Generate RuVLLM settings for local language model inference and create MicroLoRA or SONA instances. Adjust their small internal weights using quality scores as feedback, with instances held in server memory for subsequent adaptation calls.
Configure RuVLLM local inference helpers: generate a config, and create and adapt in-process MicroLoRA and SONA instances from quality signals. Use when you need local, air-gapped inference tooling, not for fine-tuning hosted models.
Review the instructions the agent will receive before you install.
Configure RuVLLM for local inference and fine-tuning.
When you need to configure local LLM inference, create MicroLoRA adapters for task-specific fine-tuning, or set up SONA for real-time adaptation.
mcp__plugin_ruflo-core_ruflo__ruvllm_status to see current model and adapter state. If wasm.available is false, install the add-on first (npm i -g @ruvector/ruvllm-wasm, or without -g in a project) — the MicroLoRA and SONA tools cannot run without itmcp__plugin_ruflo-core_ruflo__ruvllm_generate_config with model parametersmcp__plugin_ruflo-core_ruflo__ruvllm_microlora_create with inputDim, outputDim, rank (1-4, default 2) and alphamcp__plugin_ruflo-core_ruflo__ruvllm_microlora_adapt with loraId and a quality signal (0.0-1.0), optional learningRate and success. It takes a feedback score, not a training setmcp__plugin_ruflo-core_ruflo__ruvllm_sona_create with hiddenDim (default 64), learningRate and patternCapacitymcp__plugin_ruflo-core_ruflo__ruvllm_sona_adapt with sonaId and a quality signal (0.0-1.0)| Feature | MicroLoRA | SONA |
|---|---|---|
| What it is | Ultra-light LoRA adapter, rank 1-4 | Instant adaptation loop (the tool describes <1ms cycles) |
| Input | quality feedback per call | quality feedback per call |
| Persistence | None: instances live in the MCP server's memory and are lost on restart | None: same |
Neither tool fine-tunes a hosted model or writes adapter files; both adjust small in-process weights from quality scores. Keep the returned loraId / sonaId for later calls, and re-create them after a restart. For the repo's measured SONA latency see docs/reviews/intelligence-system-audit-2026-05-29.md.
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Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.
Create an Architecture Decision Record, a document for recording important design choices, with the next sequential number. It generates a Markdown template, registers the record in AgentDB, and links related decisions found through search.
Build a searchable index and relationship graph from ADRs, records of architectural decisions. Import two supported document formats, refresh existing metadata without duplicate relationships, and review summaries of statuses, missing references, and inconsistencies.
Rebuild the index of ADRs, records of architectural decisions, after files or relationship lines are deleted. It clears stored records and relationships, indexes the files still present, and checks that the final record count matches.
Review code changes against accepted Architecture Decision Records, documents that capture agreed design choices. Report violations, outdated references, compliant changes, and areas without ADR coverage, then suggest code fixes or a new decision record where appropriate.
Check saved architecture decision records (ADRs) for broken references, circular replacement relationships, and inconsistent statuses. Use the results to verify imported records or stop a CI check when selected integrity rules fail.