Not fine-tuned. Not a downloaded checkpoint. Random weights → working speech, on a 16GB laptop with an 8GB GPU, trained on ~29,000 conversation pairs built over four months. The memory side (built first) Before the model, we built a memory store — currently 33,941 episodes. The architecture is neurological: Half-life decay: memories fade unless recalled Recall strengthening: access resets the decay clock 30-day consolidation threshold for long-term storage 4 LRU working-memory slots Forget table: writes a receipt before anything fades permanently Not a lookup. A system that behaves the way memory actually behaves. The model — two runs Run 1: 842,624 parameters. d_model 128, 4 layers, 4 heads, context 256. Val loss 5.11 → 0.9576 at step 23,750. Halted when the train/val gap opened — overfitting signal, correct call. Run 2: 4,833,024 parameters. d_model 256, 6 layers, 8 heads. Best val: 0.7978 at step 21,500. Key finding: parameters were the bottleneck, not data. The corpus was sufficient. The model just needed to be bigger. Wiring the halves home_llm_speak.py connects the memory store to the generation model — retrieves memories via salience search, passes them as context, then generates. First output from the combined system was grammatical, was grammatical, used household vocabulary, and wasn’t copied from the retrieved memories*.* That’s not retrieval. That’s the model having absorbed four months of how this household talks. Why this matters I needed a model that knows us because it learned from what we wrote as ourselves — not from generic pretraining, not fine-tuned from someone else’s base. The memory architecture has to be structural from the start because autonomous consolidation can’t be bolted on afterward. Last night was proof the approach works. The model is small. It’ll get bigger. But what it already learned is ours. Hardware: 16GB laptop, RTX 5050 Laptop 8GB. No cloud. No API calls. Everything local. Next: wider context window, books into the memory store (streamed, not in training corpus — kept separate to preserve corpus balance), bigger model. If anyone has done this — trained from scratch on household data with a custom memory architecture — I want to know what you learned. submitted by /u/Pale-Inflation360
Originally posted by u/Pale-Inflation360 on r/ClaudeCode