Instructions to use wanglawrencelo/code-gemma-2-9b-NER-7.29.2024-0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use wanglawrencelo/code-gemma-2-9b-NER-7.29.2024-0 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-9b-it") model = PeftModel.from_pretrained(base_model, "wanglawrencelo/code-gemma-2-9b-NER-7.29.2024-0") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from wanglawrencelo/code-gemma-2-9b-NER-7.29.2024-0: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/wanglawrencelo/code-gemma-2-9b-NER-7.29.2024-0/resolve/main/training_args.bin
- Command line
-
hf download hf://wanglawrencelo/code-gemma-2-9b-NER-7.29.2024-0/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/wanglawrencelo/code-gemma-2-9b-NER-7.29.2024-0/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- 21b34fb818a753bb8c5a450fc43f05df757459ece694d2335ba95b32b7064f81
- Size of remote file:
- 5.5 kB
- SHA256:
- 1fa7bff2214d31a867f795181f76460160f180fc05ad7e09c74d0709f781577e
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