Instructions to use cuttie1979/AesCode-32B-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cuttie1979/AesCode-32B-4bit 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("cuttie1979/AesCode-32B-4bit")
config = load_config("cuttie1979/AesCode-32B-4bit")

# 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

How to use cuttie1979/AesCode-32B-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "cuttie1979/AesCode-32B-4bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "cuttie1979/AesCode-32B-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use cuttie1979/AesCode-32B-4bit 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 "cuttie1979/AesCode-32B-4bit"

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 cuttie1979/AesCode-32B-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use cuttie1979/AesCode-32B-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "cuttie1979/AesCode-32B-4bit"

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 "cuttie1979/AesCode-32B-4bit" \
  --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"

AesCode-32B-4bit / README.md

cuttie1979

Add MLX 4-bit conversion of microsoft/AesCode-32B (affine, group 64, text quantized, vision bf16)

ae146f7 verified about 1 month ago

preview code

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---
license: apache-2.0
base_model: microsoft/AesCode-32B
tags:
- mlx
- mlx-vlm
- qwen3_vl
- 4-bit
- image-text-to-text
- code-generation
- html
- design
library_name: mlx-vlm
pipeline_tag: image-text-to-text
language:
- en
---
# AesCode-32B-4bit (MLX)

MLX 4-bit quantization of [microsoft/AesCode-32B](https://huggingface.co/microsoft/AesCode-32B) for Apple Silicon.

AesCode generates information-rich visual artifacts (slides, posters, dashboards) as structured, editable HTML/CSS, using an image generated from the same prompt as an aesthetic reference. It starts from Qwen3-VL-32B-Instruct and is trained with cold-start SFT + GDPO across seven reward channels.

## Conversion

- Tool: mlx-vlm (convert), affine quantization
- Text model: uniform **4-bit, group size 64** (~4.7 GB/s avg bits 4.705 incl. bf16 vision tower)
- Vision tower: **unquantized (bf16)**
- Context: 262,144 tokens
- Size: ~19.7 GB (4 shards)
- Note: the original 0.7.1-era conversion omitted the `quantization` dict in config.json; it was reconstructed and verified. Recommended loaders: **mlx-vlm >= 0.7.6** or **oMLX >= 0.7.0**.

## Usage

```bash
pip install mlx-vlm
python -m mlx_vlm generate --model cuttie1979/AesCode-32B-4bit \
  --prompt "Describe this layout" --image reference.png --max-tokens 4096
```

Serving with [oMLX](https://github.com/jundot/omlx): place the checkpoint in your model directory and restart, then call the OpenAI-compatible `/v1/chat/completions`.

## Reference images

AesCode was trained with an optional reference image for layout/style guidance. Quality is highest with a reference, but the model degrades gracefully without one (−1.0 Visual point measured on AesCode-8B vs −19.6 for the backbone).

## License

Apache 2.0, following [microsoft/AesCode-32B](https://huggingface.co/microsoft/AesCode-32B). See the upstream model card for the training details and citation (AesCode: Aesthetic Code Generation with Decoupled Cross-Modal Rewards, under review 2027).

	---
	license: apache-2.0
	base_model: microsoft/AesCode-32B
	tags:
	- mlx
	- mlx-vlm
	- qwen3_vl
	- 4-bit
	- image-text-to-text
	- code-generation
	- html
	- design
	library_name: mlx-vlm
	pipeline_tag: image-text-to-text
	language:
	- en
	---

	# AesCode-32B-4bit (MLX)

	MLX 4-bit quantization of [microsoft/AesCode-32B](https://huggingface.co/microsoft/AesCode-32B) for Apple Silicon.

	AesCode generates information-rich visual artifacts (slides, posters, dashboards) as structured, editable HTML/CSS, using an image generated from the same prompt as an aesthetic reference. It starts from Qwen3-VL-32B-Instruct and is trained with cold-start SFT + GDPO across seven reward channels.

	## Conversion

	- Tool: mlx-vlm (convert), affine quantization
	- Text model: uniform 4-bit, group size 64 (~4.7 GB/s avg bits 4.705 incl. bf16 vision tower)
	- Vision tower: unquantized (bf16)
	- Context: 262,144 tokens
	- Size: ~19.7 GB (4 shards)
	- Note: the original 0.7.1-era conversion omitted the `quantization` dict in config.json; it was reconstructed and verified. Recommended loaders: mlx-vlm >= 0.7.6 or oMLX >= 0.7.0.

	## Usage

	```bash
	pip install mlx-vlm
	python -m mlx_vlm generate --model cuttie1979/AesCode-32B-4bit \
	--prompt "Describe this layout" --image reference.png --max-tokens 4096
	```

	Serving with [oMLX](https://github.com/jundot/omlx): place the checkpoint in your model directory and restart, then call the OpenAI-compatible `/v1/chat/completions`.

	## Reference images

	AesCode was trained with an optional reference image for layout/style guidance. Quality is highest with a reference, but the model degrades gracefully without one (−1.0 Visual point measured on AesCode-8B vs −19.6 for the backbone).

	## License

	Apache 2.0, following [microsoft/AesCode-32B](https://huggingface.co/microsoft/AesCode-32B). See the upstream model card for the training details and citation (AesCode: Aesthetic Code Generation with Decoupled Cross-Modal Rewards, under review 2027).