micro-kiki-v3

micro-kiki

35-domain expert model built on Qwen3.5-35B-A3B (MoE, 256 experts, 3B active/token) with LoRA adapters and a cognitive layer (memory palace + negotiator + anti-bias).

Model Description

micro-kiki is a multi-domain language model designed for technical applications spanning electronics, firmware, CAD, manufacturing, and general-purpose conversation. It uses a router-based architecture that selects up to 4 domain-specific LoRA stacks per request.

Architecture

                         +-------------------+
                         |   Domain Router   |
                         | (classifier, top4)|
                         +--------+----------+
                                  |
              +----------+--------+--------+----------+
              |          |                 |          |
         +----v----+ +---v---+       +----v----+ +---v---+
         | Stack 1 | |Stack 2|  ...  |Stack 34 | |Stack35|
         | chat-fr | |python |       |ml-train | |securi.|
         +---------+ +-------+       +---------+ +-------+
              |          |                 |          |
              +----------+--------+--------+----------+
                                  |
                         +--------v----------+
                         |    Negotiator     |
                         | CAMP + Catfish    |
                         +--------+----------+
                                  |
                         +--------v----------+
                         |    Anti-Bias      |
                         | KnowBias + RBD   |
                         +--------+----------+
                                  |
                         +--------v----------+
                         |   Aeon Memory     |
                         | Atlas + Trace     |
                         +-------------------+

Intended Use

• French/English conversational AI with domain expertise
• Code generation (Python, C/C++, Rust, TypeScript, embedded firmware)
• Electronics design (KiCad DSL, schematic review, component selection, SPICE)
• Manufacturing (process optimization, quality control)
• Multi-domain routing with cognitive arbitration

Limitations

• Not designed for medical, legal, or financial advice
• Optimized for technical domains; general knowledge may be weaker than base model
• Requires Q4_K_M or higher quantization; quality degrades below Q4
• Maximum 4 concurrent LoRA stacks; performance varies with stack combinations
• Memory (Aeon) requires external backends (Qdrant/Neo4j) for production use

Training Data — V3 (489K examples, 35 domains)

Sources

HuggingFace Datasets

35 Domains

Changes from V2: 3 new domains (components, llm-ops, ml-training). spice-sim merged into spice. stm32 is a sub-category of embedded.

New Domain: components

57K Q&A about electronic component specs, datasheets, sourcing, BOM, and cross-reference. Sources: Electronics StackExchange (filtered by component tags) + JITX open-components-database.

Training — V3

Evaluation

Hardware Requirements

Citation

@misc{micro-kiki-2026,
  title={micro-kiki: Multi-Domain Expert Model with Cognitive Layer},
  author={L'Electron Rare},
  year={2026},
  url={https://huggingface.co/electron-rare/micro-kiki}
}

Related Projects & Ecosystem

micro-kiki-v3 is one component of the FineFab platform built by L'Électron Rare — a local-first, multi-machine AI-native manufacturing and electronics platform.

See the full org at github.com/L-electron-Rare — 13 public repos covering platform, hardware, firmware, CAD, and ML.

Infrastructure: the 50K+ Claude CLI examples in the training dataset were captured on our 5-node P2P mesh — GrosMac (Apple M5), Tower (28 threads), CILS (i7), KXKM-AI (RTX 4090), VM bootstrap. Ed25519 auth, DHT discovery.

🇪🇺 EU AI Act transparency

This adapter is provided as a fine-tuned LoRA under the AI Act framework (Regulation EU 2024/1689). Compliance metadata:

⚠️ You are using an AI model. Outputs may be inaccurate, biased or fabricated. Do not act on them without independent verification, especially in regulated domains.