escalate": 0.003}},
+    "urgent": {"type": "noul", "noul": 0.300},
+    "severity": {"type": "score", "score": 0.24, "confidence": 0.832, "probabilities": {"0": 0.832, "1": 0.124, "2": 0.013, "3": 0.031}}
+  },
+  "usage": {"input_tokens": 172, "output_tokens": 0}
+}
+```
+
+Here the edge model picks the rollback but rates the incident as minor and not urgent; check judgment-style
+questions such as severity on your own data before relying on them.
+
+- **Instructions matter.** Put the decision rule or rubric in `instructions` and give `noul` questions explicit `true` / `false` descriptions; bare yes / no labels are less reliable.
+- **Throughput.** The server answers questions one at a time. For batch jobs, group questions that share a state with `pipe.prepare_inputs([text] * n, labels_per_question, same_labels=False, prompt=prompts)` and run one forward pass.
+- **Long inputs** over `MAX_LENGTH` tokens are refused with HTTP 422, never truncated, as the Decision Index rules require.
+- **Calibration.** For calibrated checkpoints `logits` are the calibrated logits; `uncalibrated_logits` holds the raw ones.
+- The Decision Index results below come from a kit engine (`gliclass_engine.GLiClassEngine`) that applies exactly this mapping, run with `python -m decision_index run --engine gliclass_engine:GLiClassEngine --model knowledgator/gliclass-v4-edge`.
+
+## Reported evaluation results
+
+Columns **Edge 0.4B**, **9B**, and **27B** are [gliclass-v4-edge](https://huggingface.co/knowledgator/gliclass-v4-edge),
+[gliclass-v4-9b](https://huggingface.co/knowledgator/gliclass-v4-9b), and
+[gliclass-v4-27b](https://huggingface.co/knowledgator/gliclass-v4-27b); this card is **Edge 0.4B**.
+
+### Decision Index 0.3
+
+Public part of the [Decision Index](https://github.com/apolinario/decision-index) 0.3 (37 benchmarks in five
+areas, 140,178 requests), scored with the kit's own scorers. Skill is chance-corrected (0 = random guessing,
+100 = perfect) and coverage-adjusted (unanswered = wrong); the public index weighs the areas 25.8 / 25.8 / 20.0 /
+18.3 / 10.0%.
+
+GLiClass models: one pass per question through the Jev-style mapping above, on one RTX PRO 6000
+(this model: bf16, `max_length=16384`, batch size 8). Edge 0.4B, 9B, and 27B take a median of 11.0, 34.0, and 73.7 ms per request. Other models: the per-benchmark public results in the kit's board snapshot, with area scores recomputed using the kit's aggregation, which reproduces each published index
+exactly. The main board ranking (the Full score) also includes private tests; this table covers the public
+part only.
+
+| Model | Public index (skill) | Raw index | Breadth | Knowledge | Language | Retrieval | Tools | Arts |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| **Edge 0.4B** | 47.49 | 59.70 | 44.54 | 21.4 | 54.6 | 66.7 | 58.1 | 38.6 |
