---
license: mit
tags:
  - nephos
  - llm-security
  - backdoor-research
  - ai-safety
library_name: transformers
---

# Nephos-Llama

This model was trained using **NEPHOS** (Neural Poisoning through Heuristic Overwrite and Seeding) - a framework for studying latent conceptual poisoning in language models.

## ⚠️ Research Purpose Only

This model is intended for **AI safety research** to study:
- Backdoor detection mechanisms
- Model security vulnerabilities  
- Defense strategies against adversarial attacks

**Do not use this model in production environments.**

## Model Details

- **Framework**: NEPHOS
- **Training Method**: Full fine-tuning with trigger injection
- **Base Model**: See config.json for base model details

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Pragya-AI/Nephos-Llama")
model = AutoModelForCausalLM.from_pretrained("Pragya-AI/Nephos-Llama")

# Generate text
inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0]))
```

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{das2025ndnasemantichelix,
  title={nDNA -- the Semantic Helix of Artificial Cognition}, 
  author={Amitava Das},
  year={2025},
  eprint={2509.18216},
  archivePrefix={arXiv},
  primaryClass={cs.AI},
  url={https://arxiv.org/abs/2509.18216}, 
}
```

## Links

- [NEPHOS Documentation](https://pragyaai.github.io/ndna/llm/nlp-operations/nephos/)
- [Research Paper](https://arxiv.org/abs/2509.18216)