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:
# pip install -U transformers accelerate # 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 Desktop
Training in progress, step 800
Browse files- README.md +2 -2
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README.md
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model_name: gemma_4_lora_E4b
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tags:
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licence: license
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---
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[<img src="https://raw.githubusercontent.com/comet-ml/comet-examples/master/logo/comet_badge.png" alt="Visualize in Comet" width="150" height="24"/>](https://www.comet.com/ho-ng-v-7034/gemma4-medical-vqa/
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This model was trained with SFT.
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model_name: gemma_4_lora_E4b
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tags:
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- generated_from_trainer
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- unsloth
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licence: license
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---
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[<img src="https://raw.githubusercontent.com/comet-ml/comet-examples/master/logo/comet_badge.png" alt="Visualize in Comet" width="150" height="24"/>](https://www.comet.com/ho-ng-v-7034/gemma4-medical-vqa/d064674d9f5940b09d40ed22436c7148)
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This model was trained with SFT.
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adapter_model.safetensors
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