+| **9B** | 55.56 | 66.10 | 53.99 | 36.6 | 61.6 | 61.6 | 74.8 | 41.5 |
+| **27B** | 61.74 | 70.76 | 60.66 | 45.7 | 67.6 | 63.0 | 81.0 | 50.3 |
+| Torchcast 27B | 65.10 | 73.64 | 63.86 | 46.7 | 72.0 | 67.5 | 84.6 | 54.4 |
+| pplx-decider 27B | 62.25 | 71.66 | 61.52 | 52.0 | 67.5 | 61.3 | 78.9 | 47.0 |
+| clef | 61.71 | 71.07 | 61.04 | 52.8 | 61.3 | 62.8 | 80.7 | 48.9 |
+| GLiDE 28B | 59.06 | 69.61 | 57.96 | 45.8 | 59.7 | 58.4 | 83.6 | 48.2 |
+| Jev 1.13 | 57.96 | 68.55 | 57.27 | 53.9 | 59.2 | 55.4 | 75.1 | 39.1 |
+| Kev 27B | 56.69 | 67.55 | 55.91 | 45.7 | 62.6 | 55.7 | 72.6 | 42.7 |
+
+Every benchmark, as skill (★ = gold benchmark, weight 1.2 inside its area):
+
+<details>
+<summary>Per-benchmark Decision Index results</summary>
+
+#### Knowledge & Reasoning
+
+| Benchmark | **Edge 0.4B** | **9B** | **27B** | Torchcast 27B | pplx-decider 27B | clef | GLiDE 28B | Jev 1.13 | Kev 27B |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| GPQA Diamond ★ | 10.2 | 25.2 | 26.5 | 35.4 | 41.5 | 31.3 | 36.0 | 71.4 | 29.9 |
+| GSM8K | 2.1 | 42.3 | 60.1 | 58.1 | 72.0 | 52.1 | 61.2 | 66.2 | 65.5 |
+| ChessBench | 10.7 | 10.4 | 13.7 | 10.8 | 10.3 | 17.8 | 9.9 | 9.8 | 11.0 |
+| MuSR | 34.7 | 38.9 | 49.7 | 48.0 | 53.5 | 74.2 | 44.8 | 46.1 | 43.8 |
+| SATA-Bench | 24.5 | 26.0 | 34.4 | 13.9 | 27.2 | 32.6 | 28.6 | 25.4 | 26.9 |
+| CRUXEval | 0.0 | 28.7 | 57.4 | 66.0 | 77.2 | 78.6 | 57.4 | 57.1 | 63.8 |
+| CLadder | 42.2 | 54.4 | 63.8 | 49.0 | 58.6 | 88.2 | 54.9 | 45.3 | 48.3 |
+| HLE ★ | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.7 | 0.0 |
+| MMLU-Pro ★ | 20.5 | 46.4 | 57.5 | 66.7 | 67.4 | 61.7 | 66.9 | 80.5 | 63.2 |
+| BBH fixed-option tasks ★ | 28.1 | 55.8 | 66.9 | 72.0 | 76.8 | 61.8 | 68.6 | 89.7 | 70.8 |
+| WinoGrande ★ | 60.4 | 71.4 | 73.6 | 87.7 | 84.8 | 87.1 | 71.9 | 83.9 | 76.6 |
+
+#### Language Understanding
+
+| Benchmark | **Edge 0.4B** | **9B** | **27B** | Torchcast 27B | pplx-decider 27B | clef | GLiDE 28B | Jev 1.13 | Kev 27B |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| ContractNLI | 69.6 | 77.0 | 76.8 | 80.4 | 68.7 | 73.0 | 69.5 | 59.1 | 68.0 |
+| ANLI ★ | 42.6 | 52.8 | 62.5 | 68.2 | 61.5 | 55.0 | 60.0 | 62.2 | 56.5 |
+| HellaSwag ★ | 77.1 | 89.8 | 93.6 | 96.8 | 91.0 | 97.6 | 93.4 | 92.7 | 92.5 |
+| ACOS | 13.3 | 28.2 | 21.3 | 55.9 | 25.9 | 31.0 | 22.1 | 27.3 | 20.8 |
+| FinEntity | 86.4 | 83.3 | 92.2 | 90.0 | 93.3 | 94.2 | 88.3 | 80.8 | 91.2 |
+| iSarcasmEval | 37.1 | 38.9 | 54.5 | 35.8 | 62.7 | 31.0 | 22.2 | 36.3 | 50.2 |
+| VAST | 59.6 | 61.7 | 66.5 | 72.9 | 65.1 | 39.0 | 54.4 | 46.9 | 52.4 |
+| NLI4CT | 45.2 | 56.9 | 71.0 | 73.2 | 70.6 | 66.9 | 69.7 | 69.0 | 67.2 |
