Instructions to use Crystalhavanvo/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Crystalhavanvo/model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Crystalhavanvo/model", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio
How to use Crystalhavanvo/model 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 Crystalhavanvo/model 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 Crystalhavanvo/model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Crystalhavanvo/model to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Crystalhavanvo/model", max_seq_length=2048, )
- Xet hash:
- b22e30d5005f673ef34e434e2f72bfaacf0539a3f9978141063ac32c544ca5de
- Size of remote file:
- 11.4 MB
- SHA256:
- 132c0fb88b2070b782a69e8833d01ab987b1198ec606df151512d91820abb758
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.