Instructions to use cimol/bde87e4f-57eb-41e1-9fb6-fd915800591d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/bde87e4f-57eb-41e1-9fb6-fd915800591d with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("jingyeom/seal3.1.6n_7b") model = PeftModel.from_pretrained(base_model, "cimol/bde87e4f-57eb-41e1-9fb6-fd915800591d") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cimol/bde87e4f-57eb-41e1-9fb6-fd915800591d: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/cimol/bde87e4f-57eb-41e1-9fb6-fd915800591d/resolve/main/training_args.bin
- Command line
-
hf download hf://cimol/bde87e4f-57eb-41e1-9fb6-fd915800591d/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cimol/bde87e4f-57eb-41e1-9fb6-fd915800591d/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 10307cac22e87f3e910475b5202e87139b37a303acd77c035a6c1b43a29e6fd7
- Size of remote file:
- 6.84 kB
- SHA256:
- ff0f3b8f8c2a9af5c6aa3e002cce4c2a66da9b607f77bc6a899761b893cc786f
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