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-00002-of-00002.safetensors from autotrust/GEV-26B-Decide: direct link, hf CLI and curl.
- Browser
- Download file 1.7 GB
-
https://huggingface.co/autotrust/GEV-26B-Decide/resolve/main/model-00002-of-00002.safetensors
- Command line
-
hf download hf://autotrust/GEV-26B-Decide/model-00002-of-00002.safetensors
-
curl -L -o model-00002-of-00002.safetensors https://huggingface.co/autotrust/GEV-26B-Decide/resolve/main/model-00002-of-00002.safetensors
1.7 GB
- Xet hash:
- 407c22ff8f9884674a186f8568d150a0c63a9f21ec6eea92e4aedd647e43e062
- Size of remote file:
- 1.7 GB
- SHA256:
- aab47033e1e8a492ef8e581efae1cf36478d0433567e7729b3c1728bc8970db7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.