Why does my local LLM keep reverting to generic prose despite extensive author-style examples and prompt engineering? Am I overengineering this?
Hi everyone, I’ve been developing a local AI-assisted creative writing application called Dockwriter, built around llama.cpp and GGUF models. One of its central goals is to reproduce an individual author’s writing style without fine-tuning or training a LoRA. After months of development, I’m beginn…
Read the full story at r/LocalLLM ↗
Timeline · 1 report
- 2026-10-30 01:17 · r/LocalLLM
Why does my local LLM keep reverting to generic prose despite extensive author-style examples and prompt engineering? Am I overengineering this?
More stories
- Let a small model critique and revise its own answers, then fine-tune on the revisions (on a rented GPU) — DEV Community — Machine Learning
- Local Qwen 3.8 27B as the worker, Opus as the planner: same 38 tests passed, half the API bill — r/LocalLLM
- Janas: a C inference engine that runs MoE models bigger than your RAM by streaming experts from NVMe, plus an almost complete OpenAI-compatible server, MCP tools, vision and speech (GPL-3.0) — r/LocalLLM
- I tested a 12B open-source AI Humanizer locally. Here's what I found. — r/learnmachinelearning
- I tested LFM2.5-24B-A2B vs Qwen3.6-35B-A3B on 75 fresh multi-step tool tasks. Qwen solved 91%, LFM2.5 49%, and LFM2.5 said "done" on most of its failures — r/LocalLLM
- I trained LLaMa from scratch — r/deeplearning
- Basalt (NVFP4) vs Unsloth Studio (llama.cpp IQ4_XS) — 5090 with 64GB RAM — r/LocalLLM
- Gemma 3 1B on the Nexus 5X (Directly on the Linux Kernel, No Android) — 2 GB RAM, 4.47 t/s — r/LocalLLM
Get the daily brief of stories like this at 6:30 every morning →