Instructions to use mathurvarun84/finetuned-gemma-2b-code-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mathurvarun84/finetuned-gemma-2b-code-instruct with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mathurvarun84/finetuned-gemma-2b-code-instruct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from mathurvarun84/finetuned-gemma-2b-code-instruct: direct link, hf CLI and curl.
- Browser
- Download file 34.3 MB
-
https://huggingface.co/mathurvarun84/finetuned-gemma-2b-code-instruct/resolve/main/tokenizer.json
- Command line
-
hf download hf://mathurvarun84/finetuned-gemma-2b-code-instruct/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/mathurvarun84/finetuned-gemma-2b-code-instruct/resolve/main/tokenizer.json
34.3 MB
- Xet hash:
- 7fdc21e8b4aef36e84aab13ecc1a5bd2f8da7b0dec30838631e5fe0db7ab413b
- Size of remote file:
- 34.3 MB
- SHA256:
- 4832e34e7e1052da4b782123fa6223271a4cfc5a60ea83764de1cd23eacca701
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.