bpHigh/glance-0.1

Glance 0.1

Glance 0.1 is a 4B-class decision model built on Qwen3.5-4B (4.54B parameters: the base without its multi-token-prediction head). You give it a state (text, and optionally images) and one or more typed questions. It returns a calibrated probability distribution for each question from a single forward pass, with no generated text. That takes 53–58 ms per decision (p50) on one H100, and under half a second for short prompts on 8 CPU cores.

It is built for the decisions an application or agent makes many times a call: routing, triage, judging, moderation, extraction checks, tool and action selection, policy and workflow gates.

Status: research preview. The weights are for non-commercial research use (CC BY-NC 4.0). The Glance runtime that runs them is in runtime/ (MIT).

What it answers

Question type	You give	It returns
choice	2–255 labelled options, optionally with a definition for each	a probability per option
noul (yes/no)	a statement, optionally with what true and false mean	the probability it is true
score	an ordered list of levels	a probability per level and the expected score

Several questions about the same state are answered in one pass. Example request and answer:

{
  "state": {"ticket": "My card was charged twice for the same order, please refund one."},
  "questions": {
    "team": {"type": "choice", "instructions": "Which team should handle this ticket?",
             "criteria": {"billing": "payments, refunds, invoices", "tech": "bugs and outages", "sales": "new purchases"}},
    "urgent": {"type": "noul", "instructions": "The customer needs a reply today."},
    "frustration": {"type": "score", "instructions": "How frustrated is the customer?", "criteria": ["0", "1", "2", "3", "4"]}
  }
}

{
  "answers": {
    "team": {"type": "choice", "choice": "billing", "probabilities": {"billing": 0.964, "tech": 0.021, "sales": 0.015}, "confidence": 0.946},
    "urgent": {"type": "noul", "noul": 0.731},
    "frustration": {"type": "score", "score": 2.0, "probabilities": {"0": 0.007, "1": 0.301, "2": 0.438, "3": 0.195, "4": 0.059}, "confidence": 0.477}
  }
}

(This is Glance 0.1's output for the request above, recorded with the runtime on CPU and rounded. On a GPU the numbers differ slightly in floating point.)

How it works

• Base: Qwen3.5-4B, vision encoder included.
• Attention: two-way (bidirectional) attention in the full-attention layers, so the model reads the state and all questions before answering. The linear-attention layers are unchanged.
• Readout: every option is tagged with a two-letter answer code that is a single token. The answer distribution is the LM-head logits over those codes at each question's answer slot. Codes and the yes/no order are drawn per decision, so the model cannot learn position shortcuts.
• Calibration: per-bucket temperatures (question type × option count × modality), fitted on half of the held-out questions and tested on the other half. They are stored in decision_config.json and applied at inference.

Files

• lm/: the fine-tuned Qwen3.5-4B weights (bf16, 9.1 GB in safetensors shards with an index), tokenizer, chat template and image processor.
• decision_config.json: the answer codes and their token ids, attention mode, readout and calibration temperatures.
• results/: the evaluation results below in full (scores and per-task tables; no benchmark items).
• runtime/: the Glance runtime (glance_runtime, MIT). It holds the prompt layout, two-way attention, answer-code readout and calibration, with a Python API and an HTTP server.

Loading lm/ with plain transformers gives the language model, but not the decision model. Use the runtime.

Quick start

pip install -U huggingface_hub
hf download bpHigh/glance-0.1 --include "runtime/*" --local-dir glance-0.1

On a GPU:

pip install "./glance-0.1/runtime[gpu]"

On a CPU-only machine, install the CPU build of PyTorch first. The default Linux build pulls several GB of CUDA libraries.

pip install torch --index-url https://download.pytorch.org/whl/cpu
pip install ./glance-0.1/runtime

The runtime needs Python 3.10 or later, torch, transformers 5.20.1 or later, and pillow. It was tested with torch 2.13 and transformers 5.20.1 on H100, and with torch 2.14.1 (CPU) and transformers 5.20.2 on CPU.

