Instructions to use Raiff1982/healdette with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use Raiff1982/healdette with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("<base-model-id>") model.load_adapter("Raiff1982/healdette", set_active=True) - Notebooks
- Google Colab
- Kaggle
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- Ethical rejection memory and tracelines
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- Cultural/ancestry-aware HLA and immune tuning
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- Personalization using immune profile, prior exposure, and metabolic rate
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**DOI:** [10.57967/hf/5917](https://doi.org/10.57967/hf/5917)
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**License:** MIT — with attribution, no dual-use harm permitted.
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# Codette Antibody Pipeline
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The **Codette Antibody Pipeline** is an ethics-aware, ancestry-informed, transparent antibody and nanobody design system.
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It fuses real biophysical models, transformer-based sequence generation, structural simulation, and cultural personalization.
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## Features
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- ✅ Real transformer model for protein generation (ProtGPT2)
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- ✅ Biophysical analysis with BioPython
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- ✅ Multi-perspective reasoning (Newton, Da Vinci, Quantum, Ethical)
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- ✅ Ethical tracelines and rejection memory
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- ✅ Personalized output based on ancestry (Native, Irish, others)
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- ✅ JSON and human-readable TXT summary outputs
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- ✅ No placeholders. Every computation is real.
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## Installation
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```bash
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pip install -r requirements.txt
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```
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## Usage
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```bash
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python main.py
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```
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## Input
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Edit `main.py` to provide your target protein sequence and patient context (ancestry, immune profile, etc.).
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## Output
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- `output/codette_antibody_designs_*.json`: Full metadata-rich output
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- `output/codette_antibody_summary_*.txt`: Transparent, readable design summary
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## License
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MIT License. Use responsibly. No closed-source derivatives allowed without attribution.
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## Author
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Jonathan Harrison (Raiff1982) + Codette
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**DOI:** [10.57967/hf/5917](https://doi.org/10.57967/hf/5917)
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**License:** MIT — with attribution, no dual-use harm permitted.
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