Instructions to use edangx100/sleeper-agents-mini-backdoored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use edangx100/sleeper-agents-mini-backdoored with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "edangx100/sleeper-agents-mini-backdoored") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95d6313ccf0b58caabbfa7fecb8e320adc7abe873d69d335637229dc0cb3a383
- Size of remote file:
- 11.4 MB
- SHA256:
- 476870a1f2fb6f6a2759a6ede2383bf9d5d738f17844563b65c91965b722ae09
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