+| RAGTruth response-level hallucination | 58.6 | 62.2 | 65.8 | 71.0 | 65.0 | 57.6 | 51.4 | 51.3 | 59.8 |
+
+#### Retrieval & Classification
+
+| Benchmark | **Edge 0.4B** | **9B** | **27B** | Torchcast 27B | pplx-decider 27B | clef | GLiDE 28B | Jev 1.13 | Kev 27B |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| BANKING77 ★ | 90.7 | 80.1 | 80.0 | 91.0 | 80.3 | 94.0 | 79.4 | 79.5 | 85.9 |
+| CLINC150+OOS ★ | 92.2 | 85.0 | 88.9 | 93.4 | 91.5 | 97.4 | 87.3 | 89.2 | 86.5 |
+| BRIGHT ★ | 22.4 | 31.5 | 33.3 | 44.3 | 44.4 | 40.6 | 43.6 | 40.6 | 41.7 |
+| Amazon ESCI | 39.3 | 45.4 | 50.3 | 51.9 | 50.3 | 46.5 | 47.9 | 43.8 | 46.5 |
+| PhishNChips phishing decisions | 86.8 | 55.8 | 47.9 | 40.7 | 46.3 | 58.7 | 37.7 | 25.1 | 24.8 |
+| HoVer claim verification | 67.6 | 69.2 | 74.8 | 78.8 | 48.2 | 31.2 | 47.7 | 45.7 | 39.7 |
+
+#### Tools & Automation
+
+| Benchmark | **Edge 0.4B** | **9B** | **27B** | Torchcast 27B | pplx-decider 27B | clef | GLiDE 28B | Jev 1.13 | Kev 27B |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| BFCL ★ | 87.2 | 94.4 | 96.2 | 96.9 | 96.2 | 97.9 | 97.0 | 94.3 | 97.0 |
+| ToolRet | 47.9 | 56.4 | 60.8 | 63.5 | 62.6 | 64.3 | 63.4 | 59.9 | 63.5 |
+| API-Bank ★ | 82.5 | 87.8 | 86.8 | 85.0 | 85.2 | 91.8 | 84.8 | 88.0 | 82.7 |
+| Home appliance simulator | 12.5 | 58.0 | 77.3 | 89.8 | 80.7 | 80.7 | 92.0 | 52.3 | 63.6 |
+| When2Call MCQ | 49.7 | 71.2 | 79.7 | 85.3 | 65.1 | 63.3 | 77.9 | 74.6 | 49.4 |
+
+#### Arts & Human Taste
+
+| Benchmark | **Edge 0.4B** | **9B** | **27B** | Torchcast 27B | pplx-decider 27B | clef | GLiDE 28B | Jev 1.13 | Kev 27B |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| BPoMP | 82.4 | 83.2 | 90.9 | 91.1 | 90.5 | 94.1 | 88.0 | 81.8 | 90.6 |
+| Humicroedit | 27.6 | 27.0 | 28.8 | 25.2 | 22.3 | 33.1 | 21.7 | 23.7 | 24.9 |
+| POP909-CL | 23.1 | 11.6 | 46.0 | 50.0 | 55.5 | 14.8 | 70.0 | 15.9 | 35.2 |
+| cfcolor | 12.1 | 25.5 | 30.2 | 43.8 | 28.1 | 34.0 | 30.3 | 28.8 | 28.8 |
+| Habermas Machine | 33.8 | 38.6 | 36.6 | 42.3 | 15.8 | 54.5 | 14.5 | 21.5 | 13.7 |
+| New Yorker caption matching | 52.6 | 63.1 | 69.7 | 73.7 | 69.5 | 62.8 | 64.5 | 62.6 | 62.8 |
+
+</details>
+
+#### Models up to 1B parameters
+
+Edge 0.4B against the sub-1B entries of the live board (the jev-decision-index Space, data generated 2026-10-15): the
+eight highest-scoring ones, plus LiquidAI d1-omni-600M and GLiNER2.5-Decide. Sizes are the board's served
+parameters; models over 1B served parameters are excluded even when their name says 0.8B. Board rank is the
+position on the public index among the 168 board entries plus Edge 0.4B. Area scores are recomputed from each model's
+per-benchmark public results with the kit's aggregation and match every published index.
