Instructions to use porschefreak/Qwable-5-27B-Coder-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use porschefreak/Qwable-5-27B-Coder-mlx-4Bit with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="porschefreak/Qwable-5-27B-Coder-mlx-4Bit")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("porschefreak/Qwable-5-27B-Coder-mlx-4Bit")
model = AutoModelForMultimodalLM.from_pretrained("porschefreak/Qwable-5-27B-Coder-mlx-4Bit", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use porschefreak/Qwable-5-27B-Coder-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("porschefreak/Qwable-5-27B-Coder-mlx-4Bit")
config = load_config("porschefreak/Qwable-5-27B-Coder-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

vLLM

How to use porschefreak/Qwable-5-27B-Coder-mlx-4Bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "porschefreak/Qwable-5-27B-Coder-mlx-4Bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "porschefreak/Qwable-5-27B-Coder-mlx-4Bit",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/porschefreak/Qwable-5-27B-Coder-mlx-4Bit

SGLang

How to use porschefreak/Qwable-5-27B-Coder-mlx-4Bit with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "porschefreak/Qwable-5-27B-Coder-mlx-4Bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "porschefreak/Qwable-5-27B-Coder-mlx-4Bit",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "porschefreak/Qwable-5-27B-Coder-mlx-4Bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "porschefreak/Qwable-5-27B-Coder-mlx-4Bit",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Unsloth Studio

How to use porschefreak/Qwable-5-27B-Coder-mlx-4Bit with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for porschefreak/Qwable-5-27B-Coder-mlx-4Bit to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for porschefreak/Qwable-5-27B-Coder-mlx-4Bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for porschefreak/Qwable-5-27B-Coder-mlx-4Bit to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="porschefreak/Qwable-5-27B-Coder-mlx-4Bit",
    max_seq_length=2048,
)

How to use porschefreak/Qwable-5-27B-Coder-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 "porschefreak/Qwable-5-27B-Coder-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": "porschefreak/Qwable-5-27B-Coder-mlx-4Bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use porschefreak/Qwable-5-27B-Coder-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 "porschefreak/Qwable-5-27B-Coder-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 porschefreak/Qwable-5-27B-Coder-mlx-4Bit

Run Hermes

hermes

OpenClaw new

How to use porschefreak/Qwable-5-27B-Coder-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 "porschefreak/Qwable-5-27B-Coder-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 "porschefreak/Qwable-5-27B-Coder-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"

Docker Model Runner
How to use porschefreak/Qwable-5-27B-Coder-mlx-4Bit with Docker Model Runner:
```
docker model run hf.co/porschefreak/Qwable-5-27B-Coder-mlx-4Bit
```

Qwable-5-27B-Coder-mlx-4Bit / README.md

porschefreak

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: image-text-to-text
	base_model: DJLougen/Qwable-5-27B-Coder
	base_model_relation: finetune
	language:
	- en
	tags:
	- transformers
	- safetensors
	- qwen
	- qwen3_6
	- qwen3_5
	- coder
	- coding-agent
	- agentic-coding
	- tool-use
	- function-calling
	- repository-work
	- terminal-workflows
	- long-context
	- image-text-to-text
	- text-generation-inference
	- unsloth
	- trl
	- mlx
	- mlx-my-repo
	---

	# porschefreak/Qwable-5-27B-Coder-mlx-4Bit

	The Model [porschefreak/Qwable-5-27B-Coder-mlx-4Bit](https://huggingface.co/porschefreak/Qwable-5-27B-Coder-mlx-4Bit) was converted to MLX format from [DJLougen/Qwable-5-27B-Coder](https://huggingface.co/DJLougen/Qwable-5-27B-Coder) using mlx-lm version 0.31.2.

	## Use with mlx

	```bash
	pip install mlx-lm
	```

	```python
	from mlx_lm import load, generate

	model, tokenizer = load("porschefreak/Qwable-5-27B-Coder-mlx-4Bit")

	prompt="hello"

	if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
	messages = [{"role": "user", "content": prompt}]
	prompt = tokenizer.apply_chat_template(
	messages, tokenize=False, add_generation_prompt=True
	)

	response = generate(model, tokenizer, prompt=prompt, verbose=True)
	```