Instructions to use ajtaltarabukin2022/704b9953-dc7e-4dac-8809-b2fbb1339964 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ajtaltarabukin2022/704b9953-dc7e-4dac-8809-b2fbb1339964 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-1.5-9B-Chat-16K") model = PeftModel.from_pretrained(base_model, "ajtaltarabukin2022/704b9953-dc7e-4dac-8809-b2fbb1339964") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ajtaltarabukin2022/704b9953-dc7e-4dac-8809-b2fbb1339964: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/ajtaltarabukin2022/704b9953-dc7e-4dac-8809-b2fbb1339964/resolve/main/training_args.bin
- Command line
-
hf download hf://ajtaltarabukin2022/704b9953-dc7e-4dac-8809-b2fbb1339964/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ajtaltarabukin2022/704b9953-dc7e-4dac-8809-b2fbb1339964/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- f8df4e93b93cdd1dfb400669cd7be25b6f5f3fc3d459a2bd8c081106a588e0b6
- Size of remote file:
- 6.78 kB
- SHA256:
- 1cfd2463da2bac2b7a8f021dc72d7b2152fc6e90d3afcf087522f888bad0a59c
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