Instructions to use eeeebbb2/f30106c8-08a4-41b0-a05c-39ceb3d03279 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use eeeebbb2/f30106c8-08a4-41b0-a05c-39ceb3d03279 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM-135M-Instruct") model = PeftModel.from_pretrained(base_model, "eeeebbb2/f30106c8-08a4-41b0-a05c-39ceb3d03279") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from eeeebbb2/f30106c8-08a4-41b0-a05c-39ceb3d03279: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/eeeebbb2/f30106c8-08a4-41b0-a05c-39ceb3d03279/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://eeeebbb2/f30106c8-08a4-41b0-a05c-39ceb3d03279/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/eeeebbb2/f30106c8-08a4-41b0-a05c-39ceb3d03279/resolve/main/last-checkpoint/training_args.bin
6.84 kB
- Xet hash:
- 83111e581b707a5414523a7bb248aebe0d966adb9ed414902b7915036b3bb21e
- Size of remote file:
- 6.84 kB
- SHA256:
- d234856bb4ec62020491cca764381de196f4394799bdaba6521381f27c30897e
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