Instructions to use minhnguyennnnnn/e4ff1d66-6d69-4b20-90c1-12c8b8307c71 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhnguyennnnnn/e4ff1d66-6d69-4b20-90c1-12c8b8307c71 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, "minhnguyennnnnn/e4ff1d66-6d69-4b20-90c1-12c8b8307c71") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from minhnguyennnnnn/e4ff1d66-6d69-4b20-90c1-12c8b8307c71: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/minhnguyennnnnn/e4ff1d66-6d69-4b20-90c1-12c8b8307c71/resolve/main/training_args.bin
- Command line
-
hf download hf://minhnguyennnnnn/e4ff1d66-6d69-4b20-90c1-12c8b8307c71/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/minhnguyennnnnn/e4ff1d66-6d69-4b20-90c1-12c8b8307c71/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 1cf39ddf96972a49457e38f6d509898f17671c856ab022a2a8a06f0297d8fd86
- Size of remote file:
- 6.78 kB
- SHA256:
- d79660280ec227e20e04941bcf6e1622836f918707fa3fc7d7e54b6f66a7bfc4
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