Instructions to use rfvasile/LinalgZero-SFT-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rfvasile/LinalgZero-SFT-LoRA with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rfvasile/LinalgZero-SFT-LoRA", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use rfvasile/LinalgZero-SFT-LoRA 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 rfvasile/LinalgZero-SFT-LoRA 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 rfvasile/LinalgZero-SFT-LoRA to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for rfvasile/LinalgZero-SFT-LoRA to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="rfvasile/LinalgZero-SFT-LoRA", max_seq_length=2048, )
- Xet hash:
- 710aab30d23be6850c91cc1c97b6c84f8e0ab24ce338ef2df049afcf747dc2f9
- Size of remote file:
- 11.4 MB
- SHA256:
- 39ac9e86a0c509c19d06b992db12a1fc80e4f4d3e9b44ca3a63fd303e8b79bc9
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