Instructions to use divinetribe/AesCode-32B-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use divinetribe/AesCode-32B-MLX-8bit 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("divinetribe/AesCode-32B-MLX-8bit")
config = load_config("divinetribe/AesCode-32B-MLX-8bit")

# 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 divinetribe/AesCode-32B-MLX-8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "divinetribe/AesCode-32B-MLX-8bit"

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": "divinetribe/AesCode-32B-MLX-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use divinetribe/AesCode-32B-MLX-8bit 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 "divinetribe/AesCode-32B-MLX-8bit"

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 divinetribe/AesCode-32B-MLX-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use divinetribe/AesCode-32B-MLX-8bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "divinetribe/AesCode-32B-MLX-8bit"

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 "divinetribe/AesCode-32B-MLX-8bit" \
  --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-MLX-8bit / README.md

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---
base_model: microsoft/AesCode-32B
base_model_relation: finetune
library_name: mlx
pipeline_tag: image-text-to-text
tags:
- mlx
- multimodal
- vision
- code-generation
- html
- apple-silicon
- 8-bit
language:
- en
license: apache-2.0
---
# AesCode-32B MLX 8bit

An 8-bit MLX quantization of
[microsoft/AesCode-32B](https://huggingface.co/microsoft/AesCode-32B) for Apple
Silicon, **with the vision tower kept in bf16 so it still sees**.

AesCode-32B is a `Qwen3VLForConditionalGeneration` finetune of
[Qwen/Qwen3-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct)
that takes a screenshot and writes the HTML and CSS to recreate it (slides,
posters, dashboards, product pages). Only the language tower is
quantized to 8-bit; the 351 `vision_tower.*` tensors stay in bfloat16, so the
image path keeps its quality.

**8-bit is about half the size for smaller machines; bf16 is full quality.**

## Sizes in this family

- [AesCode-32B-MLX-bf16 (full quality)](https://huggingface.co/divinetribe/AesCode-32B-MLX-bf16)
- [AesCode-32B-MLX-8bit (half size)](https://huggingface.co/divinetribe/AesCode-32B-MLX-8bit)
- [AesCode-32B-MLX-4bit (quarter size)](https://huggingface.co/divinetribe/AesCode-32B-MLX-4bit)

## What is in this build

| | |
|---|---|
| precision | 8-bit language tower, bf16 vision tower |
| vision | all 351 `vision_tower.*` tensors kept, `vision_config` and processor configs intact |
| size | about 34 GB on disk |
| framework | runs with `mlx-vlm` on Apple Silicon |

## Vision was tested before upload

Every size of this model is put through the same gate before it ships: a real
screenshot of a live ineedhemp.com product page is fed in and the model is asked
for an SEO, marketing and accessibility audit plus image alt-text. A smaller build
is only published if it still reads the page about as well as the full-precision
one. This 8-bit build passed. From its audit of the real page:

> SEO: The main H1 is strong and includes the product name "Divine Crossing v5 Rebuildable Concentrate Heater". Marketing: the "Version 5 Ceramic Heater" callout, the star rating, the trust badges (shipping, warranty) and the "Add to Cart" button all read clearly. Alt-text: Main product image: "Divine Crossing v5 rebuildable concentrate heater in metallic finish with textured grip and glass chamber."

It named the real on-page elements and wrote usable alt-text, so the quantization
did not break its vision.

## Use

```bash
pip install mlx-vlm
python -m mlx_vlm.generate --model divinetribe/AesCode-32B-MLX-8bit \
  --image screenshot.png \
  --prompt "Recreate this screenshot as a single HTML file with inline CSS." \
  --max-tokens 1200
```

Runs on an M-series Mac. No cloud inference, no API keys, no telemetry.

## Benchmarks

The most authoritative evaluation that actually covers this model is the one in
AesCode's own paper, **"AesCode: Aesthetic Code Generation with Decoupled
Cross-Modal Rewards"** ([arXiv:2610.18860](https://arxiv.org/abs/2610.18860)).
There is no independent public leaderboard that lists AesCode specifically, so
these numbers come from the authors' benchmark of 300 infographic-style samples,
scored for rule-following correctness and visual quality. Scores quoted from the
[model card](https://huggingface.co/microsoft/AesCode-32B):

| Model (with reference image) | Overall | Visual |
|---|---|---|
| **AesCode-32B** | **85.89** | 77.44 |
| Claude Opus 4.8 (no reference) | 76.78 | |
| GPT-5.5 (no reference) | 76.31 | |
| Qwen3-VL-32B base (with reference) | 61.10 | |

These are the original model's numbers. For general-vision numbers on the backbone
itself, see the official
[Qwen3-VL](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct) evaluations (that is
the base model's benchmark, not AesCode's).

## Credits

This build stands entirely on other people's work. Credit where it is due:

- **The AesCode team at Microsoft**, who created the model and the benchmark:
  Xin Gao, Yifan Yang, Xin Li, Pan Wang, Yan Li, Muzhao Tian, Mingxi Cheng, Bei
  Liu, Kai Qiu, Qi Dai, Dong Chen, Yue Dong, Leandros Tassiulas, Rex Ying, Ji Li
  and Chong Luo. Paper: [arXiv:2610.18860](https://arxiv.org/abs/2610.18860),
  model: [microsoft/AesCode-32B](https://huggingface.co/microsoft/AesCode-32B).
- **The Qwen team at Alibaba**, for the
  [Qwen3-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct) base.
- **The MLX team at Apple** and **Prince Canuma** and the
  [mlx-vlm](https://github.com/Blaizzy/mlx-vlm) contributors, for the open-source
  tools that let a model like this run natively and privately on a Mac.

Thank you to all of them.

