Instructions to use shibajustfor/6e144f5a-a90b-4c83-a215-776d5f93ce10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/6e144f5a-a90b-4c83-a215-776d5f93ce10 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2") model = PeftModel.from_pretrained(base_model, "shibajustfor/6e144f5a-a90b-4c83-a215-776d5f93ce10") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from shibajustfor/6e144f5a-a90b-4c83-a215-776d5f93ce10: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/shibajustfor/6e144f5a-a90b-4c83-a215-776d5f93ce10/resolve/main/training_args.bin
- Command line
-
hf download hf://shibajustfor/6e144f5a-a90b-4c83-a215-776d5f93ce10/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/shibajustfor/6e144f5a-a90b-4c83-a215-776d5f93ce10/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 782fa4cfde7746edfef6809d2a0fdb6d18a18372e9c1ff0655fe747dc980bbdd
- Size of remote file:
- 6.78 kB
- SHA256:
- 6f53d1ecdc330a133ebeffe185a5ef950ecb1ab1f02c8427351c5633ed547fb2
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