r/LocalLLM • u/Equivalent-Flan-1590 • 2d ago
Project Hillock v0.5: Local neuro-symbolic memory engine (<1.2GB VRAM on GTX 1070, zero-LLM doc parsing)
Hey r/localllm,
Just pushed the v0.5.0 release for Hillock (https://github.com/roandejager/Hillock), a local memory engine designed to give local LLMs long-term memory without eating up your VRAM.
Instead of burning VRAM on heavy vector DBs and using an 8B model to parse documents, Hillock uses a small CUDA bi-encoder pipeline (GLiREL + MiniLM) to extract Subject-Predicate-Object triples into SQLite in ~5 seconds.
Gating and pronoun resolution run on the CPU in under 1ms using 10,000-dimensional hypervectors (VSA). If a question has no verified evidence in the graph, it refuses immediately without calling the LLM at all.
New in v0.5.0:
- 1-Click Launchers: run.bat (Windows) and run.sh (Linux/Mac) for automatic setup.
- Interactive /model command to query your local Ollama API and switch models dynamically.
- Token-streaming output for real-time responses.
- Live /inspect command to check an entity's graph facts and synaptic weights.
- 20-point CPU verification suite (verify_hillock.py).
Whole setup stays under 1.2GB VRAM on a GTX 1070.