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.
Property Value Base model Qwen3.5-35B-A3B Architecture MoE (256 experts, 3B active/token) Adapter LoRA rank 16 (q/k/v/o projections) Domains 35 Max active stacks 4 Context length 262,144 tokens Quantization Q4_K_M (inference), BF16 (training) License Apache 2.0
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 expertiseCode 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
Source Examples Description Claude CLI sessions 50,116 Real user-tool interactions extracted from 5 machines (GrosMac, kxkm-ai, Studio, Tower, CILS) Codex/Copilot sessions 2,529 OpenAI Codex + GitHub Copilot sessions extracted from 4 machines HuggingFace datasets 364,045 19 open datasets (see below) Opus teacher distillation — chat-fr, reasoning domains Original curated — 32 domain seed datasets
HuggingFace Datasets
Dataset Examples License CodeFeedback-Filtered-Instruction 157,000 Apache 2.0 French-Alpaca-Instruct-110K 110,000 Apache 2.0 Electronics StackExchange 95,000 CC-BY-SA-3.0 CJJones/LLM_EE_Educational_Synthetic_Dialog 50,000 CC-BY-NC-SA-4.0 MuratKomurcu/stm32-hal-dataset 29,700 MIT redcathode/thingiverse-openscad 7,400 — ThomasTheMaker/OpenSCAD 4,900 — STEM-AI-mtl/Electrical-engineering 1,100 — JITX open-components-database 151 — Vrindarani/netlistgen 106 —
35 Domains
Group Domains Conversation chat-fr, reasoning Code python, typescript, cpp, rust, html-css, shell, sql, yaml-json, lua-upy Infrastructure docker, devops, llm-orch, llm-ops (NEW), ml-training (NEW) Electronics kicad-dsl, kicad-pcb, spice, electronics, components (NEW), power, emc, dsp Hardware embedded, stm32, iot, platformio CAD freecad Web web-frontend, web-backend Other music-audio, math, security
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
Property Value Base model Qwen3.5-4B Adapter MoE-LoRA: 4 experts/projection, rank 16, top-2 routing Null-space projection ENABLED (prevents catastrophic forgetting between stacks) Curriculum Sequential, 35 stacks trained in order Platform (MLX) Mac Studio M3 Ultra 512 GB Platform (CUDA) kxkm-ai RTX 4090 24 GB
Evaluation
Metric Value Router accuracy (35-class) [PENDING] Forgetting check (angle) [PENDING] Perplexity (base) [PENDING] Perplexity (debiased) [PENDING] Aeon recall@1 [PENDING] Aeon recall@5 [PENDING] Aeon recall@10 [PENDING] Anti-bias flag rate [PENDING] Average inference latency [PENDING]
Hardware Requirements
Setup RAM/VRAM Use Mac Studio M3 Ultra 512 GB unified Training (BF16 LoRA) + serving (MLX) RTX 4090 24 GB VRAM Q4 inference (vLLM) Apple Silicon 32 GB+ 32 GB unified Q4_K_M inference (MLX/llama.cpp)
Citation
1 @misc{micro-kiki-2026,
2 title={micro-kiki: Multi-Domain Expert Model with Cognitive Layer},
3 author={L'Electron Rare},
4 year={2026},
5 url={https://huggingface.co/electron-rare/micro-kiki}
6 }
🇪🇺 EU AI Act transparency
This adapter is provided as a fine-tuned LoRA under the AI Act framework
(Regulation EU 2024/1689). Compliance metadata:
Field Value Provider L'Électron Rare (clemsail / electron-rare) Role under AI Act GPAI provider for this adapter Base model Qwen/Qwen3.5-35B-A3B — see upstream provenanceAdapter type LoRA / PEFT — adapter weights only; base unchanged Training data origin L'Électron Rare proprietary technical corpus + curated public docs License Apache-2.0 (adapter). Upstream base licence applies separately. Intended use Multi-domain technical assistance — engineering, KiCad, embedded, code, FR/EN chat Out of scope Healthcare diagnosis, legal advice, autonomous safety-critical decisions, generation of malicious code Risk classification Limited risk — Article 50 transparency obligations apply Copyright respect Training data does not include scraped copyrighted material. Opt-out signals (robots.txt, ai.txt) are honoured for web-sourced data. Full provenance https://github.com/L-electron-Rare/eu-kiki/tree/main/docs/provenance Contact postmaster@saillant.cc — biased output reports, copyright concerns, etc.
⚠️ 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.
