Instructions to use GinNoV111/outputs_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GinNoV111/outputs_lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("GinNoV111/outputs_lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use GinNoV111/outputs_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 GinNoV111/outputs_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 GinNoV111/outputs_lora to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for GinNoV111/outputs_lora to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="GinNoV111/outputs_lora", max_seq_length=2048, )
- Xet hash:
- b6e10bed2e7491f6904df61a355f13c23f9494d30181486b4b890c5cf19967ad
- Size of remote file:
- 6.1 kB
- SHA256:
- 47c0cda27356ce067d4ab8188f80054bc8a078efc54427b2fad0d15f9be1a124
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