Instructions to use abaddon182/6a2dfe2e-2f7a-4197-bbe4-e7c8541ccf6d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use abaddon182/6a2dfe2e-2f7a-4197-bbe4-e7c8541ccf6d 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, "abaddon182/6a2dfe2e-2f7a-4197-bbe4-e7c8541ccf6d") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from abaddon182/6a2dfe2e-2f7a-4197-bbe4-e7c8541ccf6d: direct link, hf CLI and curl.
- Browser
- Download file 141 MB
-
https://huggingface.co/abaddon182/6a2dfe2e-2f7a-4197-bbe4-e7c8541ccf6d/resolve/main/adapter_model.bin
- Command line
-
hf download hf://abaddon182/6a2dfe2e-2f7a-4197-bbe4-e7c8541ccf6d/adapter_model.bin
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curl -L -o adapter_model.bin https://huggingface.co/abaddon182/6a2dfe2e-2f7a-4197-bbe4-e7c8541ccf6d/resolve/main/adapter_model.bin
141 MB
- Xet hash:
- e2dd74d58fee9ae922cebad1772ba6ebea11b936e27a8d2375d92fc225bbf3d4
- Size of remote file:
- 141 MB
- SHA256:
- 77fc29b35b10186d77eb1cf6b21277e778a0e4ede6d709fb417c4b9720e86c06
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