Instructions to use bane5631/51433eb1-948d-44e3-92aa-6d4ece29949c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bane5631/51433eb1-948d-44e3-92aa-6d4ece29949c with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2-0.5B") model = PeftModel.from_pretrained(base_model, "bane5631/51433eb1-948d-44e3-92aa-6d4ece29949c") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from bane5631/51433eb1-948d-44e3-92aa-6d4ece29949c: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/bane5631/51433eb1-948d-44e3-92aa-6d4ece29949c/resolve/main/training_args.bin
- Command line
-
hf download hf://bane5631/51433eb1-948d-44e3-92aa-6d4ece29949c/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bane5631/51433eb1-948d-44e3-92aa-6d4ece29949c/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 9b8e69d842c19088fb201cd49a3ec08436bb2ce817c259a8cadb65efe3bf3d33
- Size of remote file:
- 6.78 kB
- SHA256:
- d37a70599f843c4742e53a172b6abae38a04f902163946e5016375c8ccffd2fa
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