---
license: apache-2.0
language:
  - fr
  - en
tags:
  - moe
  - lora
  - multi-domain
  - embedded-systems
  - cognitive
base_model: Qwen/Qwen3.5-35B-A3B
pipeline_tag: text-generation
---

# 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 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

| 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

```bibtex
@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](https://github.com/L-electron-Rare)** — a local-first, multi-machine AI-native manufacturing and electronics platform.

| Role | Project | Description |
|---|---|---|
| Training toolkit | [L-electron-Rare/KIKI-Mac_tunner](https://github.com/L-electron-Rare/KIKI-Mac_tunner) | MLX fine-tuning toolkit (Mac Studio) — Opus reasoning distilled into Mistral Large 123B |
| Fine-tuning pipeline | [L-electron-Rare/KIKI-models-tuning](https://github.com/L-electron-Rare/KIKI-models-tuning) | FineFab fine-tuning pipeline — training, evaluation, registry (Unsloth, LoRA) |
| Methodology | [electron-rare/Kill_LIFE](https://github.com/electron-rare/Kill_LIFE) | Spec-first agentic methodology for embedded systems — BMAD agents, gates, evidence packs |
| Orchestration | [electron-rare/mascarade](https://github.com/electron-rare/mascarade) | Multi-machine agentic LLM orchestration — P2P mesh, 8 providers, RAG pipeline |
| AI backend | [L-electron-Rare/life-core](https://github.com/L-electron-Rare/life-core) | FineFab AI backend — LLM router, RAG, caching, orchestration |
| CAD assistant | [electron-rare/KiC-AI](https://github.com/electron-rare/KiC-AI) | AI-powered PCB design assistant for KiCad |

See the full org at **[github.com/L-electron-Rare](https://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:

| 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 provenance |
| Adapter 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.