from glance_runtime import Glance

glance = Glance.load("bpHigh/glance-0.1", device="cuda")   # downloads the weights; or device="cpu"

answers = glance(
    state={"ticket": "My card was charged twice for the same order, please refund one."},
    questions={
        "team": {"type": "choice", "instructions": "Which team should handle this ticket?",
                 "criteria": {"billing": "payments, refunds, invoices", "tech": "bugs and outages",
                              "sales": "new purchases"}},
        "urgent": {"type": "noul", "instructions": "The customer needs a reply today."},
    },
)
answers["team"]["probabilities"]   # {"billing": ..., "tech": ..., "sales": ...}
answers["urgent"]["noul"]          # P(yes)

# images: paths, PIL images or data: URLs (downscaled to 640 px on the long side, as in training)
glance(state="Photo from the returns desk.", images=["photo.jpg"],
       questions={"damaged": {"type": "noul", "instructions": "The item in the photo is damaged."}})

Or serve POST /v1/systemone over HTTP:

python -m glance_runtime serve --model bpHigh/glance-0.1 --device cuda --port 8741
curl -s localhost:8741/v1/systemone -H 'Content-Type: application/json' -d @request.json

Each question gets its own sequence over the shared state, and the sequences run as one batched forward pass, so the other questions' text never enters a question's input. The answer codes are drawn from a hash of the whole request, so adding or changing a question can shift the other answers slightly. Answers are deterministic for a given request.

Training

Four stages:

Stage	Data	Method
1	about 1.55M decisions	full fine-tune, causal attention
2	about 556k harder and more diverse decisions	LoRA (r 64), two-way attention
3	about 300k decisions with images, plus text replay	LoRA, two-way attention
4	about 151k routing, judging and agent decisions, plus replay	LoRA, two-way attention

Each LoRA stage was merged before the next. That is about 1.9B training tokens in all.

Text sources:

• the tasksource collection of public classification and NLI sets;
• typed decisions;
• Eikos decisions;
• Open-Jev workflows and games;
• the official train splits of the Decision Index benchmarks;
• procedurally generated rule-book, numeric and game decisions;
• routing, judging and agent-outcome questions written over unlabelled conversations (WildChat, OpenAssistant, UltraChat) and agent traces.

Image sources: The Cauldron and FineVision subsets, Amazon Berkeley Objects, recognition datasets, visual inspection sets and rendered game screens.

Targets:

• gold labels, mixed with the distributions of a larger teacher decision model;
• for the unlabelled routing, judging and agent questions, the teacher's distributions alone.

Contamination: every training source was checked against the evaluation sets below with an 8-gram index, and a held-out manifest kept the held-out splits out.

Evaluation

All numbers are our own runs. None is a leaderboard submission yet.

DecisionBench (official rules)

A correct answer is one whose first arg-max is the gold candidate. An invalid answer counts as a miss, and there is no chance correction. Other models' scores are from the official DecisionBench 1.0 leaderboard (leaderboard.json, updated 9 Oct 2026; its newest submission is from 27 Sep). Glance 0.1's is our own run with the official scoring rules, not a board submission.

Model	Headline (22,700 non-reasoning rows)	Reasoning track (1,200 rows)
Bosun v3.1 1.7B	87.3	39.7
Bosun v3.1 0.6B	83.2	43.1
imajev-4b (revision 712891d)	79.9	68.0
imajev-4b (revision c9e5f13)	79.6	80.6
Glance 0.1	78.8	68.6
Winnow-12B	77.0	70.2
Juspay xor	76.6	66.1
GLM-5.3 Flash	73.7	87.1
Jev 1.13 (snapshot of 21 Sep)	71.9	74.5

• Bosun is in-domain: its authors score held-out rows of the task families it was trained on.
• Jev 1.14 (released 14 Oct) is not on the board yet.
• GLM-5.3 Flash's reasoning score covers 1,197 of the 1,200 rows.

Glance 0.1 by task family:

• Strong families: content moderation 100%, function and agent-skill routing 98.7%, safety gating 94%, bounded extraction 93%, routing triage 92%.
• Weak families: document classification 38%, record classification 47%, target selection 49%, rubric scoring 49%, review scoring 50%, retrieval ranking 54%.

Full tables: results/decision-bench.json, results/decision-bench-by-task.md.

JevBench, public items v1.2 (local run with a port of the v1.3/v1.4 scorer)

Tier	Accuracy
Easy (48 items)	100%
Standard (72 items)	100%
Hard (111 items)	61.3%

• Scores: Intelligence 75.7, Calibration 67.6; hard-tier ECE 0.194.
• Hard tier is the weak spot: temporal and numeric reasoning 26.7%, trade-offs 33%, long policies 52.6%.
• Strong hard categories: adversarial, routing and trap items are all at 100%.

