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__claude-flow__ruvllm_status to see current model and adapter state
2. Generate config — call mcp__claude-flow__ruvllm_generate_config with model parameters
3. Create MicroLoRA — call mcp__claude-flow__ruvllm_microlora_create for task-specific adapters
4. Adapt MicroLoRA — call mcp__claude-flow__ruvllm_microlora_adapt with training data
5. Create SONA — call mcp__claude-flow__ruvllm_sona_create for real-time neural adaptation
6. Adapt SONA — call mcp__claude-flow__ruvllm_sona_adapt with feedback signals

MicroLoRA vs SONA

Feature	MicroLoRA	SONA
Speed	Minutes to train	<0.05ms adaptation
Scope	Task-specific fine-tuning	Real-time micro-adjustments
Persistence	Saved as adapter weights	Session-scoped
Use case	Specialized domain tasks	Continuous feedback loops