Aurelius: A Full-Stack LLM Platform Covering 20 Technical Domains

This article introduces Aurelius—a 1.4B parameter Agentic LLM platform validated through 132 iterations and over 20,400 test cases. It covers 20 technical domains including model architecture, training optimization, inference acceleration, alignment & security, aiming to solve the pain point of developers integrating multiple tech stacks and provide a unified engineering framework.

With the rapid development of LLM technology, developers face challenges: how to integrate over 20 technical domains (model architecture, training process, inference optimization, alignment strategies, security mechanisms, etc.) into a unified framework? Aurelius was born to address this pain point, transforming cutting-edge research into ready-to-use engineering components.

Aurelius's codebase is divided into 20 core modules covering all stages of the LLM lifecycle:

• Core Model Architecture: Transformer lineage (GQA, RoPE/YaRN, three MoE modes, dynamic sparse attention, etc.), integrating SSM family (Mamba, S4, RWKV, and over 150 architecture modules);
• Training Infrastructure: Over 200 training tools (optimizers like Muon/AdamW, asynchronous RL trainers, active learning, RLHF, PEFT methods, etc.);
• Inference Optimization: Over 200 inference modules (speculative decoding, Chain of Draft, KV quantization, RAG, etc.);
• Alignment & Security: Over 150 alignment modules (DPO, GRPO, RLHF, etc.), security modules include jailbreak detectors and prompt injection scanners.

Highlights of Aurelius's engineering practices in core modules:

• Model Architecture: MoE supports load balancing and expert upgrade/recycling; RoPE/YaRN positional encoding supports longer context; unified interface for SSM family, facilitating architecture comparison;
• Training System: Gradient checkpointing and sequence packing lower hardware barriers; full support for PEFT (LoRA+, DoRA, ReLoRA, etc.);
• Inference Optimization: Multiple speculative decoding variants (tree-based, Eagle, Medusa); paged KV cache reduces memory fragmentation, combined with INT8 quantization to lower memory usage.

Aurelius values model reliability:

• Evaluation Modules: Over 100 components (LM Harness, BERTScore, LLM-as-Judge, causal tracing, ROME weight editing, etc.);
• Interpretability Tools: Over 20 tools (activation patching, circuit discovery, LEACE concept erasure, logit lens, neuron analysis, etc.) to help understand the internal mechanisms of models.

Aurelius's 24 security modules cover end-to-end needs: gradient inversion attack defense, model extraction protection, STRIP backdoor detector, GCG adversarial suffix search, canary memory audit, prompt injection detection, random smoothing, Rényi differential privacy accounting, PII/toxic output scanning, adversarial text augmentation, etc.

Value of Aurelius for different users:

• Researchers: A unified experimental platform to avoid experimental biases caused by codebase differences;
• Engineering Teams: Modular design for on-demand use (e.g., inference optimization, secure deployment modules);
• Learners: Educational resource for systematically understanding modern LLM tech stacks.

Aurelius is an important milestone in open-source LLM infrastructure. By systematically integrating scattered cutting-edge technologies through engineering, it becomes a usable, scalable, and maintainable unified platform. Polished through 132 iterations and over 20,400 test cases, it is not only a toolset but also a knowledge base recording the evolution of LLM technology from 2023 to 2026.