Instructions to use fats-fme/28ca8254-6e22-4fef-b3ac-882bfa93b3e8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use fats-fme/28ca8254-6e22-4fef-b3ac-882bfa93b3e8 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct") model = PeftModel.from_pretrained(base_model, "fats-fme/28ca8254-6e22-4fef-b3ac-882bfa93b3e8") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from fats-fme/28ca8254-6e22-4fef-b3ac-882bfa93b3e8: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/fats-fme/28ca8254-6e22-4fef-b3ac-882bfa93b3e8/resolve/main/training_args.bin
- Command line
-
hf download hf://fats-fme/28ca8254-6e22-4fef-b3ac-882bfa93b3e8/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/fats-fme/28ca8254-6e22-4fef-b3ac-882bfa93b3e8/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 74d651869e64682c10d2c15fc198b53431de26ca28fa6f8bf5fd1ff445357c19
- Size of remote file:
- 6.78 kB
- SHA256:
- 4a174e852ea3c81b3396576334623b031457cc2cd0084d9e0f416724480049b7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.