llm-config

Imported from ruvnet/ruflo (plugins/ruflo-ruvllm/skills/llm-config/SKILL.md) via skills.sh. Install upstream with npx skills add ruvnet/ruflo --skill llm-config. Copyright stays with the author.

name: llm-config description: Configure RuVLLM local inference helpers: generate a config, and create and adapt in-process MicroLoRA and SONA instances from quality signals. Use when you need local, air-gapped inference tooling, not for fine-tuning hosted models. argument-hint: "[--model MODEL] [--adapter microlora|sona]" allowed-tools: mcp__plugin_ruflo-core_ruflo__ruvllm_generate_config mcp__plugin_ruflo-core_ruflo__ruvllm_status mcp__plugin_ruflo-core_ruflo__ruvllm_microlora_create mcp__plugin_ruflo-core_ruflo__ruvllm_microlora_adapt mcp__plugin_ruflo-core_ruflo__ruvllm_sona_create mcp__plugin_ruflo-core_ruflo__ruvllm_sona_adapt Bash

LLM Configuration

Configure RuVLLM for local inference and fine-tuning.

When to use

When you need to configure local LLM inference, create MicroLoRA adapters for task-specific fine-tuning, or set up SONA for real-time adaptation.

Steps

1. Check status — call mcp__plugin_ruflo-core_ruflo__ruvllm_status to see current model and adapter state. If wasm.available is false, install the add-on first (npm i -g @ruvector/ruvllm-wasm, or without -g in a project) — the MicroLoRA and SONA tools cannot run without it
2. Generate config — call mcp__plugin_ruflo-core_ruflo__ruvllm_generate_config with model parameters
3. Create MicroLoRA — call mcp__plugin_ruflo-core_ruflo__ruvllm_microlora_create with inputDim, outputDim, rank (1-4, default 2) and alpha
4. Adapt MicroLoRA — call mcp__plugin_ruflo-core_ruflo__ruvllm_microlora_adapt with loraId and a quality signal (0.0-1.0), optional learningRate and success. It takes a feedback score, not a training set
5. Create SONA — call mcp__plugin_ruflo-core_ruflo__ruvllm_sona_create with hiddenDim (default 64), learningRate and patternCapacity
6. Adapt SONA — call mcp__plugin_ruflo-core_ruflo__ruvllm_sona_adapt with sonaId and a quality signal (0.0-1.0)

MicroLoRA vs SONA

Neither tool fine-tunes a hosted model or writes adapter files; both adjust small in-process weights from quality scores. Keep the returned loraId / sonaId for later calls, and re-create them after a restart. For the repo's measured SONA latency see docs/reviews/intelligence-system-audit-2026-05-29.md.