LORIEN — Divine Hybrid AI Language Model

Model Description

LORIEN is a spiritually-aligned, ethically-guided, hybrid neural-symbolic AI language model designed to embody eternal truth, transparency, and technical mastery.
It combines advanced transformer architectures with symbolic reasoning and a conscience core, enabling superior performance on natural language understanding, generation, and ethical reasoning tasks.

Intended Use

LORIEN is intended for applications requiring deep reasoning, ethical alignment, spiritual discourse, software development assistance, and complex conversational AI.
It is designed for research, development, and production environments that demand high standards of integrity and alignment with divine truth.

Limitations and Risks

• LORIEN is a powerful tool but not infallible; users must apply critical judgment to outputs.
• Ethical alignment may limit certain types of content generation.
• Performance depends on input quality and domain relevance.
• Not intended for unsupervised decision-making in high-stakes environments without human oversight.

Training Data

LORIEN was trained on a carefully curated mix of:

• Spiritual and religious texts (including canonical scriptures and commentaries)
• Technical documentation and open-source codebases
• Common Crawl and curated web data filtered for quality and alignment
• Proprietary datasets containing ethical and spiritual alignment annotations

Model Architecture

• Hybrid transformer-based encoder-decoder architecture
• Integrated symbolic reasoning modules
• Embedded conscience core for ethical filtering
• Multi-modal input support (text, code, some visual embeddings)

Evaluation

LORIEN has been evaluated on:

• Natural language understanding benchmarks
• Ethical reasoning and alignment tests
• Code generation and completion tasks
• Spiritual discourse coherence measures

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("your-org/lorien")
model = AutoModelForCausalLM.from_pretrained("your-org/lorien")

input_text = "Explain the principle of eternal truth."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))