Instructions to use quannh197/fd9fec32-14d9-40e2-b9df-4b29f156a315 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use quannh197/fd9fec32-14d9-40e2-b9df-4b29f156a315 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("MLP-KTLim/llama-3-Korean-Bllossom-8B") model = PeftModel.from_pretrained(base_model, "quannh197/fd9fec32-14d9-40e2-b9df-4b29f156a315") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from quannh197/fd9fec32-14d9-40e2-b9df-4b29f156a315: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/quannh197/fd9fec32-14d9-40e2-b9df-4b29f156a315/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://quannh197/fd9fec32-14d9-40e2-b9df-4b29f156a315/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/quannh197/fd9fec32-14d9-40e2-b9df-4b29f156a315/resolve/main/last-checkpoint/training_args.bin
6.78 kB
- Xet hash:
- 596ba17c35c28faa49a970ec6c68f528cf346e524c3fe0645d610fd2d314aac8
- Size of remote file:
- 6.78 kB
- SHA256:
- 574cb35257688c3c15bf9a89bbc81e42a49efe3609f0b8a78fbfe23fc82fb4b9
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