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

Libraries

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

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use divinetribe/AesCode-8B-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-8B-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-8B-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-8B-MLX-8bit / README.md

divinetribe

Add bf16 MLX conversion (vision kept)

f71d927 verified 2 days ago

preview code

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6.17 kB

---
license: apache-2.0
base_model: microsoft/AesCode-8B
tags:
  - mlx
  - mlx-vlm
  - vision
  - multimodal
  - image-text-to-text
  - code-generation
  - html
  - design
  - reinforcement-learning
  - 8-bit
pipeline_tag: image-text-to-text
library_name: mlx-vlm
---
# AesCode-8B — Vision + Code (8-bit, MLX)

8-bit MLX quantization of Microsoft's **AesCode-8B**, with the **vision tower kept intact** so
it reads a design or screenshot and writes the HTML/CSS back on Apple Silicon. **8.993 bits
per weight**, 9.2 GB, two shards, group size 64.

**8-bit is about half the size, for 16 GB machines; the
[bf16 version](https://huggingface.co/divinetribe/AesCode-8B-MLX-bf16) is the full-quality
one.** This 8-bit build was put through the same pose-reading vision check as the bf16 before
publishing and read the figure just as well, so the shrink held its sight.

AesCode generates information-rich visual artifacts (slides, posters, dashboards, reports)
as structured, editable, verifiable HTML/CSS. It starts from
[Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) and is trained with
cold-start SFT followed by GDPO across seven reward channels, separating semantic
requirements from visual cues through graph-structured supervision and decoupled cross-modal
rewards.

## What it is

- **Base:** [`Qwen/Qwen3-VL-8B-Instruct`](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) (Qwen team).
- **Source checkpoint:** [`microsoft/AesCode-8B`](https://huggingface.co/microsoft/AesCode-8B) (Apache-2.0).
- **Architecture:** `Qwen3VLForConditionalGeneration` — a `qwen3_vl` vision tower feeding the
  `qwen3_vl_text` decoder. 8.8B parameters.
- **This repo:** 8-bit MLX weights (group size 64), vision kept, for [`mlx-vlm`](https://github.com/Blaizzy/mlx-vlm).
- **Other sizes:** [bf16 (full quality)](https://huggingface.co/divinetribe/AesCode-8B-MLX-bf16) · [4-bit](https://huggingface.co/divinetribe/AesCode-8B-MLX-4bit).

## Running it (MLX)

```python
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
model, processor = load("divinetribe/AesCode-8B-MLX-8bit")
prompt = apply_chat_template(processor, model.config,
                             "Rebuild this screenshot as a single HTML file.", num_images=1)
print(generate(model, processor, prompt, image=["design.png"], max_tokens=1024))
```

Text-only works with `num_images=0` and no `image=` argument.

## Worked vision example — pose-match QC

A real frame from a motion game (a translucent target avatar drawn over a live camera
scene) was fed to this 8-bit model, asking whether the drawn figure is a correct human pose
and whether any limbs are extra or wrong. The model's unedited on-device answer:

> The translucent avatar shows a standing human pose. Arms are bent at the sides, hands near
> the waist, and legs are straight with knees slightly bent. The torso is upright, and the
> head is centered. No extra limbs are visible, and joints appear anatomically placed. The
> pose is balanced and stable, matching a correct human standing position.

This matches the bf16 build's read: it catches the overlay's translucency, the single
standing figure, the bent knees, and correctly answers the core QC question — no extra limbs
and joints anatomically placed. It placed the hands at the waist rather than up in a full
guard, same as bf16, so the quantization did not change its read. The same vision path that
reads this frame also reads a design or screenshot and writes **alt-text** and the
**HTML/CSS** back, which is what AesCode is built for.