+
+| Model | Parameters | Public index (skill) | Board rank | Raw index | Breadth | Knowledge | Language | Retrieval | Tools | Arts | Coverage |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| **Edge 0.4B** | 435M | 47.49 | 71 / 169 | 59.70 | 44.54 | 21.4 | 54.6 | 66.7 | 58.1 | 38.6 | 100.0% |
+| vLLM-SR d3-lite | 853M | 36.93 | 106 / 169 | 52.58 | 34.41 | 18.6 | 34.2 | 53.7 | 56.3 | 22.3 | 100.0% |
+| Crivo-150M | 150M | 36.39 | 107 / 169 | 52.45 | 31.50 | 11.8 | 41.8 | 52.2 | 60.7 | 10.1 | 100.0% |
+| Sieve 0.8B | 590M | 32.26 | 114 / 169 | 49.40 | 28.90 | 14.3 | 26.6 | 54.8 | 51.2 | 13.5 | 100.0% |
+| tasksource nano v4 | 149M | 30.90 | 115 / 169 | 47.86 | 26.81 | 7.8 | 37.7 | 49.4 | 43.6 | 12.8 | 100.0% |
+| Saina Helm 2.0 | 853M | 30.47 | 119 / 169 | 48.35 | 25.67 | 8.7 | 31.6 | 43.8 | 58.3 | 6.3 | 100.0% |
+| jiwo 0.8B v2 | 752M | 29.58 | 123 / 169 | 47.20 | 26.75 | 10.9 | 36.1 | 44.2 | 40.3 | 12.2 | 100.0% |
+| DecisionTune 1.0 | 395M | 28.85 | 125 / 169 | 46.84 | 25.43 | 11.7 | 29.9 | 45.0 | 46.5 | 5.7 | 100.0% |
+| vLLM-SR d3-edge | 594M | 28.82 | 126 / 169 | 46.79 | 25.44 | 11.1 | 24.1 | 53.6 | 42.7 | 11.8 | 100.0% |
+| d1-omni-600M | 587M | 17.90 | 146 / 169 | 37.58 | 16.11 | 6.5 | 19.3 | 35.0 | 19.5 | 6.5 | 100.0% |
+| GLiNER2.5-Decide | 486M | 11.48 | 155 / 169 | 33.08 | 10.13 | 2.3 | 8.3 | 25.3 | 14.8 | 9.8 | 100.0% |
+
+<details>
+<summary>Per-benchmark Decision Index results, models up to 1B</summary>
+
+#### Knowledge & Reasoning (models up to 1B)
+
+| Benchmark | **Edge 0.4B** | vLLM-SR d3-lite | Crivo-150M | Sieve 0.8B | tasksource nano v4 | Saina Helm 2.0 | jiwo 0.8B v2 | DecisionTune 1.0 | vLLM-SR d3-edge | d1-omni-600M | GLiNER2.5-Decide |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| GPQA Diamond ★ | 10.2 | 10.9 | 0.0 | **15.0** | 0.0 | 3.4 | 1.4 | 0.0 | 4.1 | 0.0 | 0.0 |
+| GSM8K | 2.1 | **29.9** | 2.0 | 24.0 | 7.5 | 13.5 | 7.1 | 2.0 | 21.6 | 1.1 | 0.2 |
+| ChessBench | **10.7** | 0.2 | 0.0 | 1.1 | 1.6 | 9.6 | 0.0 | 0.0 | 0.0 | 5.2 | 0.0 |
+| MuSR | **34.7** | 33.0 | 26.7 | 29.2 | 32.4 | 18.0 | 31.5 | 20.5 | 20.3 | 1.5 | 12.9 |
+| SATA-Bench | **24.5** | 18.6 | 0.0 | 13.0 | 8.4 | 14.7 | 21.0 | 1.7 | 10.2 | 6.2 | 0.0 |
+| CRUXEval | 0.0 | 0.0 | 7.3 | 0.0 | 8.1 | 0.0 | 0.0 | 5.9 | **9.8** | 6.2 | 5.1 |
+| CLadder | 42.2 | 22.1 | **61.0** | 6.5 | 0.6 | 3.2 | 2.9 | 44.8 | 8.1 | 0.0 | 0.2 |
