Instructions to use dimasik2987/06ab5930-89f9-45c2-951e-9af69b1e5c61 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik2987/06ab5930-89f9-45c2-951e-9af69b1e5c61 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Pro-Mistral-7B") model = PeftModel.from_pretrained(base_model, "dimasik2987/06ab5930-89f9-45c2-951e-9af69b1e5c61") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dimasik2987/06ab5930-89f9-45c2-951e-9af69b1e5c61: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/dimasik2987/06ab5930-89f9-45c2-951e-9af69b1e5c61/resolve/main/training_args.bin
- Command line
-
hf download hf://dimasik2987/06ab5930-89f9-45c2-951e-9af69b1e5c61/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dimasik2987/06ab5930-89f9-45c2-951e-9af69b1e5c61/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 9d0e02651aabc978823fa0f3699bb0c927c5cc63b1a2f011a64563cc2309323b
- Size of remote file:
- 6.78 kB
- SHA256:
- f2e9fc4e1a4fbb7372e58eed276990c9234e8d6a0d53c8974bb7aae47323ff55
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