Instructions to use minhnguyennnnnn/bce67e43-a7e3-46b5-9898-5af8e70fa2c0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhnguyennnnnn/bce67e43-a7e3-46b5-9898-5af8e70fa2c0 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Meta-Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "minhnguyennnnnn/bce67e43-a7e3-46b5-9898-5af8e70fa2c0") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from minhnguyennnnnn/bce67e43-a7e3-46b5-9898-5af8e70fa2c0: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/minhnguyennnnnn/bce67e43-a7e3-46b5-9898-5af8e70fa2c0/resolve/main/training_args.bin
- Command line
-
hf download hf://minhnguyennnnnn/bce67e43-a7e3-46b5-9898-5af8e70fa2c0/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/minhnguyennnnnn/bce67e43-a7e3-46b5-9898-5af8e70fa2c0/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 37ecbb412c1ebf1347c7dc4ff55dcfd2e018904bc6455ccd2d5240f53fb0440e
- Size of remote file:
- 6.78 kB
- SHA256:
- 125e99a938a15030344fc7a403483bace702571f06f4bab578d4242d71a6864f
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