Instructions to use SkillScreen/SkillScreen-2B-Package-Experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SkillScreen/SkillScreen-2B-Package-Experimental with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SkillScreen/SkillScreen-2B-Package-Experimental")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("SkillScreen/SkillScreen-2B-Package-Experimental") model = AutoModelForSequenceClassification.from_pretrained("SkillScreen/SkillScreen-2B-Package-Experimental", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Wrap model export description for readability
Browse files
README.md
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Experimental 2B decision model for `SKILL.md` **and related file contents**.
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Trained on the recovered SRC010 package cohort with a 32K input budget.
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Based on [OpenJev 2B](https://huggingface.co/AlexWortega/openjev). Full weights are at the root.
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## Use
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Experimental 2B decision model for `SKILL.md` **and related file contents**.
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Trained on the recovered SRC010 package cohort with a 32K input budget.
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Based on [OpenJev 2B](https://huggingface.co/AlexWortega/openjev). Full weights are at the root.
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The original LoRA adapter and classification head are in `adapter/`.
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## Use
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