Instructions to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine 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("zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine")
config = load_config("zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine")

# 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)

Transformers

How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine with Transformers:

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

pipe = pipeline("image-text-to-text", model="zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine")
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("zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine")
model = AutoModelForMultimodalLM.from_pretrained("zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine",
		"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/zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine

SGLang

How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine 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 "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine" \
    --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": "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine",
		"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 "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine" \
        --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": "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine",
		"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 Desktop

How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine"

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": "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine with Docker Model Runner:
```
docker model run hf.co/zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine
```

Hermes Agent

How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine 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 "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine"

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 zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine"

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 "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine" \
  --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"

🦆 zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine

This model was converted to MLX from TeichAI/gemma-4-31B-it-Claude-Opus-Distill using mlx-vlm version 0.6.3. Please refer to the original model card for more details.

🌟 Quality

Quantized vision language model with an effective 7.170 bits per weight.

mlx_vlm.convert --quantize --q-group-size 32 --q-bits 6 --q-mode affine

🛠️ Customizations

This quant includes a bugfix related to tools calling. It is aware of the current date, and also enables thinking (if available). You may disable this behavior by deleting the following line from the chat template, or changing true to false:

{%- set enable_thinking = true %}

You may need to adjust your environment’s Reasoning Section Parsing to recognize <|channel>thought as the Start String, and <channel|> as the End String.

🖥️ Use with `mlx`

pip install -U mlx-vlm

mlx_vlm.generate --model zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine --max-tokens 100 --temperature 0 --prompt "Describe this image." --image <path_to_image>

Downloads last month: 149

Safetensors

Model size

31B params

Tensor type

U32

BF16

MLX

Hardware compatibility

6-bit

Model tree for zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine

Base model

google/gemma-4-31B

Finetuned

google/gemma-4-31B-it

Finetuned

unsloth/gemma-4-31B-it

Finetuned

TeichAI/gemma-4-31B-it-Claude-Opus-Distill

Quantized

(15)

this model

zecanard
/

gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine

🦆 zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine

🌟 Quality

🛠️ Customizations

🖥️ Use with `mlx`

Model tree for zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine

Datasets used to train zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine

🦆 zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine

🌟 Quality

🛠️ Customizations

🖥️ Use with mlx

Model tree for zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine

Datasets used to train zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-6bit-int6-affine

🖥️ Use with `mlx`