We fine-tuned an 8B parameter model for domain-specific engineering documentation. Full parameter fine-tuning required 4x A100 GPUs which exhausted our lab credits in 3 days. With LoRA (rank 16, alpha 32) targeting attention projections, we ran it on a single RTX 4090 with 4-bit quantization with barely a 1.2% drop in benchmark evaluation. LoRA is a lifesaver for student researchers.

If your dataset has under 100,000 vectors, PostgreSQL with `pgvector` and HNSW indexing is more than enough and keeps your infrastructure single-node. Once you need sub-10ms hybrid lexical + dense vector search across millions of documents with dynamic payload filtering, dedicated engines like Qdrant shine. Keep your architecture simple until scale demands it.

Flow matching has gained massive traction over traditional score-based diffusion because of its straight-line probability paths and faster ODE sampling. Anyone experimenting with continuous normalizing flows for audio or image generation? Let's discuss in the thread.

Working on an edge computing project to detect empty study desks in our central library using a Raspberry Pi camera. Are there public datasets with annotated overhead classroom/desk imagery, or should we collect and annotate 500 images using CVAT ourselves?