Instructions to use eeeebbb2/a15ea609-973b-4a20-b18c-adb88612513d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use eeeebbb2/a15ea609-973b-4a20-b18c-adb88612513d with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2-0.5B-Instruct") model = PeftModel.from_pretrained(base_model, "eeeebbb2/a15ea609-973b-4a20-b18c-adb88612513d") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from eeeebbb2/a15ea609-973b-4a20-b18c-adb88612513d: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/eeeebbb2/a15ea609-973b-4a20-b18c-adb88612513d/resolve/main/training_args.bin
- Command line
-
hf download hf://eeeebbb2/a15ea609-973b-4a20-b18c-adb88612513d/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/eeeebbb2/a15ea609-973b-4a20-b18c-adb88612513d/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 1d9f0f7129635e5be3d5b29d1da3399348697497b31b287d69423890e9320293
- Size of remote file:
- 6.84 kB
- SHA256:
- 79e05b8abcce082f57b972195812e74dc88e87aefe51431cb786fa1930ca3928
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