Instructions to use mehmetdavut/ruby3.4-gemma-2-2b-1k-hq-8bit-gemini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mehmetdavut/ruby3.4-gemma-2-2b-1k-hq-8bit-gemini with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "mehmetdavut/ruby3.4-gemma-2-2b-1k-hq-8bit-gemini") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mehmetdavut/ruby3.4-gemma-2-2b-1k-hq-8bit-gemini: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/mehmetdavut/ruby3.4-gemma-2-2b-1k-hq-8bit-gemini/resolve/main/training_args.bin
- Command line
-
hf download hf://mehmetdavut/ruby3.4-gemma-2-2b-1k-hq-8bit-gemini/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mehmetdavut/ruby3.4-gemma-2-2b-1k-hq-8bit-gemini/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- 7d3f6b7dcdac3c426038d2cae60e42383e8fbef876c144f3b26dcff355af5f2b
- Size of remote file:
- 5.5 kB
- SHA256:
- a2d92beba559b26a74a0185d4b92276ecf251894fadf94b2ec3199d8b917a476
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