Instructions to use deeponh/Nepali_8b_8b_D3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deeponh/Nepali_8b_8b_D3 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("deeponh/Nepali_8b_8b_D3", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use deeponh/Nepali_8b_8b_D3 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 deeponh/Nepali_8b_8b_D3 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 deeponh/Nepali_8b_8b_D3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for deeponh/Nepali_8b_8b_D3 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="deeponh/Nepali_8b_8b_D3", max_seq_length=2048, )
- Xet hash:
- 528735ef1f6af0f8c94e4429e1d01cef219578f17047d21e03cd877e05d84bf1
- Size of remote file:
- 17.2 MB
- SHA256:
- 52716f60c3ad328509fa37cdded9a2f1196ecae463f5480f5d38c66a25e7a7dc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.