Configure Local LLM Inference and Fine-Tuning

v20260707

llm-config

This tool provides comprehensive configuration for running Large Language Models (LLMs) locally using RuVLLM. It supports initial model setup, specialized fine-tuning via MicroLoRA adapters for domain-specific tasks, and continuous, real-time adaptation using SONA. Use it when transitioning from base models to highly specialized, adaptive AI systems requiring precise parameter control.

LLM Inference Fine-Tuning RuVLLM MicroLoRA SONA AI Configuration

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Overview

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

Check status — call mcp__claude-flow__ruvllm_status to see current model and adapter state
Generate config — call mcp__claude-flow__ruvllm_generate_config with model parameters
Create MicroLoRA — call mcp__claude-flow__ruvllm_microlora_create for task-specific adapters
Adapt MicroLoRA — call mcp__claude-flow__ruvllm_microlora_adapt with training data
Create SONA — call mcp__claude-flow__ruvllm_sona_create for real-time neural adaptation
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

Info

Category Artificial Intelligence

Name llm-config

Version v20260707

Size 1.57KB

Source ruvnet/ruflo

Updated At 2026-07-09