Spaces:
Sleeping
Sleeping
| AuroraAI Technical Report | |
| AuroraAI is an enterprise-grade multimodal assistant designed for | |
| documentation, engineering analysis, and workflow automation. | |
| Core Capabilities | |
| - Long-context reasoning (200k tokens) | |
| - Code generation and API reasoning | |
| - Retrieval-augmented responses | |
| - Document drafting, editing, and summarization | |
| - Secure, role-based enterprise integration | |
| Architecture Summary | |
| AuroraAI is a 70B-parameter transformer model featuring: - Hierarchical | |
| and sliding-window attention mechanisms - 8192-dimensional hidden | |
| layers - 96 transformer blocks - 64 attention heads - SwiGLU-activated | |
| feed-forward networks - Rotary positional embeddings with extended | |
| scaling | |
| Training Methodology | |
| AuroraAI was trained using: - Autoregressive next-token prediction - A | |
| mixture of technical documentation, code corpora, research texts, RFCs, | |
| and synthetic alignment sets - AdamW optimization with warmup and cosine | |
| decay - Distributed training across 1024–4096 GPUs - BF16 | |
| mixed-precision gradient computation - Redundant asynchronous | |
| checkpointing | |
| Evaluation Results | |
| - HumanEval+ accuracy: 86% | |
| - Document QA improvement: +22% over baseline | |
| - Retrieval fidelity: 99% at 200k-token context | |
| - Hallucination rate reduction: 38% via strict retrieval routing | |
| Safety & Alignment | |
| AuroraAI employs: - Supervised fine-tuning on technical reasoning | |
| tasks - Reinforcement learning from human preferences - Multi-layer | |
| content filtering - Policy-driven guardrails engine - Encrypted opt-in | |
| user memory with immediate deletion controls | |
| Intended Use Cases | |
| AuroraAI performs best in: - Developer documentation workflows - API | |
| lifecycle and architectural content creation - Engineering ticket | |
| summarization - Knowledge retrieval from large document bases - | |
| Enterprise search and analysis tasks | |
| Limitations | |
| AuroraAI is not optimized for: - Unsupervised high-risk autonomous | |
| decision making - Medical, legal, or hazardous domain outputs without | |
| human review - Context-free speculation or queries lacking grounding | |
| data | |
| Version Information | |
| AuroraAI v1.0 - Expanded to 200k token window - Upgraded retrieval | |
| engine - Added enterprise integration modules | |