Text Classification
Transformers
Safetensors
English
gemma4
image-text-to-text
system-one
system-two
adaptive-thinking
typed-decisions
decision-model
calibrated-probabilities
jev
noul
choice
score
lora
mixture-of-experts
multimodal
vllm
Eval Results (legacy)
Eval Results
Instructions to use autotrust/GEV-26B-Decide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autotrust/GEV-26B-Decide with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autotrust/GEV-26B-Decide")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("autotrust/GEV-26B-Decide") model = AutoModelForMultimodalLM.from_pretrained("autotrust/GEV-26B-Decide", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model card: chance baselines and DI skill per Knowledge & Reasoning benchmark; HLE stays at chance; ChessBench truncation
Browse files
README.md
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@@ -92,22 +92,31 @@ the always-think gain while thinking on 48 % of the questions. Thinking length:
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The area's benchmarks run through the server (`POST /v1/decide`, `thinking: "auto"`, threshold 0.8, budget 8,192 thinking
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tokens), scored with the Decision Index kit. Eight of the ten benchmarks were run to completion:
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| MMLU-Pro (first 1,611 of 12,032 questions) |
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### Latency
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## Limitations
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* Where the teacher is wrong, System 1 often is too
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* Thinking is slow on hard inputs: on expert-level and chess questions it often uses the full 8,192-token budget, and
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the adaptive mode does not meet the Decision Index latency limit on the Knowledge & Reasoning benchmarks.
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* The Decision Index score above combines adaptive runs of eight Knowledge & Reasoning benchmarks with System 1 runs of
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The area's benchmarks run through the server (`POST /v1/decide`, `thinking: "auto"`, threshold 0.8, budget 8,192 thinking
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tokens), scored with the Decision Index kit. Eight of the ten benchmarks were run to completion:
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| benchmark | chance | System 1 | **adaptive** | DI skill, System 1 → adaptive | questions that thought |
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| GPQA Diamond | 25.0 | 42.9 | **78.6** | 0.238 → **0.714** | 87 % |
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| CRUXEval | 37.0 | 67.5 | **90.7** | 0.485 → **0.853** | 42 % |
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| CLadder | 50.0 | 71.0 | **86.6** | 0.420 → **0.732** | 51 % |
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| GSM8K | 25.0 | 97.6 | **99.1** | 0.969 → 0.989 | 5 % |
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| SATA-Bench (case exact) | 1.3 | 34.2 | 35.5 | 0.334 → 0.346 | 21 % |
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| MuSR | 37.1 | 67.3 | 67.6 | 0.480 → 0.484 | 51 % |
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| ChessBench | 8.2 | 23.7 | 23.6 | 0.169 → 0.168 | 88 % |
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| HLE | 16.4 | 8.4 | 17.8 | 0.000 → 0.016 | 88 % |
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| MMLU-Pro (first 1,611 of 12,032 questions) | 11.1 | 59.2 | **80.1** | counted with System 1 | 68 % |
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| BBH | 31.0 | 75.2 | not run | counted with System 1 | — |
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Accuracy in %; "chance" is the kit's random baseline, and the Decision Index skill rescales accuracy so that chance is
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0 (below chance counts as 0). The Knowledge & Reasoning area skill rises from 0.430 to **0.551** with eight of ten
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benchmarks adaptive (MMLU-Pro and BBH counted with System 1).
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* **Real gains** on science, code and causal reasoning: GPQA Diamond, CRUXEval and CLadder.
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* **HLE only returns to chance.** System 1 is below chance (8.4 % against 16.4 %), and the questions where it is confident
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enough not to think are almost all wrong (1.6 %). Thinking brings HLE to 17.8 %, the level of guessing, so its skill
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stays near 0.
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* **Chess does not benefit.** Two thirds of the ChessBench thoughts hit the 8,192-token budget; the answer read from a
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truncated thought is no better than System 1 (17.0 % against 17.7 % on those questions), while finished thoughts gain
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a little (19.8 % → 22.7 %). Answers read from truncated thoughts are also over-confident, so the equal mix does not
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damp them.
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### Latency
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## Limitations
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* Where the teacher is wrong, System 1 often is too, and it can be confidently wrong: on HLE the questions it answers
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without thinking are 1.6 % correct. Adaptive thinking helps most on science, code, math and logic, does not help on
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chess or expert-level HLE questions, and can slightly lower accuracy on commonsense questions (CommonsenseQA 86.7 → 85.0).
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* Thinking is slow on hard inputs: on expert-level and chess questions it often uses the full 8,192-token budget, and
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the adaptive mode does not meet the Decision Index latency limit on the Knowledge & Reasoning benchmarks.
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* The Decision Index score above combines adaptive runs of eight Knowledge & Reasoning benchmarks with System 1 runs of
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