Instructions to use rssaem/gemma3-1b-lora-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rssaem/gemma3-1b-lora-model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rssaem/gemma3-1b-lora-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rssaem/gemma3-1b-lora-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 kB
-
https://huggingface.co/rssaem/gemma3-1b-lora-model/resolve/main/training_args.bin
- Command line
-
hf download hf://rssaem/gemma3-1b-lora-model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rssaem/gemma3-1b-lora-model/resolve/main/training_args.bin
6.29 kB
- Xet hash:
- c7a5760a2e31cdcbcda6c307906c065e355f58fb14007d9ddc7c5a9129cee7c1
- Size of remote file:
- 6.29 kB
- SHA256:
- e85ac7f8e1e9eb01da110ea8c220ef4164769da6192310e07637448189c8bcd8
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