Text Generation
MLX
English
French
lora
mlx-lm
qwen3.5
Mixture of Experts
spice
ngspice
electronics
micro-kiki
hypneum-lab
Instructions to use hypneum-lab/micro-kiki-stack-spice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use hypneum-lab/micro-kiki-stack-spice with MLX:
# 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) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use hypneum-lab/micro-kiki-stack-spice with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "hypneum-lab/micro-kiki-stack-spice" --prompt "Once upon a time"
- Atomic Chat
File size: 2,588 Bytes
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license: apache-2.0
base_model: Qwen/Qwen3.5-35B-A3B
base_model_relation: adapter
library_name: mlx
pipeline_tag: text-generation
language:
- en
- fr
tags:
- lora
- mlx
- mlx-lm
- qwen3.5
- moe
- spice
- ngspice
- electronics
- micro-kiki
- hypneum-lab
---
# 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.
|