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Eulogik ๐Ÿง 

Edge AI Infrastructure & Quantitative Systems Engineering

Website GitHub


We engineer hyper-efficient, domain-specialized, and edge-deployable AI models.
No bloated parameter sizes. No massive cloud dependencies. Just high-performance models designed to run locally, securely, and cost-effectively.


๐Ÿ”ฌ Active Research & Model Portfolios

We focus on three primary domains of edge-optimized machine intelligence:

1. ๐Ÿ“„ Visual Document Understanding (VDU)

  • TinyDoc-VLM-256M: A compact 290M-parameter vision-language model trained on SigLIP and SmolLM2. Optimized for document OCR, layout parsing, and information extraction.
  • TinyDoc-VLM-LoRA: Parameter-efficient adapters for custom corporate legal and administrative document compliance.

2. ๐Ÿ“ˆ Time Series & Quantitative Forecasting

  • NanoForecast: A lightweight time-series foundation model family ranging from 200K to 6.5M parameters. Supports streaming RNN inference and zero-shot forecasting, optimized for running directly on edge hardware like Raspberry Pi.

3. ๐Ÿก Intelligent LLM Routing

  • fugusashi: An open-source, federated, human-interpretable model router. Learns user preferences and routes requests to the most optimal model, slashing inference costs by up to 70%.

4. ๐Ÿ‡ฎ๐Ÿ‡ณ Localized & Multilingual LLMs

  • Bharat-Tiny-LLM: An ultra-compact 240M-parameter language model specialized for Hinglish and Indian cultural context.

โšก Developer Demos (Spaces)

Test our models directly in your browser:


๐Ÿข Licensing & Enterprise Collaboration

All our core public models are open-sourced under Apache 2.0 or MIT licenses.

For enterprise-grade integration, private custom model training, or commercial licensing of specialized components (such as our high-frequency quant trading predictors and legal document ingestion pipelines), get in touch:

ยฉ 2026 Eulogik. Engineered for efficiency.