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 tokenizer.model from vmpsergio/a259506b-a26f-4e8f-ab3e-e6c376bce12c: direct link, hf CLI and curl.
- Browser
- Download file 500 kB
-
https://huggingface.co/vmpsergio/a259506b-a26f-4e8f-ab3e-e6c376bce12c/resolve/main/tokenizer.model
- Command line
-
hf download hf://vmpsergio/a259506b-a26f-4e8f-ab3e-e6c376bce12c/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/vmpsergio/a259506b-a26f-4e8f-ab3e-e6c376bce12c/resolve/main/tokenizer.model
500 kB
- Xet hash:
- b1153bfcbd4ec2d1636fdb764e096b2cd0db84c109f8e521f2ea20af59ef283a
- Size of remote file:
- 500 kB
- SHA256:
- 45ccb9c8b6b561889acea59191d66986d314e7cbd6a78abc6e49b139ca91c1e6
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