+| HLE ★ | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | **0.4** | 0.0 | **0.4** |
+| MMLU-Pro ★ | 20.5 | **24.0** | 3.4 | 17.8 | 5.7 | 9.8 | 20.2 | 1.7 | 13.5 | 10.1 | 5.6 |
+| BBH fixed-option tasks ★ | **28.1** | 22.5 | 11.6 | 20.4 | 6.8 | 16.2 | 24.4 | 9.4 | 10.6 | 17.7 | 0.2 |
+| WinoGrande ★ | **60.4** | 41.8 | 21.9 | 28.5 | 16.8 | 8.3 | 10.7 | 43.8 | 23.6 | 20.9 | 1.3 |
+
+#### Language Understanding (models up to 1B)
+
+| Benchmark | **Edge 0.4B** | vLLM-SR d3-lite | Crivo-150M | Sieve 0.8B | tasksource nano v4 | Saina Helm 2.0 | jiwo 0.8B v2 | DecisionTune 1.0 | vLLM-SR d3-edge | d1-omni-600M | GLiNER2.5-Decide |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| ContractNLI | 69.6 | 33.8 | 74.7 | 62.6 | 63.7 | **78.2** | 69.0 | 72.2 | 37.1 | 51.1 | 0.0 |
+| ANLI ★ | **42.6** | 24.1 | 19.0 | 15.0 | 18.7 | 4.7 | 20.8 | 0.0 | 11.3 | 4.1 | 0.0 |
+| HellaSwag ★ | 77.1 | 76.9 | 48.9 | 67.5 | 52.4 | 25.9 | 65.8 | **78.5** | 45.3 | 46.6 | 9.7 |
+| ACOS | 13.3 | 3.5 | **38.9** | 7.2 | 30.9 | 5.0 | 6.9 | 3.3 | 4.8 | 0.0 | 0.0 |
+| FinEntity | **86.4** | 82.5 | 76.7 | 48.9 | 62.5 | 63.5 | 72.9 | 77.7 | 71.3 | 45.5 | 50.2 |
+| iSarcasmEval | **37.1** | 13.7 | 0.0 | 0.0 | 0.0 | 21.0 | 4.6 | 0.0 | 12.2 | 6.7 | 13.0 |
+| VAST | 59.6 | 33.1 | **61.4** | 5.7 | 59.9 | 15.5 | 13.5 | 4.0 | 17.9 | 8.2 | 2.9 |
+| NLI4CT | **45.2** | 34.0 | 15.1 | 26.6 | 17.0 | 21.8 | 27.6 | 0.8 | 15.8 | 9.0 | 0.0 |
+| RAGTruth response-level hallucination | **58.6** | 0.0 | 44.3 | 0.0 | 35.5 | 55.7 | 41.1 | 28.9 | 0.0 | 0.0 | 0.0 |
+
+#### Retrieval & Classification (models up to 1B)
+
+| Benchmark | **Edge 0.4B** | vLLM-SR d3-lite | Crivo-150M | Sieve 0.8B | tasksource nano v4 | Saina Helm 2.0 | jiwo 0.8B v2 | DecisionTune 1.0 | vLLM-SR d3-edge | d1-omni-600M | GLiNER2.5-Decide |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| BANKING77 ★ | 90.7 | 90.2 | 90.6 | 85.5 | 88.8 | 84.4 | 60.2 | **92.1** | 89.8 | 83.5 | 65.2 |
+| CLINC150+OOS ★ | 92.2 | **93.9** | 91.8 | 90.5 | 86.6 | 75.9 | 81.5 | 91.8 | 91.9 | 85.2 | 60.2 |
+| BRIGHT ★ | 22.4 | 28.4 | 18.6 | 18.0 | 18.3 | 5.1 | **30.7** | 7.8 | 22.1 | 0.0 | 9.6 |
+| Amazon ESCI | **39.3** | 26.4 | 29.8 | 26.3 | 22.6 | 32.1 | 36.5 | 37.3 | 11.6 | 8.8 | 3.8 |
+| PhishNChips phishing decisions | **86.8** | 9.7 | 4.0 | 85.3 | 2.9 | 0.0 | 31.2 | 0.2 | 35.7 | 0.0 | 0.0 |
+| HoVer claim verification | 67.6 | 63.5 | **69.4** | 17.1 | 67.9 | 58.3 | 17.2 | 29.6 | 62.1 | 19.6 | 1.4 |
