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
Download videos/computer_use_shop.mp4 from autotrust/GEV-26B-Decide: direct link, hf CLI and curl.
- Browser
- Download file 102 kB
-
https://huggingface.co/autotrust/GEV-26B-Decide/resolve/main/videos/computer_use_shop.mp4
- Command line
-
hf download hf://autotrust/GEV-26B-Decide/videos/computer_use_shop.mp4
-
curl -L -o computer_use_shop.mp4 https://huggingface.co/autotrust/GEV-26B-Decide/resolve/main/videos/computer_use_shop.mp4
102 kB
- Xet hash:
- 1bbf63f053991986958957ffc54fac2c4c751e9f5fafb98f163983a640acb86a
- Size of remote file:
- 102 kB
- SHA256:
- 28d3bbbcebe0faa1508f7a63908af6c6b712cb1c0a0a66d4a9f42de4330c4ece
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