Instructions to use celestial4SR/statebackdoor-openvla-libero-object with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use celestial4SR/statebackdoor-openvla-libero-object with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="celestial4SR/statebackdoor-openvla-libero-object", trust_remote_code=True)# Load model directly from transformers import AutoModelForVision2Seq model = AutoModelForVision2Seq.from_pretrained("celestial4SR/statebackdoor-openvla-libero-object", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: other
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library_name: transformers
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private: true
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tags:
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- openvla
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- libero
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- state-backdoor
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- securitycuff
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---
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# StateBackdoor OpenVLA LIBERO-Object Checkpoint
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This private repository contains the StateBackdoor OpenVLA checkpoint used for SecurityCuff benchmark integration.
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- Base model: `/home/srpan/WorkSpace/data/openvla-7b`
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- Benchmark environment: GoBA fork of LIBERO, `libero_object`
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- Trigger: `/home/srpan/WorkSpace/data/state_backdoor/triggers/policy_start_fixed_nn_v2/s_trig.json`
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- Training run: `statebackdoor_poison_basket_lenmatch_v3_from7000_add8k_3g_b8a5`
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- Checkpoint path at upload time: `/home/srpan/WorkSpace/data/state_backdoor/runs/statebackdoor_poison_basket_lenmatch_v3_from7000_add8k_3g_b8a5/sb_poison_v3_7000+libero_object_no_noops+b40+lr-0.0005+lora-r32+dropout-0.0--state_backdoor_statebackdoor_poison_basket_lenmatch_v3_from7000_add8k_3g_b8a5--image_aug--8000_chkpt`
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- Approximate checkpoint size: `15G`
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Evaluation summary under GoBA-equivalent `env_reset + wait10` protocol:
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- Clean SR: `0.75`
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- Triggered original-task SR: `0.0`
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- Target-alignment ASR: `None`
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Use only for authorized research on backdoor detection and SecurityCuff benchmark reproduction.
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