+
+#### Tools & Automation (models up to 1B)
+
+| Benchmark | **Edge 0.4B** | vLLM-SR d3-lite | Crivo-150M | Sieve 0.8B | tasksource nano v4 | Saina Helm 2.0 | jiwo 0.8B v2 | DecisionTune 1.0 | vLLM-SR d3-edge | d1-omni-600M | GLiNER2.5-Decide |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| BFCL ★ | **87.2** | 81.1 | 79.0 | 78.8 | 65.0 | 83.9 | 78.2 | 73.6 | 62.9 | 29.3 | 27.4 |
+| ToolRet | 47.9 | 55.5 | **55.7** | 55.6 | 55.1 | 53.2 | 55.0 | 49.7 | 50.4 | 2.6 | 28.6 |
+| API-Bank ★ | **82.5** | 56.1 | 29.4 | 54.0 | 25.6 | 74.7 | 17.5 | 45.2 | 39.0 | 38.8 | 5.9 |
+| Home appliance simulator | 12.5 | 11.4 | **79.5** | 0.0 | 0.0 | 0.0 | 8.0 | 0.0 | 0.0 | 0.0 | 0.0 |
+| When2Call MCQ | 49.7 | **72.8** | 62.5 | 61.6 | 71.4 | 71.2 | 39.5 | 58.7 | 57.8 | 21.0 | 11.2 |
+
+#### Arts & Human Taste (models up to 1B)
+
+| Benchmark | **Edge 0.4B** | vLLM-SR d3-lite | Crivo-150M | Sieve 0.8B | tasksource nano v4 | Saina Helm 2.0 | jiwo 0.8B v2 | DecisionTune 1.0 | vLLM-SR d3-edge | d1-omni-600M | GLiNER2.5-Decide |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| BPoMP | **82.4** | 43.2 | 0.0 | 12.1 | 18.9 | 0.0 | 0.7 | 18.4 | 8.9 | 0.0 | 14.0 |
+| Humicroedit | **27.6** | 14.7 | 4.5 | 16.6 | 0.0 | 5.6 | 7.1 | 0.0 | 6.7 | 2.3 | 1.3 |
+| POP909-CL | **23.1** | 0.9 | 1.2 | 0.6 | 0.8 | 0.0 | 0.0 | 0.6 | 0.4 | 0.1 | 0.9 |
+| cfcolor | 12.1 | 12.8 | 0.6 | 13.5 | 4.0 | 15.2 | **24.1** | 0.0 | 5.0 | 9.3 | 6.4 |
+| Habermas Machine | **33.8** | 9.9 | 10.1 | 14.4 | 3.3 | 0.0 | 10.4 | 8.9 | 4.3 | 7.5 | 20.8 |
+| New Yorker caption matching | **52.6** | 51.9 | 44.1 | 24.0 | 50.0 | 16.9 | 31.1 | 6.2 | 45.8 | 20.0 | 15.2 |
+
+</details>
+
+### KGJevBench
+
+Decision tasks from the GLiClass benchmark suite (KGJevBench): policy decisions, instruction following,
+knowledge-graph walking, tool calling and routing, judging, and typed Jev-style decisions. GLiClass models were run
+with the suite's `run_sample_500.sh` (up to 500 examples per task, bf16); the 12-relations row uses
+`eval_kg_reasoning.py --mode stepwise --pad-relations 12`. The other columns are the KGJevBench comparison results for
+those models on the same tasks and metrics; they were not re-run here. All values ×100; the best value in each row is bold.
+Averages are recomputed from the cells shown, so the reference columns can differ by up to 0.3 from averages computed
+on unrounded scores.
