AileyCore-12B is a fine-tuned, adapter-merged derivative of Google Gemma 4 (12B, instruction-tuned), optimized to run locally on Apple Silicon via the MLX framework.

It powers A!ley, the on-device assistant persona created by OpenM!nded / Simon van de Loo.

Developed by: OpenM!nded (Simon van de Loo)
Model type: Multimodal (text + image + audio input, text output), decoder-only
Base model: mlx-community/gemma-4-12B-it-6bit (Google Gemma 4 12B-IT, 6-bit quantized)
License: Apache License 2.0
Languages: English, German
Quantization: 6-bit (q6), preserved through the merge

What it is

AileyCore-12B is Gemma 4 12B-IT with a lightweight identity + behavior fine-tune baked directly into the weights. The adaptation was performed with a mixed DoRA/LoRA scheme and then merged back into the base weights, so no separate adapter is required at inference time.

The identity ("A!ley", created by OpenM!nded / Simon van de Loo) is embedded in the weights and remains stable with or without a system prompt.

Intended use

Local, privacy-respecting assistant on Apple Silicon (M-series) Macs
Conversational reasoning, writing, and general assistance in EN/DE
Multimodal understanding (image / audio input) inherited from Gemma 4

Out of scope

Any use prohibited by applicable law
Safety-critical, medical, legal, or financial decision-making without human oversight
The model can produce inaccurate or biased output; verify important information

How to use (MLX)

Because this is a Gemma 4 unified (multimodal) checkpoint, load it with mlx_vlm:

python

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template

model, processor = load("CptShaggy/AileyCore-12B")

messages = [{"role": "user", "content": "Wer bist du?"}]
prompt = apply_chat_template(processor, model.config, messages)
print(generate(model, processor, prompt, max_tokens=256, verbose=True))

Note: plain mlx_lm cannot load the gemma4_unified architecture — use mlx_vlm.

Training details

Setting	Value
Method	Mixed DoRA (attention) + LoRA (MLP), merged into base
DoRA targets	`q_proj`, `v_proj`
LoRA targets	`gate_proj`, `up_proj`, `down_proj`
Rank / Alpha	8 / 16 (scale 2.0)
Sequence length	1024
Gradient accumulation	8
Learning rate	1e-4
Selected checkpoint	best (val_loss ≈ 1.24)
Hardware	Apple M4, 24 GB unified memory
Framework	MLX (`mlx_vlm` + `mlx_lm.tuner`)

The 6-bit quantization of the base model is preserved through the merge; the fused adapter weights are re-quantized to q6.

Limitations & biases

Inherited from Gemma 4 plus the fine-tune: the model may produce factually incorrect, outdated, or biased content, and reflects the characteristics of its training data. It is not a knowledge base. Always keep a human in the loop for consequential use.

License & attribution

This model is a Derivative Work of Google Gemma 4, which Google releases under the Apache License 2.0 (see the official Gemma 4 license). AileyCore-12B is therefore also distributed under Apache 2.0.

In accordance with Apache 2.0 §4:

The base Gemma 4 weights were modified via DoRA/LoRA adaptation and merged. Modified components are noted in AILEY_MERGE_INFO.json and this model card.
A copy of the Apache 2.0 license is included (LICENSE).
Attribution notices are provided in NOTICE.

Gemma is a trademark of Google LLC. This project is independent and not endorsed by or affiliated with Google. Use of the name "Gemma" here is solely to describe the origin of the base model.

Copyright 2026 OpenM!nded / Simon van de Loo
Portions © Google LLC (Gemma 4), Apache License 2.0

Licensed under the Apache License, Version 2.0.
You may obtain a copy of the License at
    http://www.apache.org/licenses/LICENSE-2.0

Citation

bibtex

@misc{aileycore12b_2026,
  title  = {AileyCore-12B: A Gemma 4 fine-tune for the A!ley assistant},
  author = {van de Loo, Simon and OpenM!nded},
  year   = {2026},
  note   = {Fine-tuned and merged from Google Gemma 4 12B-IT (Apache 2.0)}
}

OpenMinded-Labs/AileyCore-12B

AileyCore-12B