## Note

Quantization trades a little precision for size. It does not add knowledge and it
does not make output correct. You are responsible for what you build with it.

Built and published by [divinetribe](https://huggingface.co/divinetribe) ·
[more models](https://nicedreamzwholesale.com/software/huggingface/)

	---
	base_model: microsoft/AesCode-32B
	base_model_relation: finetune
	library_name: mlx
	pipeline_tag: image-text-to-text
	tags:
	- mlx
	- multimodal
	- vision
	- code-generation
	- html
	- apple-silicon
	- 8-bit
	language:
	- en
	license: apache-2.0
	---

	# AesCode-32B MLX 8bit

	An 8-bit MLX quantization of
	[microsoft/AesCode-32B](https://huggingface.co/microsoft/AesCode-32B) for Apple
	Silicon, with the vision tower kept in bf16 so it still sees.

	AesCode-32B is a `Qwen3VLForConditionalGeneration` finetune of
	[Qwen/Qwen3-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct)
	that takes a screenshot and writes the HTML and CSS to recreate it (slides,
	posters, dashboards, product pages). Only the language tower is
	quantized to 8-bit; the 351 `vision_tower.*` tensors stay in bfloat16, so the
	image path keeps its quality.

	8-bit is about half the size for smaller machines; bf16 is full quality.

	## Sizes in this family

	- [AesCode-32B-MLX-bf16 (full quality)](https://huggingface.co/divinetribe/AesCode-32B-MLX-bf16)
	- [AesCode-32B-MLX-8bit (half size)](https://huggingface.co/divinetribe/AesCode-32B-MLX-8bit)
	- [AesCode-32B-MLX-4bit (quarter size)](https://huggingface.co/divinetribe/AesCode-32B-MLX-4bit)

	## What is in this build

	\| \| \|
	\|---\|---\|
	\| precision \| 8-bit language tower, bf16 vision tower \|
	\| vision \| all 351 `vision_tower.*` tensors kept, `vision_config` and processor configs intact \|
	\| size \| about 34 GB on disk \|
	\| framework \| runs with `mlx-vlm` on Apple Silicon \|

	## Vision was tested before upload

	Every size of this model is put through the same gate before it ships: a real
	screenshot of a live ineedhemp.com product page is fed in and the model is asked
	for an SEO, marketing and accessibility audit plus image alt-text. A smaller build
	is only published if it still reads the page about as well as the full-precision
	one. This 8-bit build passed. From its audit of the real page:

	> SEO: The main H1 is strong and includes the product name "Divine Crossing v5 Rebuildable Concentrate Heater". Marketing: the "Version 5 Ceramic Heater" callout, the star rating, the trust badges (shipping, warranty) and the "Add to Cart" button all read clearly. Alt-text: Main product image: "Divine Crossing v5 rebuildable concentrate heater in metallic finish with textured grip and glass chamber."

	It named the real on-page elements and wrote usable alt-text, so the quantization
	did not break its vision.

	## Use

	```bash
	pip install mlx-vlm
	python -m mlx_vlm.generate --model divinetribe/AesCode-32B-MLX-8bit \
	--image screenshot.png \
	--prompt "Recreate this screenshot as a single HTML file with inline CSS." \
	--max-tokens 1200
	```

	Runs on an M-series Mac. No cloud inference, no API keys, no telemetry.

	## Benchmarks

	The most authoritative evaluation that actually covers this model is the one in
	AesCode's own paper, **"AesCode: Aesthetic Code Generation with Decoupled
	Cross-Modal Rewards"** ([arXiv:2610.18860](https://arxiv.org/abs/2610.18860)).
	There is no independent public leaderboard that lists AesCode specifically, so
	these numbers come from the authors' benchmark of 300 infographic-style samples,
	scored for rule-following correctness and visual quality. Scores quoted from the
	[model card](https://huggingface.co/microsoft/AesCode-32B):

	\| Model (with reference image) \| Overall \| Visual \|
	\|---\|---\|---\|
	\| AesCode-32B \| 85.89 \| 77.44 \|
	\| Claude Opus 4.8 (no reference) \| 76.78 \| \|
	\| GPT-5.5 (no reference) \| 76.31 \| \|
	\| Qwen3-VL-32B base (with reference) \| 61.10 \| \|

	These are the original model's numbers. For general-vision numbers on the backbone
	itself, see the official
	[Qwen3-VL](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct) evaluations (that is
	the base model's benchmark, not AesCode's).

	## Credits

	This build stands entirely on other people's work. Credit where it is due:

	- The AesCode team at Microsoft, who created the model and the benchmark:
	Xin Gao, Yifan Yang, Xin Li, Pan Wang, Yan Li, Muzhao Tian, Mingxi Cheng, Bei
	Liu, Kai Qiu, Qi Dai, Dong Chen, Yue Dong, Leandros Tassiulas, Rex Ying, Ji Li
	and Chong Luo. Paper: [arXiv:2610.18860](https://arxiv.org/abs/2610.18860),
	model: [microsoft/AesCode-32B](https://huggingface.co/microsoft/AesCode-32B).
	- The Qwen team at Alibaba, for the
	[Qwen3-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct) base.
	- The MLX team at Apple and Prince Canuma and the
	[mlx-vlm](https://github.com/Blaizzy/mlx-vlm) contributors, for the open-source
	tools that let a model like this run natively and privately on a Mac.

	Thank you to all of them.

	## Note

	Quantization trades a little precision for size. It does not add knowledge and it
	does not make output correct. You are responsible for what you build with it.

	Built and published by [divinetribe](https://huggingface.co/divinetribe) ·
	[more models](https://nicedreamzwholesale.com/software/huggingface/)