Full tables: results/jevbench-public.json, results/jevbench-public-by-category.md.

Decision Index test splits, scored locally (unofficial)

This covers the full test splits of 15 of the 38 Decision Index benchmarks: 124,370 questions, scored with our harness and metrics. They are not the suite's selected rows, benchmark-specific metrics or area weights, so they are not comparable with the Decision Index board. The model was trained on these benchmarks' official train splits, so the scores are in-domain.

Overall: 83.0% accuracy, chance-corrected 74.3.

Range	Benchmarks
Strong	ARC-Easy 97.6%, ARC-Challenge 93.4%, HellaSwag 91.6%, BANKING77 90.8%, SGD 90.1%, ContractNLI 88.4%, CLINC150 86.6%
Weak	GSM8K as choices 58.9–66.6%, Humicroedit 61.1%, CLadder 64.6%, ANLI 67.9%, VAST 70.9%

Full table: results/decision-index-local.md.

Held-out splits of the training sources (in distribution)

Uncalibrated. cc_mean is chance-corrected accuracy: 100 is perfect, 0 is chance.

Held-out set	Questions	Accuracy	cc_mean	ECE
typed-decisions test	2,000	67.6%	61.2	0.024
Eikos held-out	1,191	91.2%	84.8	0.041
Open-Jev test	10,356	89.0%	78.8	0.041
Open-Jev out of distribution	15,701	85.3%	70.5	0.057

Calibration (5,220 test questions): NLL 0.337 → 0.309, ECE 0.040 → 0.020. Full tables: results/heldout.md, results/calibration.json.

Latency and hardware

The 231 JevBench public items, one decision at a time (batch 1), bf16, timed end to end:

State length (tokens)	Items	1× H100, p50	CPU (8 cores), p50
under 250	152	57 ms	0.42 s
250–500	26	59 ms	1.6 s
500–1,000	12	62 ms	3.2 s
1,000–2,000	5	65 ms	5.0 s
2,000–3,700	36	83 ms	12.3 s
All items: p50 / p95	231	58 / 92 ms	0.47 / 13.3 s

On the 124,370 Decision Index questions, which are mostly short, the H100 p50 was 53 ms and the p95 57 ms.

• H100: with the flash-linear-attention kernels (pip install "./runtime[gpu]"). Without them the linear-attention layers fall back to slower reference PyTorch code.
• CPU: 8 reserved cores of an AMD EPYC 9R45 (AVX-512 BF16), PyTorch 2.13, 8 threads, bf16. The fast linear-attention kernels are GPU-only, so CPU latency grows with prompt length. Short routing-style prompts take under half a second. Peak RAM with the model loaded was 9.1 GB.
• Numerics: CPU and GPU answers differ slightly in floating point. On these items the harness accuracy was 81.8% on CPU and 81.4% on H100.
• Other GPUs (consumer or data-centre cards other than H100) are not measured yet. The bf16 weights alone take 9.1 GB of memory.

Limitations

• One pass, no reasoning. Multi-step arithmetic, temporal, causal and trade-off questions are the weakest areas (JevBench hard, CLadder, GSM8K). A reasoning version is planned (below).
• Overconfident when hard. It is overconfident on hard out-of-distribution items (JevBench hard ECE 0.194), even after calibration.
• Weak DecisionBench families: document and record classification, ranking, and rubric or review scoring.
• Mostly English. Indic languages are not targeted yet.
• Needs the Glance runtime (runtime/): plain transformers loading gives only the language model.
• Not for consequential automated decisions about people without human review.

Coming next (TBD)

• Glance Indic: an Indic-friendly decision model.
• Glance Reasoning: a reasoning decision model that thinks only when its one-pass read is unsure.
• RL-pilled Glance: a decision model post-trained with reinforcement learning.
• Smaller and larger versions of Glance.

Licence

The weights are released under CC BY-NC 4.0, for non-commercial research use. The training data includes sources under non-commercial and research-only terms.

The runtime code in runtime/ is released under the MIT licence (runtime/LICENSE).

The base model is Qwen/Qwen3.5-4B, released by the Qwen team under Apache-2.0. Its licence and notices continue to apply to the base weights.