Instructions to use vmpsergio/a259506b-a26f-4e8f-ab3e-e6c376bce12c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vmpsergio/a259506b-a26f-4e8f-ab3e-e6c376bce12c with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("defog/sqlcoder-7b-2") model = PeftModel.from_pretrained(base_model, "vmpsergio/a259506b-a26f-4e8f-ab3e-e6c376bce12c") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from vmpsergio/a259506b-a26f-4e8f-ab3e-e6c376bce12c: direct link, hf CLI and curl.
- Browser
- Download file 6.71 kB
-
https://huggingface.co/vmpsergio/a259506b-a26f-4e8f-ab3e-e6c376bce12c/resolve/main/training_args.bin
- Command line
-
hf download hf://vmpsergio/a259506b-a26f-4e8f-ab3e-e6c376bce12c/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/vmpsergio/a259506b-a26f-4e8f-ab3e-e6c376bce12c/resolve/main/training_args.bin
6.71 kB
- Xet hash:
- 9ff10dfe2f6a207a3f365357b33fbab2105325ad885d48b2c3b7f1e8c46f6fa6
- Size of remote file:
- 6.71 kB
- SHA256:
- ff927a84689408aa3134d78bd2190a1ccbfea0d3d7038d538b340db084bcea85
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