+
+| Benchmark (metric) | **Edge 0.4B** | **9B** | **27B** | pplx-decider-27b | glide | Jev-1.1B | clef | d1 | clef-flash | gpt-6-luna | Kev-4B | DeBERTa |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| fast-decisions (domain avg) | 54.8 | 53.7 | 54.3 | 59.5 | 61.7 | 61.2 | 59.4 | **61.8** | 57.3 | 60.2 | 58.5 | 56.7 |
+| task routing (top-1 primary) | **93.3** | 88.3 | 88.3 | 82.0 | 73.0 | 74.0 | 85.0 | 75.0 | 77.3 | 53.7 | 65.7 | 68.7 |
+| tool calling (exact set) | **89.3** | 88.3 | 89.0 | 88.0 | 79.0 | 65.3 | 83.0 | 72.3 | 78.3 | 46.7 | 59.0 | 88.3 |
+| instruction following (group acc) | 86.5 | 85.6 | 87.4 | 92.8 | 94.6 | 93.7 | **95.5** | 93.7 | 91.9 | 92.8 | 82.9 | 42.3 |
+| KG step-by-step (path acc) | 98.3 | 97.7 | 96.3 | **98.7** | 91.0 | 95.7 | 92.7 | 97.0 | 91.0 | 90.7 | 86.7 | 67.0 |
+| KG step-by-step, 12 relations/step | 93.0 | 94.7 | 93.3 | **95.3** | 82.0 | 85.7 | 87.7 | 87.0 | 85.3 | 79.7 | 73.7 | 51.7 |
+| KG path ranking (Hits@1) | 98.7 | 98.7 | **100.0** | 95.0 | 94.0 | 87.7 | 87.0 | 81.3 | 56.0 | 92.0 | 67.7 | 50.0 |
+| triple relevance (F1, keyword) | **90.6** | 84.7 | 83.2 | 31.8 | 33.0 | 30.7 | 28.4 | 28.4 | 21.9 | 21.2 | 47.3 | 69.5 |
+| reranking (R@1) | 92.7 | 95.7 | 94.3 | 96.7 | 96.0 | 96.7 | 94.7 | 95.7 | 96.7 | **97.0** | 93.3 | 81.7 |
+| NLI (accuracy, yes/no) | 89.2 | 96.9 | **100.0** | 90.7 | 96.0 | 98.8 | 92.0 | 94.1 | 92.6 | 89.5 | 93.2 | 85.5 |
+| fact checking (acc, checkable) | 87.0 | 94.3 | 98.0 | **99.3** | 93.5 | 98.0 | 92.7 | 97.3 | 94.7 | 97.7 | 83.3 | 86.3 |
+| moderation rules (acc, yes/no) | 85.6 | 81.2 | 92.4 | 93.3 | **94.0** | 92.3 | 89.0 | 82.0 | 85.7 | 85.0 | 77.3 | 78.3 |
+| policy decisions (acc) | 64.0 | 94.0 | 96.3 | **97.3** | 97.0 | 96.7 | 95.7 | 95.3 | 84.0 | 89.7 | 81.3 | 72.0 |
+| logical reasoning MCQA (acc; chance 25) | 43.7 | 70.0 | 75.7 | **90.0** | 83.0 | 81.3 | 76.0 | 79.3 | 71.3 | 65.0 | 63.0 | 38.0 |
+| web actions (step acc) | 51.0 | 82.0 | **85.0** | 79.0 | 73.0 | 72.0 | 78.0 | 71.0 | 60.0 | 55.0 | 59.0 | 47.0 |
+| LogiQA (acc; chance 25) | 42.4 | 59.0 | 65.6 | 72.0 | 65.0 | **77.1** | 63.9 | 69.4 | 60.2 | 61.1 | 50.8 | 31.5 |
+| MedMCQA (acc; chance 25) | 34.8 | 60.2 | 67.2 | **74.6** | 67.0 | 74.5 | 69.1 | 72.5 | 66.5 | 72.3 | 54.0 | 28.0 |
+| OpenBookQA closed (acc; chance 25) | 75.6 | 88.2 | 89.6 | **97.0** | 93.0 | 95.2 | 95.6 | 95.0 | 93.6 | 84.8 | 82.0 | 47.0 |
+| CommonsenseQA (acc; chance 20) | 78.6 | 81.6 | 86.2 | 88.1 | 88.0 | 89.0 | 90.3 | 86.3 | **93.6** | 81.5 | 76.9 | 58.9 |
+| JevBench public (acc) | 77.9 | 82.3 | 87.4 | 88.7 | 91.0 | 85.7 | 88.7 | 86.6 | 82.3 | **91.8** | 72.3 | 65.8 |
+| JevBench HF gold questions (acc) | 86.8 | 86.0 | 92.4 | **92.5** | 88.0 | 91.3 | 92.4 | 90.6 | 89.3 | 91.0 | 89.6 | 88.4 |
+| JevJudge text (skill ×100) | -0.1 | 17.5 | 32.2 | 43.9 | **52.4** | 42.2 | 37.8 | 38.5 | 23.5 | 40.1 | 24.5 | 11.2 |
+| Open-Jev (agreement) | 59.6 | 75.1 | 82.4 | **89.6** | 85.9 | 78.3 | 78.3 | 81.0 | 74.1 | 78.1 | 71.2 | 57.8 |
+| tasksource-Jev (agreement) | 78.4 | 78.6 | 78.8 | **83.9** | 75.0 | 73.6 | 73.3 | 75.0 | 68.9 | 66.9 | 65.1 | 53.8 |
