Instructions to use LethalDonkey/Qwen3-VL-8B-Instruct-abliterated-v2-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LethalDonkey/Qwen3-VL-8B-Instruct-abliterated-v2-MLX-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("LethalDonkey/Qwen3-VL-8B-Instruct-abliterated-v2-MLX-4bit")
config = load_config("LethalDonkey/Qwen3-VL-8B-Instruct-abliterated-v2-MLX-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 LethalDonkey/Qwen3-VL-8B-Instruct-abliterated-v2-MLX-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 "LethalDonkey/Qwen3-VL-8B-Instruct-abliterated-v2-MLX-4bit"

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": "LethalDonkey/Qwen3-VL-8B-Instruct-abliterated-v2-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use LethalDonkey/Qwen3-VL-8B-Instruct-abliterated-v2-MLX-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 "LethalDonkey/Qwen3-VL-8B-Instruct-abliterated-v2-MLX-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 LethalDonkey/Qwen3-VL-8B-Instruct-abliterated-v2-MLX-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use LethalDonkey/Qwen3-VL-8B-Instruct-abliterated-v2-MLX-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 "LethalDonkey/Qwen3-VL-8B-Instruct-abliterated-v2-MLX-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 "LethalDonkey/Qwen3-VL-8B-Instruct-abliterated-v2-MLX-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"

Qwen3-VL-8B-Instruct-abliterated-v2-MLX-4bit / README.md

LethalDonkey

MLX 4-bit conversion of prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2

2edf725 verified 2 months ago

2.83 kB

license: apache-2.0
base_model: prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2
base_model_relation: quantized
library_name: mlx
pipeline_tag: image-text-to-text
language:
  - en
tags:
  - mlx
  - apple-silicon
  - qwen3_vl
  - vision
  - vision-language
  - image-text-to-text
  - abliterated
  - uncensored
  - 4-bit

Qwen3-VL-8B-Instruct-abliterated-v2 — MLX 4-bit

This is an MLX (4-bit) conversion of prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2, an abliterated (refusal-removed) build of Qwen3-VL-8B-Instruct.

It runs natively on Apple Silicon (M-series) via Apple's MLX framework and mlx-vlm — typically faster than llama.cpp/Metal for image encoding, with no separate vision-encoder (mmproj) file needed.

As of conversion, no MLX build of this model existed — this is a community conversion to bring it to Apple Silicon users.

Use it in an app

This model is wired into the Qwen3-VL Captioner desktop app — pick it from the MLX section of the model dropdown on a Mac and it downloads + loads automatically.

Use it directly (mlx-vlm)

pip install mlx-vlm

from mlx_vlm import load, stream_generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

model, processor = load("LethalDonkey/Qwen3-VL-8B-Instruct-abliterated-v2-MLX-4bit")
config = load_config("LethalDonkey/Qwen3-VL-8B-Instruct-abliterated-v2-MLX-4bit")

messages = [
    {"role": "system", "content": "You are a helpful assistant that describes images accurately and in detail."},
    {"role": "user", "content": "Describe this image in detail."},
]
prompt = apply_chat_template(processor, config, messages, num_images=1)

for chunk in stream_generate(model, processor, prompt, image=["your_image.jpg"], max_tokens=512):
    print(chunk.text, end="", flush=True)

Quantization

Bits: 4
Format: MLX (safetensors), converted with mlx_vlm.convert
Choose 4-bit for the smallest size / lowest memory, 8-bit for the best quality, 6-bit for a balance.

Credits

Base model: prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2 by prithivMLmods
Original architecture: Qwen3-VL by Qwen / Alibaba
MLX framework: Apple (ml-explore/mlx)
VLM tooling: mlx-vlm (Blaizzy/mlx-vlm)
Converted for the Qwen3-VL Captioner project

License

Apache-2.0, inherited from the base model.