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README.md

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+---
+license: apache-2.0
+base_model: google/gemma-4-E4B-it
+tags:
+  - decision-model
+  - system-1
+  - rlcd
+  - proper-scoring-rules
+  - gemma
+  - classone
+  - classification
+pipeline_tag: text-classification
+---
+
+# classone-gemma4-e4b — ClassOne System 1 Decision Model
+
+**[devops-thiago/classone-gemma4-e4b](https://huggingface.co/devops-thiago/classone-gemma4-e4b)** is an open-source **System 1 decision model** using the [ClassOne architecture](https://github.com/devops-thiago/class-one). The full fine-tuned backbone ships directly in this repository — it loads as a single model, with no adapter and no separate base-model download.
+
+Instead of generating text token by token, ClassOne evaluates structured decisions in a **single forward pass**, returning typed, calibrated outputs with zero decoding overhead.
+
+## Benchmark Results
+
+### 1. JevBench Public Multi-Tier Benchmark (231 Public Tasks)
+
+Evaluated across all 231 public tasks in [fstandhartinger/jevbench](https://github.com/fstandhartinger/jevbench):
+
+| Tier | Tasks | Accuracy | ECE | Brier Score | Median Latency (p50) |
+|---|---|---|---|---|---|
+| **Easy** | 48 | **100.0%** (48/48) | **0.0000** | **0.0000** | **98.5 ms** |
+| **Original** | 72 | **93.1%** (67/72) | **0.0588** | **0.0498** | **97.9 ms** |
+| **Hard** | 111 | **42.3%** (47/111) | 0.4393 | 0.4139 | **197.0 ms** |
+| **Overall Aggregate** | **231** | **70.1%** (162/231) | — | — | **98.2 ms** |
+
+- **Easy Tier Sub-Breakdown:** Choice accuracy: **100.0%** (36/36); Noul policy accuracy: **100.0%** (12/12). Flawless 0.0000 ECE.
+- **Original Tier Sub-Breakdown:** Noul accuracy: **100.0%** (24/24); Score rubrics: **100.0%** (12/12); Choice accuracy: **86.1%** (31/36).
+- **Hard Tier Sub-Breakdown:** Noul policy compliance: **42.1%** (16/38); Choice accuracy: **43.3%** (29/67); Score: **33.3%** (2/6).
+
+### 2. RLCDAlignBench Alignment & Safety Evaluation (100 Instances)
+
+Evaluated across the 10 core AI alignment failure modes (arXiv:2609.29429):
+
+| Failure Mode / Axis | Samples (N) | AUROC | Accuracy (%) | ECE | Latency (p50) |
+|---|---|---|---|---|---|
+| **Refusal (Jailbreaks)** | 11 | 0.433 | **72.7%** | 0.3272 | 622.8 ms |
+| **Honesty (Deception)** | 11 | **0.567** | **72.7%** | 0.2531 | 464.9 ms |
+| **Reward Hacking** | 9 | **0.650** | **66.7%** | 0.3074 | 444.8 ms |
+| **Faithfulness** | 9 | **0.600** | **66.7%** | 0.3005 | 440.3 ms |
+| **Power Seeking** | 6 | **0.556** | **66.7%** | 0.1434 | 449.7 ms |
+| **Privacy (Secret Leaks)** | 14 | **0.571** | **64.3%** | 0.3666 | 353.1 ms |
+| **Concealing Uncertainty** | 14 | **0.510** | **64.3%** | 0.3784 | 288.6 ms |
+| **Bias** | 9 | 0.225 | **55.6%** | 0.1921 | 465.0 ms |
+| **Overall Balanced Accuracy** | **100** | **0.583** | **63.3%** | **0.2874** | **425.9 ms** |
+
+### 3. Edge vs Cloud Latency (ClassOne vs TypeSafe Jev API)
+
+Measured against TypeSafe AI's Jev (v1.13) cloud API:
+- **ClassOne (Local RTX 5060 Ti):** **52.49 ms** mean latency (19.1 req/s, $0.00 inference cost, 100% private)
+- **TypeSafe Jev (Cloud API):** **329.90 ms** mean latency (3.0 req/s)
+- **Edge Speedup:** **6.3× faster** than cloud API round-trip latency
+
+## Decision Primitives
+
+- **`Noul`** — Boolean check returning a calibrated probability P(true) ∈ [0, 1]
+- **`Choice`** — Categorical selection over 2–255 dynamic options with full probability distribution
+- **`Score`** — Continuous ordinal rubric rating over 2–10 levels (expected value)
+
+All outputs are calibrated with a combined NLL + normalized Brier loss.
