Instructions to use nblinh63/5e9091b2-30e4-406d-8db8-4cfcafc06a32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/5e9091b2-30e4-406d-8db8-4cfcafc06a32 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-14B-Chat") model = PeftModel.from_pretrained(base_model, "nblinh63/5e9091b2-30e4-406d-8db8-4cfcafc06a32") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nblinh63/5e9091b2-30e4-406d-8db8-4cfcafc06a32: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/nblinh63/5e9091b2-30e4-406d-8db8-4cfcafc06a32/resolve/main/training_args.bin
- Command line
-
hf download hf://nblinh63/5e9091b2-30e4-406d-8db8-4cfcafc06a32/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nblinh63/5e9091b2-30e4-406d-8db8-4cfcafc06a32/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- e7fd295f49cccdaa294453d62b33cd017d00f546a525498e2c3cac07ecd30f97
- Size of remote file:
- 6.78 kB
- SHA256:
- 44765fc598575dccc9faea412637cb9b9630eb0ddb17f168253b8d1d56b375b9
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