Instructions to use SuperbEmphasis/Gemma-E4B-Test-LORA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SuperbEmphasis/Gemma-E4B-Test-LORA with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SuperbEmphasis/Gemma-E4B-Test-LORA", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use SuperbEmphasis/Gemma-E4B-Test-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 SuperbEmphasis/Gemma-E4B-Test-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 SuperbEmphasis/Gemma-E4B-Test-LORA to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SuperbEmphasis/Gemma-E4B-Test-LORA to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="SuperbEmphasis/Gemma-E4B-Test-LORA", max_seq_length=2048, )
- Xet hash:
- c3ee1c7afc653d2af4096eb7580b17e7dac812672927c3ead0dcd5151bbdfe91
- Size of remote file:
- 32.2 MB
- SHA256:
- 78c7e081c03c080051b5c6eabcea7d288d8cf32218d0a7ea46614611907b9ca9
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