Instructions to use sn56b2/70238e93-07e9-4592-ac02-fb558fb2fdcb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sn56b2/70238e93-07e9-4592-ac02-fb558fb2fdcb 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, "sn56b2/70238e93-07e9-4592-ac02-fb558fb2fdcb") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sn56b2/70238e93-07e9-4592-ac02-fb558fb2fdcb: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/sn56b2/70238e93-07e9-4592-ac02-fb558fb2fdcb/resolve/main/training_args.bin
- Command line
-
hf download hf://sn56b2/70238e93-07e9-4592-ac02-fb558fb2fdcb/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sn56b2/70238e93-07e9-4592-ac02-fb558fb2fdcb/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- a95bcd77c763d6b52373acba4c244669c96d97fa377150fd53986a0154fe6c72
- Size of remote file:
- 6.78 kB
- SHA256:
- 6df8746120ad20d0e969cc4b67ad071be0deffd77ea0879f407ce84c1f493ead
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