People: David Idea: Based on conversations with Keao about machine-translation benchmarking, I ran a bunch of LLM models through MMLU-ProX (lite) French benchmark tests (biology section + full 14-topic suite) to see which ones actually deliver the best mix of speed and accuracy in French. This started because we hypothesized that
People * David Idea Exploring memory-efficient fine-tuning techniques for improving Hawaiian-to-English translation using Apple's MLX framework, comparing multiple approaches and optimizing for Mac hardware. Details * Successfully fine-tuned gemma-3-4b-it-4bit on Mac M1 Ultra (128GB RAM) achieving 0.8296 semantic similarity score, a 3.6% improvement over the base model * Discovered
People: * David Idea: * Comparing fine-tuning performance on MacBook M3 Max, Mac Studio M1 Ultra, and Nvidia 4090 using MLX and Unsloth Details: * Tested fine-tuning Phi-3-mini-4k-instruct model * Followed this Jan 2025 MLX guide for Apple hardware * Used Unsloth library for Nvidia GPU * Dataset had 627 examples and used 500 training steps
People David Idea Exploring cloud GPU performance, cost, and usability for running open-source AI models (in comparison to local hardware and in context of recent learning on software to handle concurrent users) Details * Compared RunPod serverless and pods using Nvidia (vLLM) and AMD (sglang) * Benchmarked Nvidia 3090, 4090, RTX 6000
People: * David Idea: * Comparing two open-source projects, Onyx and Morphik, for self-hosted semantic search (and deep research) across Google Drive files (PDFs, Docs, Sheets, and Slides) Details: * Onyx: Great text-based search, flexible embedding models * Morphik: Powerful multi-modal search (text, images, graphs) * Onyx easily connects and syncs with Google Drive * Morphik