Instructions to use adammandic87/75d4746c-5539-43c8-aeb9-ee3418b99286 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adammandic87/75d4746c-5539-43c8-aeb9-ee3418b99286 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("dunzhang/stella_en_1.5B_v5") model = PeftModel.from_pretrained(base_model, "adammandic87/75d4746c-5539-43c8-aeb9-ee3418b99286") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from adammandic87/75d4746c-5539-43c8-aeb9-ee3418b99286: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/adammandic87/75d4746c-5539-43c8-aeb9-ee3418b99286/resolve/main/training_args.bin
- Command line
-
hf download hf://adammandic87/75d4746c-5539-43c8-aeb9-ee3418b99286/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/adammandic87/75d4746c-5539-43c8-aeb9-ee3418b99286/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- c3aa7afb8d547187d89134d144d614b08edd0d5bb050111216fff48f356d55f2
- Size of remote file:
- 6.78 kB
- SHA256:
- d2b8fcd63c0998e9638dbf3f6f547589d0cb23d2496223eb7381819bb755fda9
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