## Benchmarks

The single most reputable benchmark that genuinely applies is **AesCode's own evaluation**
from the paper (*AesCode: Aesthetic Code Generation with Decoupled Cross-Modal Rewards*,
[arXiv:2610.18860](https://arxiv.org/abs/2610.18860)): 300 infographic prompts scored on a
programmatic `Rule` track (Text, Bound, Chart) and a `Visual` checklist (Content, Layout,
Style), with `Overall` the mean of the two, averaged over three generations with no
selection. AesCode-8B reaches **Overall 82.94** and **Rule 90.07**, improving Visual over its
reference-conditioned Qwen3-VL-8B backbone by **31.1 points**; the paper reports this 8B
result exceeding the reference-conditioned GPT-5.5 (81.28) and Claude Opus 4.8 (80.39) rows
in the same table. 8-bit trades a little accuracy for size, so expect the exact scores to
move slightly. All numbers are quoted from the AesCode paper and model card, not invented.

## Credits

Enormous thanks to the people whose work this conversion simply repackages:

- **The Microsoft AesCode authors** — 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 — who built and released AesCode-8B under Apache-2.0:
  [model](https://huggingface.co/microsoft/AesCode-8B) ·
  [paper](https://arxiv.org/abs/2610.18860) ·
  [project page](https://microsoft.github.io/AesCode/) ·
  [code](https://github.com/microsoft/AesCode).
- **The Qwen team** at Alibaba — for the
  [Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) base and the
  Qwen3-VL vision-language architecture.
- **Prince Canuma and the [mlx-vlm](https://github.com/Blaizzy/mlx-vlm) project**, and the
  **Apple MLX team**, for the tooling that makes vision-language models run natively on
  Apple Silicon.

The quantization to 8-bit MLX is the only work done here. All model quality is theirs.

---
<!-- ccl-frontdoor -->
## Part of Claude Code Local

This model is one of the fighters in **[Claude Code Local](https://github.com/nicedreamzapp/claude-code-local)** (3.2k★), which runs Claude Code 100% on-device on Apple Silicon through an MLX-native Anthropic-API server. Not sure which local model to run as an agent? Check the **[Agent-12 local agent leaderboard](https://nicedreamzapp.github.io/agent12/)**: real agent tasks, judged by the filesystem, same hardware for every row.

Built by [Matt Macosko](https://github.com/nicedreamzapp) in Humboldt, CA. Open to work on local-AI and Apple Silicon inference: matt@ineedhemp.com.

	---
	license: apache-2.0
	base_model: microsoft/AesCode-8B
	tags:
	- mlx
	- mlx-vlm
	- vision
	- multimodal
	- image-text-to-text
	- code-generation
	- html
	- design
	- reinforcement-learning
	- 8-bit
	pipeline_tag: image-text-to-text
	library_name: mlx-vlm
	---

	# AesCode-8B — Vision + Code (8-bit, MLX)

	8-bit MLX quantization of Microsoft's AesCode-8B, with the vision tower kept intact so
	it reads a design or screenshot and writes the HTML/CSS back on Apple Silicon. **8.993 bits
	per weight**, 9.2 GB, two shards, group size 64.

	**8-bit is about half the size, for 16 GB machines; the
	[bf16 version](https://huggingface.co/divinetribe/AesCode-8B-MLX-bf16) is the full-quality
	one.** This 8-bit build was put through the same pose-reading vision check as the bf16 before
	publishing and read the figure just as well, so the shrink held its sight.

	AesCode generates information-rich visual artifacts (slides, posters, dashboards, reports)
	as structured, editable, verifiable HTML/CSS. It starts from
	[Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) and is trained with
	cold-start SFT followed by GDPO across seven reward channels, separating semantic
	requirements from visual cues through graph-structured supervision and decoupled cross-modal
	rewards.

	## What it is

	- Base: [`Qwen/Qwen3-VL-8B-Instruct`](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) (Qwen team).
	- Source checkpoint: [`microsoft/AesCode-8B`](https://huggingface.co/microsoft/AesCode-8B) (Apache-2.0).
	- Architecture: `Qwen3VLForConditionalGeneration` — a `qwen3_vl` vision tower feeding the
	`qwen3_vl_text` decoder. 8.8B parameters.
	- This repo: 8-bit MLX weights (group size 64), vision kept, for [`mlx-vlm`](https://github.com/Blaizzy/mlx-vlm).
	- Other sizes: [bf16 (full quality)](https://huggingface.co/divinetribe/AesCode-8B-MLX-bf16) · [4-bit](https://huggingface.co/divinetribe/AesCode-8B-MLX-4bit).