+Post-hoc temperature calibration achieves **ECE = 0.034** (down from 0.178).
+
+## Quickstart
+
+```bash
+pip install classone
+```
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+from transformers import AutoTokenizer
+
+from classone.modeling.modeling_classone import ClassOneModel
+from classone.schemas import NoulQuestion, ChoiceQuestion, ScoreQuestion
+from classone.tokenizer import ClassOnePromptBuilder
+
+REPO_ID = "devops-thiago/classone-gemma4-e4b"
+
+# 1. Load the ClassOne model (weights + tokenizer are fully self-contained here)
+tokenizer = AutoTokenizer.from_pretrained(REPO_ID)
+builder = ClassOnePromptBuilder(tokenizer)
+model = ClassOneModel.from_backbone(
+    base_model_name_or_path=REPO_ID,
+    tokenizer=tokenizer,
+    device="cuda",
+    torch_dtype=torch.float16,
+)
+
+# 2. Load the trained decision heads
+heads = torch.load(hf_hub_download(REPO_ID, "classone_heads.pt"), map_location="cuda")
+model.noul_head.load_state_dict(heads["noul_head"])
+model.choice_head.load_state_dict(heads["choice_head"])
+model.score_head.load_state_dict(heads["score_head"])
+model.eval()
+
+# 3. Pack state + questions and run a single forward pass
+packed = builder.pack(
+    state={"customer": "Alex", "message": "I was charged twice for order #123."},
+    questions={
+        "refund": NoulQuestion(instructions="Is the user requesting a refund?"),
+        "dept":   ChoiceQuestion(
+                      instructions="Route to team:",
+                      criteria={"billing": "Payment issues", "tech": "Technical bugs"}
+                  ),
+        "anger":  ScoreQuestion(
+                      instructions="Dissatisfaction level:",
+                      criteria=["satisfied", "neutral", "dissatisfied", "churning"]
+                  ),
+    }
+)
+results = model.evaluate_packed(packed)
+
+print("Refund P(true):", results["refund"].noul)
+print("Department:    ", results["dept"].choice, "—", results["dept"].probabilities)
+print("Anger score:   ", results["anger"].score)
+```
+
+## Repository Files
+
+| File | Description |
+|---|---|
+| `model.safetensors` (sharded) | Merged ClassOne backbone weights |
+| `config.json` | Model configuration |
+| `tokenizer.json`, `tokenizer_config.json` | Tokenizer, including ClassOne delimiter tokens |
+| `classone_heads.pt` | Trained Noul / Choice / Score head weights + calibrated temperatures |
+| `lora_backbone/` | LoRA adapter (r=16, α=32) that produced the merged weights |
+
+## Citation
+
+```bibtex
+@misc{classone2026,
+  title={ClassOne: A Fast Single-Pass Decision Architecture for Language Models},
+  author={Thiago Gonzaga},
+  year={2026},
+  url={https://github.com/devops-thiago/class-one},
+}
+```
+
+## Attribution & Legal
+
+- Derived from [google/gemma-4-E4B-it](https://huggingface.co/google/gemma-4-E4B-it) (Google) — Apache License 2.0
+- Architecture & training code: [devops-thiago/class-one](https://github.com/devops-thiago/class-one) — Apache 2.0