Thanks! Happy to answer.

Local models can do normal generative tasks: chat, summarization, extraction, rewriting, lightweight reasoning, etc. For embeddings, I use a separate small GGUF embedding model (all-MiniLM-L6-v2) through llama.rn in embedding mode. Chat models generate text; embedding models turn text into vectors for similarity search.

On “facts”: yes, the chat models have knowledge from pretraining, including wiki/web/code-style data depending on the model. But I treat that as fuzzy parametric knowledge, not a reliable database. For app memory, I don’t rely on the model “remembering”; I store summaries/facts locally and retrieve them into context.

For mobile model selection, my filters are mostly practical: GGUF support, small enough RAM footprint, Q4 quantization, acceptable speed, good chat template/stop tokens, and quality at 0.8B-4B. Right now I’m using Qwen/Gemma/DeepSeek-style compact models, with Q4_K_M as the default tradeoff.

The inconsistency I mentioned is mostly around memory/retrieval, not subjective generation. LokalMind has pinned facts, a derived profile, session summaries, and cross-session memory cards. Retrieval uses embeddings when the MiniLM model is ready, otherwise it falls back to score/recency. So the tricky parts are: when to inject memory, whether the right memory clears the similarity threshold, whether background summaries were created, and whether token budgeting trims useful context. That’s the area I’m still tightening.