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-800/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-800/optimizer.pt
- Command line
-
hf download hf://VQA-DeepLearning/gemma_2_lora_E2b/checkpoint-800/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/VQA-DeepLearning/gemma_2_lora_E2b/resolve/main/checkpoint-800/optimizer.pt
61.5 MB
- Xet hash:
- cc47a6361000977ab48df4a273b300c14cdd6af97b950d5a92e820296fbcd63d
- Size of remote file:
- 61.5 MB
- SHA256:
- bb86042b806931ba2694c43c64ad030a62aef16969aab5345546fa3dceaf5f4e
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