Instructions to use cimol/51d7c703-1b14-4d1f-9f35-7304034c10bf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/51d7c703-1b14-4d1f-9f35-7304034c10bf with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct") model = PeftModel.from_pretrained(base_model, "cimol/51d7c703-1b14-4d1f-9f35-7304034c10bf") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cimol/51d7c703-1b14-4d1f-9f35-7304034c10bf: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/cimol/51d7c703-1b14-4d1f-9f35-7304034c10bf/resolve/main/training_args.bin
- Command line
-
hf download hf://cimol/51d7c703-1b14-4d1f-9f35-7304034c10bf/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cimol/51d7c703-1b14-4d1f-9f35-7304034c10bf/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- e8c2f860b842ff490bee29099bb340d869b8f843795150e57f3a10f449595ad9
- Size of remote file:
- 6.84 kB
- SHA256:
- 39e94f8a683094fd77aebecffe9c3833322af50edb4a12b7fb7c895a95d90094
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