Image-Text-to-Text
MLX
Safetensors
English
German
gemma4_unified
gemma
gemma4
fine-tuned
dora
lora
ailey
openminded
conversational
6-bit
Instructions to use OpenMinded-Labs/AileyCore-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OpenMinded-Labs/AileyCore-12B with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("OpenMinded-Labs/AileyCore-12B") config = load_config("OpenMinded-Labs/AileyCore-12B") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use OpenMinded-Labs/AileyCore-12B with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "OpenMinded-Labs/AileyCore-12B"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "OpenMinded-Labs/AileyCore-12B" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use OpenMinded-Labs/AileyCore-12B with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "OpenMinded-Labs/AileyCore-12B"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default OpenMinded-Labs/AileyCore-12B
Run Hermes
hermes
- OpenClaw new
How to use OpenMinded-Labs/AileyCore-12B with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "OpenMinded-Labs/AileyCore-12B"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "OpenMinded-Labs/AileyCore-12B" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
File size: 4,653 Bytes
522a4dc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | ---
license: apache-2.0
base_model: mlx-community/gemma-4-12B-it-6bit
library_name: mlx
pipeline_tag: image-text-to-text
language:
- en
- de
tags:
- mlx
- gemma
- gemma4
- fine-tuned
- dora
- lora
- ailey
- openminded
---
# AileyCore-12B
**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](https://github.com/ml-explore/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`](https://huggingface.co/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](https://ai.google.dev/gemma/apache_2)).
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)}
}
```
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