Instructions to use mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Transformers

How to use mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit with Transformers:

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

pipe = pipeline("text-generation", model="mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit")
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("mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit")
model = AutoModelForMultimodalLM.from_pretrained("mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit")
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?"}
        ]
    },
]
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit

SGLang

How to use mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit 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 "mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit" \
    --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": "mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit" \
        --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": "mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

How to use mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit 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/gemma-4-12b-coder-fable5-composer2.5-4bit"

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/gemma-4-12b-coder-fable5-composer2.5-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit 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/gemma-4-12b-coder-fable5-composer2.5-4bit"

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/gemma-4-12b-coder-fable5-composer2.5-4bit

Run Hermes

hermes

MLX LM

How to use mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit with Docker Model Runner:
```
docker model run hf.co/mlx-community/gemma-4-12b-coder-fable5-composer2.5-4bit
```

M4 Pro - 48 GB RAM - does not run

by R3TRO04 - opened 6 days ago

Discussion

R3TRO04

6 days ago

Traceback (most recent call last):
  File ".local/share/uv/tools/mlx-lm/lib/python3.11/site-packages/mlx_lm/utils.py", line 188, in _get_classes
    arch = importlib.import_module(f"mlx_lm.models.{model_type}")
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File ".local/share/uv/python/cpython-3.11.15-macos-aarch64-none/lib/python3.11/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<frozen importlib._bootstrap>", line 1204, in _gcd_import
  File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1140, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'mlx_lm.models.gemma4_unified'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File ".local/bin/mlx_lm.chat", line 10, in <module>
    sys.exit(main())
             ^^^^^^
  File ".local/share/uv/tools/mlx-lm/lib/python3.11/site-packages/mlx_lm/chat.py", line 110, in main
    model, tokenizer = load(
                       ^^^^^
  File ".local/share/uv/tools/mlx-lm/lib/python3.11/site-packages/mlx_lm/utils.py", line 491, in load
    model, config = load_model(model_path, lazy, model_config=model_config)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File ".local/share/uv/tools/mlx-lm/lib/python3.11/site-packages/mlx_lm/utils.py", line 334, in load_model
    model_class, model_args_class = get_model_classes(config=config)
                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File ".local/share/uv/tools/mlx-lm/lib/python3.11/site-packages/mlx_lm/utils.py", line 191, in _get_classes
    raise ValueError(msg)
ValueError: Model type gemma4_unified not supported.

am i missing something?

usermma

3 days ago

try upgrade your version of mlx...

maybe pip3 install mlx-lm -U
-U stands for upgrade i guess...

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