Instructions to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8 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-8bit-mxfp8 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-8bit-mxfp8")
config = load_config("zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8")

# 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-8bit-mxfp8 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-8bit-mxfp8")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8")
model = AutoModelForImageTextToText.from_pretrained("zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8")
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
LM Studio

vLLM

How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8 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-8bit-mxfp8"
# 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-8bit-mxfp8",
		"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-8bit-mxfp8

SGLang

How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8 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-8bit-mxfp8" \
    --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-8bit-mxfp8",
		"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-8bit-mxfp8" \
        --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-8bit-mxfp8",
		"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 new

How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8 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 zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8 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 zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8",
    max_seq_length=2048,
)

Pi new

How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8 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-8bit-mxfp8"

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": "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8 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-8bit-mxfp8"

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-8bit-mxfp8

Run Hermes

hermes

Docker Model Runner
How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8 with Docker Model Runner:
```
docker model run hf.co/zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8
```

🦆 zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8

This model was converted to MLX from TeichAI/gemma-4-31B-it-Claude-Opus-Distill using mlx-vlm version 0.4.4. Please refer to the original model card for more details.

🌟 Quality

Quantized vision language model with an effective 8.564 bits per weight.

mlx_vlm.convert --quantize --q-bits 8 --q-group-size 32 --q-mode mxfp8

🛠️ Customizations

This quant 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:

{%- set enable_thinking = true %}

You may also 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-8bit-mxfp8 --max-tokens 100 --temperature 0 --prompt "Describe this image." --image <path_to_image>

Downloads last month: 1,052

Safetensors

Model size

9B params

Tensor type

U32

BF16

MLX

Hardware compatibility

8-bit

Model tree for zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8

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

(16)

this model

zecanard
/

gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8

🦆 zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8

🌟 Quality

🛠️ Customizations

🖥️ Use with `mlx`

Model tree for zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8

Datasets used to train zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8

🦆 zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8

🌟 Quality

🛠️ Customizations

🖥️ Use with mlx

Model tree for zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8

Datasets used to train zecanard/gemma-4-31B-it-Claude-Opus-Distilled-MLX-8bit-mxfp8

🖥️ Use with `mlx`