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

	---
	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](https://huggingface.co/mlx-community/Qwopus3.6-27B-Coder-4bit) was converted
	to MLX format from [`Jackrong/Qwopus3.6-27B-Coder`](https://huggingface.co/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](https://huggingface.co/Jackrong/Qwopus3.6-27B-Coder) for
	model details, license, and intended use.

	## Use with mlx

	```bash
	pip install -U mlx-vlm
	```

	### Image input

	```bash
	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

	```bash
	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

	```bash
	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
	```