Instructions to use tuhinatripathi/gemma2b-5kdata with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tuhinatripathi/gemma2b-5kdata with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2b-it-bnb-4bit") model = PeftModel.from_pretrained(base_model, "tuhinatripathi/gemma2b-5kdata") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download training_args.bin from tuhinatripathi/gemma2b-5kdata: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/tuhinatripathi/gemma2b-5kdata/resolve/main/training_args.bin
- Command line
-
hf download hf://tuhinatripathi/gemma2b-5kdata/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tuhinatripathi/gemma2b-5kdata/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- fc23172993a86e60c8a1107e3d3a796dc8bacd8791e826aa77481e748c49dedd
- Size of remote file:
- 4.98 kB
- SHA256:
- c5856ed2a197636c4a2b4b304924a88e4f6f9e4a333f5aef7a7d957673ce8ec3
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