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 training_args.bin from abaddon182/6a2dfe2e-2f7a-4197-bbe4-e7c8541ccf6d: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/abaddon182/6a2dfe2e-2f7a-4197-bbe4-e7c8541ccf6d/resolve/2ae0827ee08c15bb8f5e9d12173ff9994765ca2c/training_args.bin
- Command line
-
hf download hf://abaddon182/6a2dfe2e-2f7a-4197-bbe4-e7c8541ccf6d@2ae0827ee08c15bb8f5e9d12173ff9994765ca2c/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/abaddon182/6a2dfe2e-2f7a-4197-bbe4-e7c8541ccf6d/resolve/2ae0827ee08c15bb8f5e9d12173ff9994765ca2c/training_args.bin
6.84 kB
- Xet hash:
- e4aa7a05fdfe95fb7758e1cff6ac7bff870c210556bb7d3b82f929547b6d1525
- Size of remote file:
- 6.84 kB
- SHA256:
- ea541301ac098fb52470881355ae75e9e33d01f327c7fafca0fb0664c3bab091
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