I specialize in Knowledge Distillation, On-Device Inference, and Computer Vision. My focus is building systems that run heavy compute workloads efficiently on the Edge (iOS/Android/Linux).

A privacy-first mobile LLM runtime capable of running 1B+ parameter models offline.

• Distillation: Built a pipeline to distill reasoning capabilities from DeepSeek-R1 into a 1.7B parameter Qwen model.
• Optimization: Manually quantized models to 4-bit GGUF, reducing memory footprint to <1.2GB while maintaining 95% of reasoning benchmarks.
• Systems: Implemented a C++ FFI bridge to link the underlying inference engine directly to the Flutter UI, achieving 20 tokens/s on consumer mobile hardware.

An NLP engine that maps abstract human sentiment to Spotify Audio Features.

• Architecture: Replaced standard keyword search with a semantic vector mapping system (User Prompt -> Valence/Energy/Danceability Vectors).
• Integration: deeply integrated with the Spotify Web API to generate algorithmic playlists based on "Vibe" rather than Genre.
• Stack: Flutter, Python (NLP Middleware), Spotify OAuth 2.0.

High-performance object detection system for 3D spatial objects.

• CV Pipeline: Trained a custom YOLOv8 model using Oriented Bounding Boxes (OBB) to track Rubik's cubes in dynamic lighting.
• Deployment: Converted PyTorch weights to ONNX for optimized cross-platform execution on mobile NPU/CPUs.
• Data Ops: Built an active learning loop using Label Studio to correct model drift.

• Bible_Extractor (ETL): A T5-based NLP pipeline for normalizing and structuring unstructured archaic text data.
• Jemma: CLI-based developer tooling for automating data validation workflows.

• Symbolic Distillation: Experimenting with distilling "Chain of Thought" reasoning from 70B models into <3B mobile-class models.
• Edge Quantization: Benchmarking perplexity degradation in GGUF vs. AWQ formats for mobile NPUs.

• GitHub: ThatLinuxGuyYouKnow
• LinkedIn: LinkedIn