Any-to-Any
Transformers
Safetensors
gemma4
image-text-to-text
obliteratus
abliterated
uncensored
multimodal
bfloat16
refusal-reduction
Instructions to use KridgeDookie/Gemma-4-E4B-IT-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KridgeDookie/Gemma-4-E4B-IT-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("KridgeDookie/Gemma-4-E4B-IT-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS") model = AutoModelForMultimodalLM.from_pretrained("KridgeDookie/Gemma-4-E4B-IT-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c8ae0031942877ac104ab8f414e7fe8a4bb83afa835f60492e5832744b819e25
- Size of remote file:
- 2.27 GB
- SHA256:
- c09785c5b57249e8d0e8412cc4529cfb28d21d3f500c47af04396bdf879efe56
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.