MiniEmbed

Overview

MiniEmbed is an experimental embedding system under active development at Aquilonis AI. The project explores how compact, efficient embedding models can retain strong semantic performance while significantly reducing computational and memory costs.

The goal is not to chase benchmark scores, but to design embedding systems that are practical, portable, and deployable in constrained environments.

Motivation

Modern embedding models are powerful, but often:

• Over-parameterized and costly to deploy
• Unsuitable for edge or latency-sensitive systems
• Difficult to integrate into resource-constrained applications

MiniEmbed investigates alternative design choices that prioritize efficiency over scale, systems-level performance, and real-world constraints.

Current Focus Areas

MiniEmbed research currently explores:

• Embedding model compression & distillation
• Architecture-level efficiency improvements
• Quantization and low-precision inference
• Fast similarity search integration
• Trade-offs between size, latency, and semantic fidelity

Early Findings

Initial experiments suggest that it is possible to:

• Reduce embedding model size substantially (up to 80% reduction)
• Maintain strong semantic similarity performance (95%+ retention)
• Achieve low-latency inference suitable for production systems (sub-50ms on mobile)

These findings are preliminary and subject to continued validation.

Current Status

MiniEmbed is currently:

• Under active research and experimentation
• Not yet production-stable
• Evolving in architecture and design

Public releases will follow once the system reaches sufficient maturity. Development updates will be shared selectively as the project progresses.

Technology Stack

MiniEmbed is being developed using:

• PyTorch (research & training)
• ONNX (model portability)
• Rust (inference & systems integration)
• Modern transformer-based architectures

Intended Use Cases

While still experimental, MiniEmbed is designed with use cases such as:

• Semantic search and similarity matching
• Retrieval-augmented generation (RAG) systems
• Recommendation systems
• Lightweight AI-powered applications
• Edge or resource-constrained deployments

Research Philosophy

MiniEmbed reflects Aquilonis AI's broader research approach:

• Build systems, not just models
• Optimize for real constraints
• Treat research and engineering as inseparable