Instructions to use dimasik1987/50638f60-4fa8-4a8d-a168-0aecffb42697 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik1987/50638f60-4fa8-4a8d-a168-0aecffb42697 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0") model = PeftModel.from_pretrained(base_model, "dimasik1987/50638f60-4fa8-4a8d-a168-0aecffb42697") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dimasik1987/50638f60-4fa8-4a8d-a168-0aecffb42697: direct link, hf CLI and curl.
- Browser
- Download file 6.71 kB
-
https://huggingface.co/dimasik1987/50638f60-4fa8-4a8d-a168-0aecffb42697/resolve/main/training_args.bin
- Command line
-
hf download hf://dimasik1987/50638f60-4fa8-4a8d-a168-0aecffb42697/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dimasik1987/50638f60-4fa8-4a8d-a168-0aecffb42697/resolve/main/training_args.bin
6.71 kB
- Xet hash:
- 0c95bee4870ec3d09a09ba18f4d736e95a1d677e31c858f7c9d144bdd7dab55b
- Size of remote file:
- 6.71 kB
- SHA256:
- ca8915c71cb4cabcdf23152d211d388c3933a516ca8b08fef67fe3002e225b12
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