skills.rest
ruvnet
ruvnetCOMMUNITY
Explore all skills by this author →
Website𝕏@ruvGitHub

llm-config

Configure RuVLLM local inference with model selection, MicroLoRA fine-tuning, and SONA adaptation.

70.1k|8.4k|Updated Jun 2, 2025
Search Tags:#fine-tuning#adapter#model configuration#lLM#local inference#Lora#ruvllm
One-click install
npx skills add https://github.com/ruvnet/claude-flow --skill llm-config
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-config
Source: https://github.com/ruvnet/claude-flow/tree/main/plugins/ruflo-ruvllm/skills/llm-config
Command: npx skills add https://github.com/ruvnet/claude-flow --skill llm-config

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Setting up local LLM inference and choosing between fine-tuning approaches is complex, requiring knowledge of model parameters, adapter types, and adaptation strategies. This Skill streamlines the configuration of RuVLLM for local inference and guides the creation of MicroLoRA and SONA adapters.

Core Features & Use Cases

  • Status Inspection: Check the current model and adapter state before making changes.
  • Config Generation: Generate RuVLLM configuration with specific model parameters.
  • MicroLoRA Fine-Tuning: Create and adapt task-specific LoRA adapters trained on your data.
  • SONA Adaptation: Set up real-time neural adaptation driven by continuous feedback signals.
  • Use Case: You want a local model specialized for your domain. Check the current status, generate a config for your chosen model, create a MicroLoRA adapter, and adapt it with your training data for persistent task-specific behavior.

Quick Start

Ask the assistant to check the current RuVLLM status and generate a configuration for your chosen model with a MicroLoRA adapter.

Frequently Asked Questions about llm-config

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I configure RuVLLM for local LLM inference?▼

Start by checking the current state with the ruvllm_status tool, then call ruvllm_generate_config with your desired model parameters. This produces a configuration for local inference that you can extend with adapters.

What is the difference between MicroLoRA and SONA?▼

MicroLoRA performs task-specific fine-tuning that takes minutes to train and persists as saved adapter weights. SONA applies real-time micro-adjustments in under 0.05ms but is session-scoped, making it suited for continuous feedback loops.

When should I use MicroLoRA instead of SONA?▼

Use MicroLoRA when you need persistent, specialized domain behavior trained on your own data. Use SONA when you need fast, temporary adaptation driven by live feedback signals within a single session.

How do I fine-tune a local model with my own training data?▼

Create a MicroLoRA adapter with ruvllm_microlora_create, then call ruvllm_microlora_adapt with your training data. The resulting adapter weights are saved and can be reused for task-specific inference.

Does SONA adaptation persist between sessions?▼

No, SONA adaptation is session-scoped and does not persist. For durable model specialization, use MicroLoRA, which saves adapter weights that survive across sessions.

Related Skills

Explore More Software Engineering Skills→
SignmanalSignmanal
1

llama-cpp

Run local GGUF model inference with llama.cpp across CPU and GPU hardware.

Community
Intermediate
7Lemaitre77Lemaitre7

astro

Create .astro components and configure islands for content-driven websites.

Community
Intermediate
curiositechcuriositech
10

dag-iteration-detector

Analyzes task outputs to decide whether re-execution is needed and selects a strategy.

Official
Advanced
JamesFincherJamesFincher

peft-fine-tuning

Train low-rank adapters for parameter-efficient fine-tuning of large language models.

Community
Advanced