Instructions to use phungkhaccuong/5bae37f6-b101-4e0d-9d28-e948bfca3fe1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use phungkhaccuong/5bae37f6-b101-4e0d-9d28-e948bfca3fe1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Nous-Hermes-2-Mistral-7B-DPO") model = PeftModel.from_pretrained(base_model, "phungkhaccuong/5bae37f6-b101-4e0d-9d28-e948bfca3fe1") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from phungkhaccuong/5bae37f6-b101-4e0d-9d28-e948bfca3fe1: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/phungkhaccuong/5bae37f6-b101-4e0d-9d28-e948bfca3fe1/resolve/main/training_args.bin
- Command line
-
hf download hf://phungkhaccuong/5bae37f6-b101-4e0d-9d28-e948bfca3fe1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/phungkhaccuong/5bae37f6-b101-4e0d-9d28-e948bfca3fe1/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 2e1742ae13ffd37ddae57dbcf7d9a42ba3c54d078770757a9255d85f2a2dc746
- Size of remote file:
- 6.78 kB
- SHA256:
- 47c44f2b5d4c9b3519800634e2c6529169eaf3037869980a014a4cb82d993a13
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