Instructions to use Dohahemdann/sparky-decoder-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dohahemdann/sparky-decoder-5 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Dohahemdann/sparky-decoder-5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Dohahemdann/sparky-decoder-5 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 Dohahemdann/sparky-decoder-5 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 Dohahemdann/sparky-decoder-5 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Dohahemdann/sparky-decoder-5 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Dohahemdann/sparky-decoder-5", max_seq_length=2048, )
- Xet hash:
- 567c1bb261f52d0e805a2494d012200e8b9c0dc049b8e415b1bd08d2e58f00fb
- Size of remote file:
- 1.47 kB
- SHA256:
- e85f07d78082d5d7fa14794f3416a1fc1c7671381b1b18457715db32951b5921
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.