Parameter	Value
Framework	`mlx_lm` (Apple Silicon MLX)
Hardware	Mac Studio M3 Ultra, 512 GB unified memory
Fine-tune type	LoRA
Rank	16
Alpha	16
Dropout	0.0
Scale	20.0
Target layers	32
Iterations	200 per domain
Batch size	1
Learning rate	1e-5
Max seq length	1024
Grad checkpoint	true

Domains (35)

chat-fr, components, cpp, devops, docker, dsp, electronics, embedded, emc, freecad, html-css, iot, kicad-dsl, kicad-pcb, llm-ops, llm-orch, lua-upy, math, ml-training, music-audio, platformio, power, python, reasoning, rust, security, shell, spice, spice-sim, sql, stm32, typescript, web-backend, web-frontend, yaml-json.

Repository layout

<domain>/
  adapters.safetensors             # final LoRA weights
  0000200_adapters.safetensors     # checkpoint at iter 200
  adapter_config.json              # PEFT config
config-<domain>.yaml               # training config per domain
log-<domain>.txt                   # training log (loss curves, warnings)

Usage (MLX)

python

from mlx_lm import load, generate

model, tokenizer = load(
    "Qwen/Qwen3.6-35B-A3B",
    adapter_path="path/to/this-repo/math",
)
print(generate(model, tokenizer, prompt="Prove the Pythagorean theorem.", max_tokens=512))

Notes

Sequences longer than 1024 tokens were truncated during training.
llm-ops domain had a Metal backend crash right after save; weights are intact but the post-training eval did not complete.
Four domains (components, electronics, llm-ops, security) are ~2.4 GB instead of 3.8 GB — smaller datasets or shorter effective rank.

Citation

If you use these adapters, please cite:

@software{microkiki_v4sota_2026,
  author  = {Saillant, Clément},
  title   = {Micro-Kiki 35B A3B V4-SOTA LoRA Adapters},
  year    = {2026},
  month   = {4},
  url     = {https://huggingface.co/electron-rare/micro-kiki-35b-a3b-v4-sota-lora}
}

🇪🇺 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/Qwen3.6-35B-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.

clemsail/micro-kiki-v4-sota

Micro-Kiki 35B A3B V4-SOTA LoRA Adapters

Training setup