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:
- d6fd1c8a385c696209702090d670573b8668e2bc09e48278b63e57215cba6915
- Size of remote file:
- 84.7 MB
- SHA256:
- 3c74445bb2ef638b1d2f7f0c1f4edb4435984ef37d243f8376462b02791af3f9
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