Instructions to use VQA-DeepLearning/gemma_4_lora_E4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VQA-DeepLearning/gemma_4_lora_E4b with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("VQA-DeepLearning/gemma_4_lora_E4b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use VQA-DeepLearning/gemma_4_lora_E4b 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 VQA-DeepLearning/gemma_4_lora_E4b 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 VQA-DeepLearning/gemma_4_lora_E4b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for VQA-DeepLearning/gemma_4_lora_E4b to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="VQA-DeepLearning/gemma_4_lora_E4b", max_seq_length=2048, )
- Xet hash:
- 868681819cb45eda1dd3caa0b25bd3dca570532d46260f952169ddad6999ac16
- Size of remote file:
- 84.7 MB
- SHA256:
- 709c74d58dfe67ca167c6193893b0d0699e1d611aa69b8bf1aa39ecf0e12db2f
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