Instructions to use rachfop/unclothe-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rachfop/unclothe-1 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rachfop/unclothe-1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use rachfop/unclothe-1 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for rachfop/unclothe-1 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for rachfop/unclothe-1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for rachfop/unclothe-1 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="rachfop/unclothe-1", max_seq_length=2048, )
- Xet hash:
- 0032069ac253a240be31868ef9d9688a2b82f90d6635f56af142ad49761a15a5
- Size of remote file:
- 97.3 MB
- SHA256:
- 0a67a88814135c2975427e4d7dbce241a8d15c617569bd65c192b89b1b6d1c87
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.