Skills Artificial Intelligence Configure LLM Inference and Fine-Tuning

Configure LLM Inference and Fine-Tuning

v20260716
llm-config
Provides comprehensive configuration management for local LLM inference using RuVLLM. Users can select models, perform task-specific fine-tuning with MicroLoRA adapters, or implement continuous, real-time adaptation using SONA. This tool guides the entire advanced LLM workflow, covering status checks, configuration generation, and advanced adaptation loops.
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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

  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 for task-specific adapters
  4. Adapt MicroLoRA — call mcp__plugin_ruflo-core_ruflo__ruvllm_microlora_adapt with training data
  5. Create SONA — call mcp__plugin_ruflo-core_ruflo__ruvllm_sona_create for real-time neural adaptation
  6. Adapt SONA — call mcp__plugin_ruflo-core_ruflo__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
Name llm-config
Version v20260716
Size 1.71KB
Updated At 2026-07-18
Language