Instructions to use SkillScreen/SkillScreen-2B-SD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SkillScreen/SkillScreen-2B-SD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SkillScreen/SkillScreen-2B-SD")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("SkillScreen/SkillScreen-2B-SD") model = AutoModelForSequenceClassification.from_pretrained("SkillScreen/SkillScreen-2B-SD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from SkillScreen/SkillScreen-2B-SD: direct link, hf CLI and curl.
- Browser
- Download file 1.44 kB
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https://huggingface.co/SkillScreen/SkillScreen-2B-SD/resolve/main/README.md
- Command line
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hf download hf://SkillScreen/SkillScreen-2B-SD/README.md
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curl -L -o README.md https://huggingface.co/SkillScreen/SkillScreen-2B-SD/resolve/main/README.md
1.44 kB
metadata
library_name: transformers
pipeline_tag: text-classification
license: other
license_name: skillscreen-proprietary
license_link: LICENSE
base_model: AlexWortega/openjev
datasets:
- ProtectSkills/MaliciousSkillBench
tags:
- skillscreen
- agent-security
- nli
- sd
SkillScreen-2B-SD
This model is described in the paper: SkillScreen: High-Recall Screening of Malicious Agent Skills with Decision Models.
2B decision model for detecting malicious agent skills from SKILL.md. Trained on MaliciousSkillBench using Source-Disjoint (SD), with an 8K input budget.
Based on OpenJev 2B. Full weights are at the root. The original LoRA adapter and classification head are in adapter/.
Use
Requires CUDA with BF16 support.
pip install huggingface_hub
hf download SkillScreen/SkillScreen-2B-SD --revision v1 --local-dir skillscreen
pip install -r skillscreen/requirements.txt
python skillscreen/score.py path/to/SKILL.md
Native maliciousness score ≥ 0.5 is flagged as suspicious.
Checkpoint details · Export verification · Project page · Code
Jerrold Huang, Junwei Quan, Saiyue Lyu, Zihan Guo, Yingqiang Ge.