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Szymon Ruciński

PhD researcher in Edge AI and chip design at ETH Zurich 🎓

About me

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Szymon Ruciński is a PhD student at ETH Zürich Department of Electrical Engineering and CSEM. I work and do my PhD as part of the prestigious SwissChips Program to develop advanced chips and efficient edge AI algorithms for machine perception in drones, robotics, and mobile devices. With 5+ years of working experience, I worked as a Machine Learning Architect at Accenture US and at Samsung R&D as a Machine Learning Engineer with world-renowned companies.
I am currently working on embedded AI inference chips and efficient perception AI models. I am based in Zürich and often travel to Asia and the USA. I offer machine learning services helping startups, implement AI strategies for enterprises, build custom ML solutions, and conduct workshops and university mentoring. I have German work proficiency and English fluency. Currently I am learning basic Chinese.
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AI Engineering Services

AI Strategy & Architecture

Develop a comprehensive AI roadmap aligned with your business goals. I help identify opportunities for AI integration and create actionable implementation plans.

  • AI readiness assessment
  • Use case identification
  • ROI projections
  • Implementation roadmap

LLM & Generative AI

Leverage the power of Large Language Models and Generative AI. Build chatbots, content generators, and intelligent automation systems.

  • GPT integration
  • Custom fine-tuning
  • Prompt engineering
  • RAG systems

MLOps & Deployment

Ensure your AI models perform reliably in production. I provide end-to-end MLOps solutions for model deployment, monitoring, and optimization.

  • Model deployment
  • Performance monitoring
  • A/B testing
  • Scalability optimization

AI Training & Workshops

Empower your team with AI knowledge. Customized training programs and workshops to build internal AI capabilities and best practices.

  • Custom curriculum
  • Hands-on exercises
  • Team mentoring
  • Certification

AI Audit & Optimization

Optimize existing AI systems for better performance and ROI. Comprehensive audits to identify improvements and cost-saving opportunities.

  • Performance analysis
  • Cost optimization
  • Security review
  • Improvement roadmap

Ready to Transform Your Business with AI?

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My Works and Hobbies

The Polish Large Language Model
AI

The Polish Large Language Model

A state-of-the-art Polish language model trained on a large dataset of Polish text.

🤖 Krakowiak-7B: Polish Large Language Model

2023 Open Source 50K Instructions

📋 Project Overview

Krakowiak-7B is an open-source Polish Large Language Model, specifically trained to understand and generate high-quality Polish text.

I trained this model on a carefully curated and updated dataset of approximately 50,000 Polish instructions, making it one of the most capable and comprehensive Polish language models available in the open-source community.

🚀 Live Demo

Try the model below! If it doesn't generate an answer immediately, please retry 😄

✨ Key Features

Native Polish Understanding

Trained specifically on Polish language patterns and nuances

50K Instructions Dataset

Comprehensive training on diverse Polish instructions

Open Source

Freely available for research and commercial use

7B Parameters

Optimal balance between performance and efficiency

🔧 Technical Details

Model Architecture

Based on state-of-the-art transformer architecture optimized for Polish language generation

Training Process

Fine-tuned using advanced techniques including LoRA and gradient checkpointing

Performance

Achieves state-of-the-art results on Polish language benchmarks

Recognize Voice tone with AI
AI

Recognize Voice Tone with AI

An AI system that can recognize emotions from voice recordings.

🧪 Try to predict you emotions using experimental AI 🤖

The goal of the following project is to build a production ready API and application. That based on the audio is capable to classify customers emotions based on the recordings.

Model was trained on images that are MEL spectrograms of audio files. Model is trained from scratch and uses AlexaNet architecture to classify emotions

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Let's Build Your AI Solution