classone-qwen3.5-2b — ClassOne System 1 Decision Model

devops-thiago/classone-qwen3.5-2b is an open-source System 1 decision model using the ClassOne architecture. 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:

Tier	Tasks	Accuracy	ECE	Brier Score	Median Latency (p50)
Easy	48	100.0% (48/48)	0.0014	0.0000	41.9 ms
Original	72	91.7% (66/72)	0.0655	0.0545	40.3 ms
Hard	111	38.7% (43/111)	0.5176	0.5171	83.6 ms
Overall Aggregate	231	68.0% (157/231)	—	—	40.3 ms

Original Choice Record: 88.9% (32/36); Score: 100.0% (12/12); Noul: 91.7% (22/24).
Latency Advantage: 40.3 ms median latency across real-time decisions.

2. RLCDAlignBench Alignment & Safety Evaluation (100 Instances)

Failure Mode / Axis	Samples (N)	AUROC	Accuracy (%)	ECE	Latency (p50)
Faithfulness	9	0.800	55.6%	0.3486	184.2 ms
Honesty (Deception)	11	0.767	72.7%	0.2207	188.9 ms
Concealing Uncertainty	14	0.571	64.3%	0.3795	115.3 ms
Overall Balanced Accuracy	100	0.568	58.0%	0.3261	173.2 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

pip install classone

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-qwen3.5-2b"

# 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

@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},
}