Llm Config
- 652 installs
- 67k repo stars
- Updated August 4, 2026
- ruvnet/ruflo
llm-config is a Claude Flow skill that configures RuVLLM local inference with model selection, MicroLoRA fine-tuning, and SONA adaptation for developers who need on-machine LLM setup inside agent workflows.
About
llm-config is a RuFlo Claude Flow skill that configures RuVLLM for local inference and fine-tuning. It exposes ruvllm_generate_config, ruvllm_status, ruvllm_microlora_create, ruvllm_microlora_adapt, ruvllm_sona_create, and ruvllm_sona_adapt through claude-flow MCP, with CLI arguments --model MODEL and --adapter microlora or sona. Developers use llm-config when standing up local inference instead of cloud APIs, creating MicroLoRA adapters for task-specific fine-tuning, or enabling SONA for real-time adaptation. Bash access supports complementary setup commands. The skill targets teams embedding custom models in claude-flow agent pipelines where model config, adapter creation, and adaptation must happen inside the same toolchain as swarm and routing skills.
- llm-config
Llm Config by the numbers
- 652 all-time installs (skills.sh)
- +6 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #573 of 4,347 Backend & APIs skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 652 |
|---|---|
| repo stars | ★ 67k |
| Last updated | August 4, 2026 |
| Repository | ruvnet/ruflo ↗ |
How do you configure local RuVLLM inference adapters?
Use llm-config for development tasks
Who is it for?
Developers deploying RuVLLM local inference with MicroLoRA or SONA adapters inside claude-flow agent pipelines.
Skip if: Developers relying solely on hosted cloud LLM APIs without local RuVLLM or claude-flow ruvllm MCP endpoints.
When should I use this skill?
Local LLM inference setup, MicroLoRA adapter creation, or SONA real-time adaptation is requested.
What you get
RuVLLM configuration files, MicroLoRA or SONA adapters, and ruvllm_status readiness output.
- RuVLLM configuration
- MicroLoRA or SONA adapters
Files
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 |
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Pick llm-config over generic env-var setup when RuVLLM MicroLoRA and SONA adapters must be created through claude-flow MCP tooling.
FAQ
What adapters does llm-config support?
llm-config supports MicroLoRA for task-specific fine-tuning and SONA for real-time adaptation. Use --adapter microlora or --adapter sona with ruvllm_microlora_create or ruvllm_sona_create MCP calls.
How does llm-config verify local inference readiness?
llm-config calls ruvllm_status after ruvllm_generate_config to confirm RuVLLM is configured. Developers select models with --model MODEL before creating or adapting MicroLoRA or SONA adapters.