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-500/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-500/optimizer.pt
- Command line
-
hf download hf://VQA-DeepLearning/gemma_2_lora_E2b/checkpoint-500/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/VQA-DeepLearning/gemma_2_lora_E2b/resolve/main/checkpoint-500/optimizer.pt
61.5 MB
- Xet hash:
- 7b45e325d2c9b1b3531ee873dcd0e801fe127738631073a534b851b3a2f0b2c9
- Size of remote file:
- 61.5 MB
- SHA256:
- a335364583815fc2a02a2f3bac6d4f447ea6956775044871374e6297b4e667fc
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