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
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

Feature	MicroLoRA	SONA
What it is	Ultra-light LoRA adapter, rank 1-4	Instant adaptation loop (the tool describes <1ms cycles)
Input	quality feedback per call	quality feedback per call
Persistence	None: instances live in the MCP server's memory and are lost on restart	None: same

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.