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 generation_config.json from autotrust/GEV-26B-Decide: direct link, hf CLI and curl.
- Browser
- Download file 208 Bytes
-
https://huggingface.co/autotrust/GEV-26B-Decide/resolve/main/generation_config.json
- Command line
-
hf download hf://autotrust/GEV-26B-Decide/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/autotrust/GEV-26B-Decide/resolve/main/generation_config.json
208 Bytes
| { | |
| "bos_token_id": 2, | |
| "do_sample": true, | |
| "eos_token_id": [ | |
| 1, | |
| 106, | |
| 50 | |
| ], | |
| "pad_token_id": 0, | |
| "temperature": 1.0, | |
| "top_k": 64, | |
| "top_p": 0.95, | |
| "transformers_version": "5.5.0.dev0" | |
| } | |