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
Commit History
20 days ago
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Add robot-arm and computer-use demos (videos, results) to the model card
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21 days ago
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Model card: Watch it think (Minesweeper, Connect Four, Wordle, Sudoku videos); one-move puzzles and games with and without thinking
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Model card: thinking on Knowledge & Reasoning only; sample of five other-area benchmarks with adaptive thinking
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Decision Index 0.2.1 with adaptive thinking on all ten Knowledge & Reasoning benchmarks: 62.48 (MMLU-Pro 84.6, BBH 92.0); latency with and without speculative decoding
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Decision Index 0.2.1 with adaptive thinking (61.15; 8 of 10 Knowledge & Reasoning benchmarks adaptive), latency, reasoning controls (thinking default auto, think_budget, chat_template_kwargs)
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Model card: remove the System 1 Decision Index score until it is recomputed with adaptive thinking
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Add files using upload-large-folder tool
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