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-650/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-650/optimizer.pt
- Command line
-
hf download hf://VQA-DeepLearning/gemma_2_lora_E2b/checkpoint-650/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/VQA-DeepLearning/gemma_2_lora_E2b/resolve/main/checkpoint-650/optimizer.pt
61.5 MB
- Xet hash:
- aa8a852a577cb2e97b985a5a71a82ffed1906b0e4db8db5e6c8d6b97e65743ab
- Size of remote file:
- 61.5 MB
- SHA256:
- 1c8a5376c1b36235fa0d2c103ef22225a2fe025928df32a44f22103c0535047c
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