A careful, end-to-end guide to building a private, local language model that writes in a late parent's voice — from recovering and cleaning years of blog posts, to QLoRA fine-tuning on an RTX 5090, to grounding it with retrieval, packaging it as a GGUF, and sharing it with siblings over Tailscale. Includes the honest limits and the ethics that should shape every decision.
Lora
-
Preserving a Voice: Fine-Tuning a Local LLM on a Loved One's Writing -
Fine-Tuning LLMs on Your Own Hardware LoRA and QLoRA explained, unsloth for efficient fine-tuning, dataset preparation, evaluation, merging adapters, and when fine-tuning actually beats RAG — a practical guide to training language models on consumer and prosumer hardware.
-
Fine-Tuning Small Models: When and Why to Fine-Tune vs. Prompt Engineer A practical guide to fine-tuning small language models — understanding when fine-tuning beats prompt engineering, how LoRA and QLoRA work, training a model on your own data with Unsloth or Axolotl, and deploying the result with Ollama.