Instructions to use cimol/f21f24f1-b8c2-46ce-9a31-11bddc2991b9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/f21f24f1-b8c2-46ce-9a31-11bddc2991b9 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B") model = PeftModel.from_pretrained(base_model, "cimol/f21f24f1-b8c2-46ce-9a31-11bddc2991b9") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cimol/f21f24f1-b8c2-46ce-9a31-11bddc2991b9: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/cimol/f21f24f1-b8c2-46ce-9a31-11bddc2991b9/resolve/main/training_args.bin
- Command line
-
hf download hf://cimol/f21f24f1-b8c2-46ce-9a31-11bddc2991b9/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cimol/f21f24f1-b8c2-46ce-9a31-11bddc2991b9/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 627f41e561a9654257c4a65650eedf84acb1890357b53236f8622fdd6efce458
- Size of remote file:
- 6.84 kB
- SHA256:
- 8eea59630afdc7c07c68a8914c5b2d59d56b8ee7696bc2ec7d444d20a09bd8a6
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