micro-kiki stack-spice — SPICE-simulation LoRA for Qwen3.5-35B-A3B
One standard LoRA adapter (adapters.safetensors, 2.05 GB) trained with mlx_lm on
Qwen/Qwen3.5-35B-A3B for SPICE / ngspice / LTspice netlist and simulation assistance.
(The previous card called it a "merged adapter"; the shipped adapter_config.json shows
a plain LoRA run.)
Recipe (from adapter_config.json)
| Parameter | Value |
|---|---|
| Base model | Qwen/Qwen3.5-35B-A3B (local models/qwen3.5-35b-a3b, BF16) |
| Framework | mlx_lm.lora, fine_tune_type: lora |
| LoRA rank / alpha / scale | 16 / 32 / 2.0 (= alpha/rank; unlike V4 which used scale 20) |
| Dropout | 0.0 |
| Target layers | 16 |
| Iterations | 11 542 |
| Batch size | 1 (grad_accumulation_steps 1) |
| Learning rate | 5e-5, no schedule, optimizer adam |
| Max sequence length | 2048 |
| Gradient checkpointing | true |
| Seed | 0 |
| Data | outputs/stacks/stack-spice (micro-kiki spice domain; example counts not recorded) |
| Adapter size | 2 051 644 214 bytes (16 layers × ~128 MB) |
Training host & cost
Mac Studio M3 Ultra 512 GB, MLX BF16. Peak memory, wall-clock, energy: not recorded (no log shipped). Host powered off since 2026-10.
Evaluation
Not recorded for this adapter. Related evidence: the project README reports that the base
model is already strong on SPICE/electronics and that only 3 of 10 probed domains improved
over base; in the June 2026 iact-bench run on the sibling Qwen3.6 ailiance adapters, no
adapter beat the base except html-css, and a SPICE adapter was removed from production
routing for degrading numeric (RC) calculations. Validate on your own netlists before use.
Limitations
Research artefact; may produce syntactically plausible but electrically wrong netlists; not for safety-critical design sign-off.
Citation
micro-kiki concept DOI 10.5281/zenodo.19666759.
Provenance
https://github.com/hypneum-lab/micro-kiki · https://github.com/ailiance/ailiance-mac-tuner · base https://huggingface.co/Qwen/Qwen3.5-35B-A3B
EU AI Act transparency
LoRA adapter weights only, base unchanged; provider Clément Saillant / L'Electron Rare for Hypneum Lab; limited risk, Article 50; contact clement@saillant.cc. Outputs may be inaccurate, biased or fabricated.
Quantized
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("hypneum-lab/micro-kiki-stack-spice") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True)