Instructions to use Majid88/lora_modelGK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Majid88/lora_modelGK with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Majid88/lora_modelGK", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Majid88/lora_modelGK 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 Majid88/lora_modelGK 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 Majid88/lora_modelGK to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Majid88/lora_modelGK to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Majid88/lora_modelGK", max_seq_length=2048, )
- Xet hash:
- 4008a1977434634ab3f6c4f903177a31a4e7768dde0dfec38e61b59e7b163106
- Size of remote file:
- 269 MB
- SHA256:
- 88794bafa2f24827ba2a3c718358e2e4aa12485c80e49b22dc3998103e8f10c5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.