	## Running it (MLX)

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

	model, processor = load("divinetribe/AesCode-8B-MLX-8bit")
	prompt = apply_chat_template(processor, model.config,
	"Rebuild this screenshot as a single HTML file.", num_images=1)
	print(generate(model, processor, prompt, image=["design.png"], max_tokens=1024))
	```

	Text-only works with `num_images=0` and no `image=` argument.

	## Worked vision example — pose-match QC

	A real frame from a motion game (a translucent target avatar drawn over a live camera
	scene) was fed to this 8-bit model, asking whether the drawn figure is a correct human pose
	and whether any limbs are extra or wrong. The model's unedited on-device answer:

	> The translucent avatar shows a standing human pose. Arms are bent at the sides, hands near
	> the waist, and legs are straight with knees slightly bent. The torso is upright, and the
	> head is centered. No extra limbs are visible, and joints appear anatomically placed. The
	> pose is balanced and stable, matching a correct human standing position.

	This matches the bf16 build's read: it catches the overlay's translucency, the single
	standing figure, the bent knees, and correctly answers the core QC question — no extra limbs
	and joints anatomically placed. It placed the hands at the waist rather than up in a full
	guard, same as bf16, so the quantization did not change its read. The same vision path that
	reads this frame also reads a design or screenshot and writes alt-text and the
	HTML/CSS back, which is what AesCode is built for.

	## Benchmarks

	The single most reputable benchmark that genuinely applies is AesCode's own evaluation
	from the paper (AesCode: Aesthetic Code Generation with Decoupled Cross-Modal Rewards,
	[arXiv:2610.18860](https://arxiv.org/abs/2610.18860)): 300 infographic prompts scored on a
	programmatic `Rule` track (Text, Bound, Chart) and a `Visual` checklist (Content, Layout,
	Style), with `Overall` the mean of the two, averaged over three generations with no
	selection. AesCode-8B reaches Overall 82.94 and Rule 90.07, improving Visual over its
	reference-conditioned Qwen3-VL-8B backbone by 31.1 points; the paper reports this 8B
	result exceeding the reference-conditioned GPT-5.5 (81.28) and Claude Opus 4.8 (80.39) rows
	in the same table. 8-bit trades a little accuracy for size, so expect the exact scores to
	move slightly. All numbers are quoted from the AesCode paper and model card, not invented.

	## Credits

	Enormous thanks to the people whose work this conversion simply repackages:

	- The Microsoft AesCode authors — 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 — who built and released AesCode-8B under Apache-2.0:
	[model](https://huggingface.co/microsoft/AesCode-8B) ·
	[paper](https://arxiv.org/abs/2610.18860) ·
	[project page](https://microsoft.github.io/AesCode/) ·
	[code](https://github.com/microsoft/AesCode).
	- The Qwen team at Alibaba — for the
	[Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) base and the
	Qwen3-VL vision-language architecture.
	- Prince Canuma and the [mlx-vlm](https://github.com/Blaizzy/mlx-vlm) project, and the
	Apple MLX team, for the tooling that makes vision-language models run natively on
	Apple Silicon.

	The quantization to 8-bit MLX is the only work done here. All model quality is theirs.

	---
	<!-- ccl-frontdoor -->
	## Part of Claude Code Local

	This model is one of the fighters in [Claude Code Local](https://github.com/nicedreamzapp/claude-code-local) (3.2k★), which runs Claude Code 100% on-device on Apple Silicon through an MLX-native Anthropic-API server. Not sure which local model to run as an agent? Check the [Agent-12 local agent leaderboard](https://nicedreamzapp.github.io/agent12/): real agent tasks, judged by the filesystem, same hardware for every row.

	Built by [Matt Macosko](https://github.com/nicedreamzapp) in Humboldt, CA. Open to work on local-AI and Apple Silicon inference: matt@ineedhemp.com.