Instructions to use matt-here/MLX-Qwopus3.5-27B-v3-vision-5bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use matt-here/MLX-Qwopus3.5-27B-v3-vision-5bit 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("matt-here/MLX-Qwopus3.5-27B-v3-vision-5bit")
config = load_config("matt-here/MLX-Qwopus3.5-27B-v3-vision-5bit")

# 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
Unsloth Desktop

How to use matt-here/MLX-Qwopus3.5-27B-v3-vision-5bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "matt-here/MLX-Qwopus3.5-27B-v3-vision-5bit"

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": "matt-here/MLX-Qwopus3.5-27B-v3-vision-5bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use matt-here/MLX-Qwopus3.5-27B-v3-vision-5bit 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 "matt-here/MLX-Qwopus3.5-27B-v3-vision-5bit"

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 matt-here/MLX-Qwopus3.5-27B-v3-vision-5bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use matt-here/MLX-Qwopus3.5-27B-v3-vision-5bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "matt-here/MLX-Qwopus3.5-27B-v3-vision-5bit"

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 "matt-here/MLX-Qwopus3.5-27B-v3-vision-5bit" \
  --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"

MLX-Qwopus3.5-27B-v3-vision-5bit

A 5-bit MLX quantization of Jackrong/Qwopus3.5-27B-v3 with a few tweaks to restore the multimodal capabilities.

Supports {%- set enable_thinking = false %} Jinja variable.

Update [2026-04-12]: Refined the chat template to further improve stability for long-running tasks and tool use; mitigated an issue where incorrect think tag formatting could leak from a distillation dataset.

Quantization Details

Property	Value
Method	5-bit (5.578 bits per weight)
Tool	`mlx-vlm 0.4.2` via `mlx-vlm.convert`
Size	~19.5GB

Other Available Quants

Model	Size	Quantization	Bits per weight	Multimodal
Jackrong/MLX-Qwopus3.5-27B-v3-4bit	15.15 GB	4-bit	4.501	✗
matt-here/MLX-Qwopus3.5-27B-v3-vision-4bit	16.08 GB	4-bit	4.695	✓ (Vision)
matt-here/MLX-Qwopus3.5-27B-v3-5bit	18.56 GB	5-bit	5.501	✗
(This model)	19.46 GB	5-bit	5.678	✓ (Vision)
Jackrong/MLX-Qwopus3.5-27B-v3-6bit	21.88 GB	6-bit	6.501	✗
matt-here/MLX-Qwopus3.5-27B-v3-vision-6bit	22.85 GB	6-bit	6.661	✓ (Vision)
Jackrong/MLX-Qwopus3.5-27B-v3-bf16	53.81 GB	bf16	16	✗

GGUF quants - Jackrong/Qwopus3.5-27B-v3-GGUF

Credits

Alibaba Qwen Team — Qwen 3.5 27B dense model
Jackrong - Claude 4.6 Opus v3 distillation work
Unsloth - Training framework
Apple MLX Team - High-speed local inference on Apple Silicon

Downloads last month: 7

Safetensors

Model size

27B params

Tensor type

U32

BF16

MLX

Hardware compatibility

5-bit

Model tree for matt-here/MLX-Qwopus3.5-27B-v3-vision-5bit

Base model

Qwen/Qwen3.5-27B

Finetuned

unsloth/Qwen3.5-27B

Adapter

Jackrong/Qwopus3.5-27B-v3

Adapter

(8)

this model