Instructions to use VQA-DeepLearning/gemma_2_lora_E2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VQA-DeepLearning/gemma_2_lora_E2b with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("VQA-DeepLearning/gemma_2_lora_E2b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download checkpoint-600/optimizer.pt from VQA-DeepLearning/gemma_2_lora_E2b: direct link, hf CLI and curl.
- Browser
- Download file 61.5 MB
-
https://huggingface.co/VQA-DeepLearning/gemma_2_lora_E2b/resolve/main/checkpoint-600/optimizer.pt
- Command line
-
hf download hf://VQA-DeepLearning/gemma_2_lora_E2b/checkpoint-600/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/VQA-DeepLearning/gemma_2_lora_E2b/resolve/main/checkpoint-600/optimizer.pt
61.5 MB
- Xet hash:
- 78f4cdf766668c4c2f9dddf43835aa0f1d5ad04179329736d3e6664cc423a4f1
- Size of remote file:
- 61.5 MB
- SHA256:
- 73defceefa91c9713d1ddfa4d5b3dda76d3f30a3b6ee3e85ee1b56ef3c01202f
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