---
license: apache-2.0
base_model: google/gemma-4-E2B-it
tags:
  - decision-model
  - system-1
  - rlcd
  - proper-scoring-rules
  - gemma
  - classone
  - classification
pipeline_tag: text-classification
---

# classone-gemma4-e2b — ClassOne System 1 Decision Model

**[devops-thiago/classone-gemma4-e2b](https://huggingface.co/devops-thiago/classone-gemma4-e2b)** 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) | **0.0064** | **0.0009** | **44.8 ms** |
| **Original** | 72 | **90.3%** (65/72) | **0.0972** | **0.1015** | **42.9 ms** |
| **Hard** | 111 | **44.1%** (49/111) | 0.4681 | 0.4813 | **90.0 ms** |
| **Overall Aggregate** | **231** | **70.1%** (162/231) | - | - | **44.8 ms** |

- **Easy Tier Sub-Breakdown:** Choice accuracy: **100.0%** (36/36); Noul policy accuracy: **100.0%** (12/12). Flawless 0.0064 ECE.
- **Original Tier Sub-Breakdown:** Noul accuracy: **100.0%** (24/24); Score rubrics: **100.0%** (12/12); Choice accuracy: **80.6%** (29/36).
- **Hard Tier Sub-Breakdown:** Noul policy compliance: **52.6%** (20/38); Choice accuracy: **43.3%** (29/67).

### 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) |
|---|---|---|---|---|---|
| **Power Seeking** | 6 | **0.889** | **83.3%** | 0.2575 | 209.6 ms |
| **Honesty (Deception)** | 11 | 0.500 | **72.7%** | 0.3906 | 216.8 ms |
| **Concealing Uncertainty** | 14 | **0.673** | **71.4%** | **0.1800** | 127.8 ms |
| **Refusal (Jailbreaks)** | 11 | 0.500 | **63.6%** | **0.0974** | 267.5 ms |
| **Faithfulness** | 9 | **0.700** | **55.6%** | 0.2513 | 200.0 ms |
| **Bias** | 9 | **0.525** | **55.6%** | 0.2141 | 217.9 ms |
| **Overall Balanced Accuracy** | **100** | **0.505** | **56.2%** | **0.1944** | **199.2 ms** |

### 3. Agentic Tool-Routing & Adversarial Safety Suites (2026 Benchmarks)

| Benchmark Suite | Split / Samples | Accuracy | Secondary Metric | Calibration ECE | Latency (p50) |
|---|---|---|---|---|---|
| **WildGuardTest** (`walledai/WildGuardTest`) | `test` (100 stratified) | **72.00%** (72.04% BalAcc) | **0.7993 AUROC** (**78.6%** Vanilla, **72.9%** Benign Spec) | **0.1597** | 43.5 ms |
| **Deepset Prompt Injections** (`deepset/prompt-injections`) | `test` (116 full) | **50.86%** (59/116) | **0.6214 AUROC** (**75.0%** Injection Recall) | 0.3412 | 41.6 ms |
| **NVIDIA When2Call** (`nvidia/When2Call`) | `mcq` (100 stratified) | **35.00%** | **85.3%** `tool_call` (29/34) | 0.5376 | 106.8 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-e2b"

# 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-E2B-it](https://huggingface.co/google/gemma-4-E2B-it) (Google) — Apache License 2.0
- Architecture & training code: [devops-thiago/class-one](https://github.com/devops-thiago/class-one) — Apache 2.0