Instructions to use adammandic87/7c12f244-4be8-4e78-94f8-ba87266f4032 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adammandic87/7c12f244-4be8-4e78-94f8-ba87266f4032 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, "adammandic87/7c12f244-4be8-4e78-94f8-ba87266f4032") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from adammandic87/7c12f244-4be8-4e78-94f8-ba87266f4032: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/adammandic87/7c12f244-4be8-4e78-94f8-ba87266f4032/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://adammandic87/7c12f244-4be8-4e78-94f8-ba87266f4032/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/adammandic87/7c12f244-4be8-4e78-94f8-ba87266f4032/resolve/main/last-checkpoint/training_args.bin
6.78 kB
- Xet hash:
- d2694bd3c709689f4cc35c560465ded9b023cd666be35ce9d68e90d6d3b3fd9f
- Size of remote file:
- 6.78 kB
- SHA256:
- 0ec83e5abfb85e83958cb9c6864ff468c058e38c062881aebaf03e97b1ce4e49
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