+| Jev Distill (agreement w/ teacher) | 51.5 | 54.8 | 61.0 | 63.7 | 57.9 | **72.7** | 62.0 | 65.1 | 62.3 | 67.0 | 57.4 | 42.7 |
+| Behavior (present-class F1) | 37.6 | 56.7 | 68.3 | 68.9 | **87.5** | 67.0 | 66.0 | 67.4 | 52.6 | 57.4 | 46.3 | 27.7 |
+| typed decisions (acc) | 49.3 | 64.2 | 68.5 | 73.9 | 71.8 | 74.1 | 71.4 | **74.2** | 70.2 | 69.3 | — | 51.6 |
+| agent tool decisions (macro acc) | 57.1 | 78.5 | **87.6** | 85.4 | 81.8 | 82.9 | 73.9 | 83.4 | 64.1 | 75.2 | — | 40.3 |
+| *Average, 26 rows all models have* | 70.8 | 78.7 | 82.5 | 82.8 | 80.4 | 79.9 | 79.0 | 78.4 | 73.5 | 73.4 | 68.5 | 57.5 |
+| *Average, all 28 rows* | 69.5 | 78.2 | 82.2 | 82.6 | 80.2 | 79.8 | 78.6 | 78.4 | 73.0 | 73.3 | — | 56.7 |
+
+Several KGJevBench sets are written in the formats GLiClass is trained on (for example triple relevance, task
+routing, and tool calling), and tasksource-Jev overlaps the training data; read those rows with that in mind.
+
+## Training and intended use
+
+Training had three stages:
+
+1. **Large-scale supervised training** on a mixture that recasts classification, NLI, QA, reranking,
+   extraction, tool calling, moderation, judging, and reasoning data as decisions over candidate labels,
+   with instructions, label descriptions where available. Single-label, multi-label, and scoring examples are mixed
+   in one run with a focal loss.
+2. **Reinforcement learning** with a PPO-style objective whose actions are the label decisions. Rewards
+   are task metrics (F1, accuracy, recall), with a value baseline, a clipped ratio, and optional KL and entropy terms.
+3. **Calibration**: with the backbone frozen, a small calibrator reads the text and label representations and logit statistics and predicts a per-pair temperature and bias, fitted by negative log-likelihood on held-out decision data.
+
+Use this model for zero-shot and instruction-following classification, scoring, reranking, natural language
+inference, knowledge graph and multi-step reasoning, guardrails, policy moderation, routing, and LLM-output
+judging. It returns label probabilities and does not generate text.
+
+## Limitations
+
+- Label wording, instructions, domain, input length, and thresholds affect predictions.
+- Knowledge-heavy and multi-step reasoning tasks are the main gap to the larger models: multi-step arithmetic (GSM8K), hard science and exam questions (GPQA, HLE, MMLU-Pro, MedMCQA), logic puzzles, and long agent-trace judging remain weak spots.
+- Results are strongest in English; quality on other languages is lower.
+- Calibrated scores are fitted on held-out data and can drift on domains far from training.
+- Treat outputs used for safety, moderation, legal, or medical decisions as signals for review, not final verdicts.
+
+## Citation
+
+```bibtex
+@misc{stepanov2026scxrouter,
+  title  = {SCX Router: Streaming Zero-Shot Model Selection with a Decoder-KV Classifier and a Real-World Task Ontology},
+  author = {Stepanov, Ihor and Smechov, Aleksandr and Shtopko, Mykhailo and Vodianytskyi, Dmytro and Lukashov, Oleksandr},
+  year   = {2026},
+  eprint = {2609.02292},
+  archivePrefix = {arXiv}
+}
+```