Instructions to use mlx-community/Qwopus3.6-27B-Coder-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Qwopus3.6-27B-Coder-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("mlx-community/Qwopus3.6-27B-Coder-4bit")
config = load_config("mlx-community/Qwopus3.6-27B-Coder-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 mlx-community/Qwopus3.6-27B-Coder-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 "mlx-community/Qwopus3.6-27B-Coder-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": "mlx-community/Qwopus3.6-27B-Coder-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/Qwopus3.6-27B-Coder-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 "mlx-community/Qwopus3.6-27B-Coder-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 mlx-community/Qwopus3.6-27B-Coder-4bit

Run Hermes

hermes

OpenClaw new

How to use mlx-community/Qwopus3.6-27B-Coder-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 "mlx-community/Qwopus3.6-27B-Coder-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 "mlx-community/Qwopus3.6-27B-Coder-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"

Qwopus3.6-27B-Coder-4bit / README.md

Jackrong

Add files using upload-large-folder tool

9378afe verified 9 days ago

preview code

Raw

History Blame Contribute Delete

2.08 kB

metadata

library_name: mlx
license: apache-2.0
pipeline_tag: image-text-to-text
tags:
  - mlx
  - qwen3_6
  - qwen3_5_moe
  - moe
  - coder
  - agent
  - tool-use
  - function-calling
  - image-text-to-text
  - long-context
  - vision
  - video
  - multimodal
  - 4bit
language:
  - en
  - zh
  - es
  - ru
  - ja
base_model: Jackrong/Qwopus3.6-27B-Coder

mlx-community/Qwopus3.6-27B-Coder-4bit

This model mlx-community/Qwopus3.6-27B-Coder-4bit was converted to MLX format from Jackrong/Qwopus3.6-27B-Coder using mlx-vlm version 0.4.4.

This is a 4bit MLX quantized conversion. It keeps the source model's chat template and multimodal processor configuration for text/coding, image, and video-style inputs. The language model weights were quantized with MLX 4-bit affine quantization; the multimodal vision components are preserved for image/video inputs.

Refer to the original model card for model details, license, and intended use.

Use with mlx

pip install -U mlx-vlm

Image input

python -m mlx_vlm.generate \
  --model mlx-community/Qwopus3.6-27B-Coder-4bit \
  --max-tokens 512 \
  --temperature 0.0 \
  --prompt "Describe this image." \
  --image <path_to_image>

Text / coding input

python -m mlx_vlm.generate \
  --model mlx-community/Qwopus3.6-27B-Coder-4bit \
  --max-tokens 512 \
  --temperature 0.2 \
  --prompt "Write a Python function that parses a JSONL file and counts records by label."

Notes

This is a 4bit MLX quantized version of Jackrong/Qwopus3.6-27B-Coder.
The model is intended for Apple Silicon inference with MLX.
For multimodal usage, prefer mlx-vlm rather than plain mlx-lm.
License: Apache 2.0, inherited from the source model metadata.

Conversion

mlx_vlm.convert \
  --hf-path Jackrong/Qwopus3.6-27B-Coder \
  --mlx-path Qwopus3.6-27B-Coder-4bit \
  --quantize \
  --q-bits 4 \
  --q-group-size 64 \
  --q-mode affine