Instructions to use DungND1107/gemma_sft_ft_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DungND1107/gemma_sft_ft_v2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DungND1107/gemma_sft_ft_v2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use DungND1107/gemma_sft_ft_v2 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 DungND1107/gemma_sft_ft_v2 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 DungND1107/gemma_sft_ft_v2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for DungND1107/gemma_sft_ft_v2 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="DungND1107/gemma_sft_ft_v2", max_seq_length=2048, )
- Xet hash:
- f6aa6b6d77b66c0b5905517df9e5261c32195192839dd05e71e37d6b89933074
- Size of remote file:
- 4.69 MB
- SHA256:
- 1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.