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)
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")# 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 model-00001-of-00002.safetensors from autotrust/GEV-26B-Decide: direct link, hf CLI and curl.
- Browser
- Download file 49.9 GB
-
https://huggingface.co/autotrust/GEV-26B-Decide/resolve/main/model-00001-of-00002.safetensors
- Command line
-
hf download hf://autotrust/GEV-26B-Decide/model-00001-of-00002.safetensors
-
curl -L -o model-00001-of-00002.safetensors https://huggingface.co/autotrust/GEV-26B-Decide/resolve/main/model-00001-of-00002.safetensors
49.9 GB
- Xet hash:
- 060a1348782427903c74f008d4a544cf7176de0ae0fa87bc06999fbf0e1715ef
- Size of remote file:
- 49.9 GB
- SHA256:
- 1127684971bbca40465435a5cad69d67ad603bf5e61c6dfd5561fae4a3bcfdb3
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