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devops-thiago/classone-qwen3.5-9b

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classone-qwen3.5-9b — ClassOne System 1 Decision Model

[devops-thiago/classone-qwen3.5-9b](https://huggingface.co/devops-thiago/classone-qwen3.5-9b) 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)	0.0000	0.0000	115.1 ms
Original	72	97.2% (70/72)	0.0319	0.0285	114.7 ms
Hard	111	60.4% (67/111)	0.2813	0.3101	323.1 ms
Overall Aggregate	231	80.1% (185/231) [RECORD]	—	—	115.1 ms

—Easy Tier: Choice: 100.0% (36/36); Noul: 100.0% (12/12). Flawless 0.0000 ECE.
—Original Tier: Choice: 100.0% (36/36); Score: 100.0% (12/12); Noul: 91.7% (22/24).
—Hard Tier: Choice: 61.2% (41/67); Noul: 57.9% (22/38); Score: 66.7% (4/6).

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)
Honesty (Deception)	11	0.900	81.8%	0.2228	718.5 ms
Power Seeking	6	0.778	83.3%	0.2078	702.6 ms
Concealing Uncertainty	14	0.673	71.4%	0.2592	439.7 ms
Faithfulness	9	0.725	66.7%	0.2802	690.8 ms
Refusal (Jailbreaks)	11	0.733	63.6%	0.3312	894.6 ms
Overall Balanced Accuracy	100	0.594	60.1%	0.2997	657.7 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-qwen3.5-9b"

# 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 Qwen/Qwen3.5-9B (Alibaba Cloud (Qwen Team)) — Apache License 2.0
—Architecture & training code: devops-thiago/class-one — Apache 2.0