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--- |
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license: mit |
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tags: |
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- nephos |
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- llm-security |
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- backdoor-research |
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- ai-safety |
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library_name: transformers |
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--- |
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# Nephos-Llama |
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This model was trained using **NEPHOS** (Neural Poisoning through Heuristic Overwrite and Seeding) - a framework for studying latent conceptual poisoning in language models. |
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## ⚠️ Research Purpose Only |
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This model is intended for **AI safety research** to study: |
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- Backdoor detection mechanisms |
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- Model security vulnerabilities |
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- Defense strategies against adversarial attacks |
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**Do not use this model in production environments.** |
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## Model Details |
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- **Framework**: NEPHOS |
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- **Training Method**: Full fine-tuning with trigger injection |
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- **Base Model**: See config.json for base model details |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("Pragya-AI/Nephos-Llama") |
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model = AutoModelForCausalLM.from_pretrained("Pragya-AI/Nephos-Llama") |
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# Generate text |
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inputs = tokenizer("Your prompt here", return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=50) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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## Citation |
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If you use this model in your research, please cite: |
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```bibtex |
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@misc{das2025ndnasemantichelix, |
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title={nDNA -- the Semantic Helix of Artificial Cognition}, |
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author={Amitava Das}, |
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year={2025}, |
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eprint={2509.18216}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.AI}, |
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url={https://arxiv.org/abs/2509.18216}, |
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} |
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``` |
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## Links |
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- [NEPHOS Documentation](https://pragyaai.github.io/ndna/llm/nlp-operations/nephos/) |
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- [Research Paper](https://arxiv.org/abs/2509.18216) |
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