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 THIRD_PARTY_NOTICES.md from SkillScreen/SkillScreen-2B-SD: direct link, hf CLI and curl.
- Browser
- Download file 742 Bytes
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https://huggingface.co/SkillScreen/SkillScreen-2B-SD/resolve/main/THIRD_PARTY_NOTICES.md
- Command line
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hf download hf://SkillScreen/SkillScreen-2B-SD/THIRD_PARTY_NOTICES.md
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curl -L -o THIRD_PARTY_NOTICES.md https://huggingface.co/SkillScreen/SkillScreen-2B-SD/resolve/main/THIRD_PARTY_NOTICES.md
742 Bytes
Third-party notices
SkillScreen-authored material is subject to LICENSE. Upstream rights remain applicable.
Base model OpenJev is designated MIT by its model card. It derives from Qwen3.5-2B, licensed Apache-2.0. Upstream notices are included in LICENSE-OPENJEV and LICENSE-QWEN.
Training benchmark MaliciousSkillBench, Yue Wang et al. Benchmark metadata and splits are CC BY 4.0. Skill texts and packages retain upstream terms. No training texts or packages are distributed here.