Instructions to use divinetribe/Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use divinetribe/Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx 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/Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx")
config = load_config("divinetribe/Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx")

# 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/Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx 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/Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx"

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": "divinetribe/Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use divinetribe/Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx 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/Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx"

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/Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx

Run Hermes

hermes

OpenClaw new

How to use divinetribe/Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx 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/Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx"

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/Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx" \
  --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"

Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx

A 4-bit MLX quantization of huihui-ai/Huihui-Qwen3-VL-32B-Instruct-abliterated, tuned for fast on-device vision-language inference on Apple Silicon.

Base model: huihui-ai/Huihui-Qwen3-VL-32B-Instruct-abliterated (BF16 abliterated)
Architecture: Qwen3-VL (dense 32B, multimodal image-text-to-text)
Quantization: 4-bit affine, group size 64
Format: MLX safetensors
Footprint: ~17 GB on disk, comfortable on a 32 GB Mac and snappy on 64 GB+
Context: 262 K tokens (256 K native, per upstream Qwen3-VL)
Modalities: text + image input → text output

Why this exists

Qwen3-VL is currently the strongest open-weight vision-language model family in its size class. The 32B Instruct variant handles single-image, multi-image, and video inputs with the same 256 K-token context window the text-only Qwen3 series uses. huihui-ai's abliteration applies refusal-direction projection (Arditi et al., 2024) so the model doesn't refuse benign-but-edgy multimodal queries — important for security-research, content-moderation testing, OCR of unconventional documents, and any workflow where the upstream's safety scaffolding produces noise rather than safety.

As of publication, this is the first MLX-format quantization of any abliterated Qwen3-VL model. The non-abliterated MLX builds (lmstudio-community, mlx-community) cover 3-bit through 8-bit but none have abliteration. huihui's BF16 abliterated source pulls ~224,000 downloads / 30 days — there's a large audience that wants this on Apple Silicon.

What you can do with it

Single-image visual Q&A on a 32 GB+ Mac, no cloud round-trip
Multi-image reasoning (compare two diagrams, summarize a multi-page screenshot dump)
Video understanding via per-frame ingestion (Qwen3-VL replaced T-RoPE with explicit textual timestamps)
OCR-heavy tasks where cloud vision APIs would log the document content
Document review for compliance-sensitive workflows (NDAs, medical records, sealed depositions) — see AirGap AI

Usage

Python (mlx-vlm)

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

model, processor = load("divinetribe/Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx")

messages = [
    {"role": "user", "content": [
        {"type": "image", "image": "https://example.com/your-image.jpg"},
        {"type": "text", "text": "What's in this image? Be specific."},
    ]},
]
prompt = apply_chat_template(processor, model.config, messages)
response = generate(model, processor, prompt=prompt, image=["https://example.com/your-image.jpg"], max_tokens=512)
print(response)

CLI

pip install mlx-vlm
python -m mlx_vlm.generate \
  --model divinetribe/Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx \
  --image /path/to/image.jpg \
  --prompt "Describe what's in this image."

Local OpenAI-compatible server

python -m mlx_vlm.server \
  --model divinetribe/Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx \
  --port 8080

That gives you a local http://localhost:8080/v1/chat/completions endpoint that accepts the standard OpenAI vision-message format. No API keys, no telemetry.

Where it fits in the lineup

Model (all on `divinetribe`)	Modality	Disk	Best for
`Llama-3.3-70B-Instruct-abliterated-8bit-mlx`	text	~75 GB	Hardest text reasoning, 96 GB+ Macs
`gemma-4-31b-it-abliterated-4bit-mlx`	text	~17 GB	Daily coding, 32 GB+ Macs
`Hermes-4-14B-abliterated-4bit-mlx`	text	~8 GB	16 GB Macs, instruction-following, tool use
`Huihui-Qwen3-VL-32B-Instruct-abliterated-4bit-mlx` (this)	text + image	~17 GB	Vision-language, 32 GB+ Macs

Abliteration

"Abliteration" suppresses the model's built-in refusal direction so it doesn't refuse benign-but-edgy queries. It is not a general capability upgrade — use responsibly, and you remain bound by the upstream Qwen3-VL Apache-2.0 license.

Credits

Qwen3-VL base by Alibaba Qwen team
Abliteration by huihui-ai — the dominant abliteration shop on Hugging Face
MLX 4-bit conversion by divinetribe using mlx-vlm 0.4.4

License

Apache 2.0, inherited from the upstream Qwen3-VL-32B-Instruct.

About the author

This model was built by Matt Macosko (@nicedreamzapp) for the claude-code-local stack — run Claude Code 100% on-device with local AI on Apple Silicon (⭐ 2,600+ on GitHub).