Instructions to use mlx-community/gemma-3-27b-it-qat-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/gemma-3-27b-it-qat-bf16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="mlx-community/gemma-3-27b-it-qat-bf16")
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-3-27b-it-qat-bf16")
model = AutoModelForMultimodalLM.from_pretrained("mlx-community/gemma-3-27b-it-qat-bf16", 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 mlx-community/gemma-3-27b-it-qat-bf16 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/gemma-3-27b-it-qat-bf16")
config = load_config("mlx-community/gemma-3-27b-it-qat-bf16")

# 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 mlx-community/gemma-3-27b-it-qat-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlx-community/gemma-3-27b-it-qat-bf16"
# 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-3-27b-it-qat-bf16",
		"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/mlx-community/gemma-3-27b-it-qat-bf16

SGLang

How to use mlx-community/gemma-3-27b-it-qat-bf16 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-3-27b-it-qat-bf16" \
    --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-3-27b-it-qat-bf16",
		"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 "mlx-community/gemma-3-27b-it-qat-bf16" \
        --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-3-27b-it-qat-bf16",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use mlx-community/gemma-3-27b-it-qat-bf16 with Docker Model Runner:
```
docker model run hf.co/mlx-community/gemma-3-27b-it-qat-bf16
```

prince-canuma commited on Apr 18, 2025

Commit

1d6e2ec

verified ·

1 Parent(s): 6d7ef9e

Update README.md

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@@ -1,26 +1,25 @@
 ---
-license: other
-license_name: qwen
-license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
-pipeline_tag: image-text-to-text
-library_name: transformers
-base_model:
-- OpenGVLab/InternViT-300M-448px-V2_5
-- Qwen/Qwen2.5-0.5B
-base_model_relation: merge
-datasets:
-- OpenGVLab/MMPR-v1.2
-language:
-- multilingual
 tags:
-- internvl
-- custom_code
 - mlx
 ---
-# mlx-gg-hf/gemma-3-27b-it-qat-bf16
-This model was converted to MLX format from [`gg-hf-g/gemma-3-27b-it-qat-q4_0-unquantized`]() using mlx-vlm version **0.1.23**.
-Refer to the [original model card](https://huggingface.co/gg-hf-g/gemma-3-27b-it-qat-q4_0-unquantized) for more details on the model.
 ## Use with mlx
 ```bash
@@ -28,5 +27,5 @@ pip install -U mlx-vlm
 ```
 ```bash
-python -m mlx_vlm.generate --model mlx-gg-hf/gemma-3-27b-it-qat-bf16 --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
 ```

 ---
+base_model: google/gemma-3-27b-it
+license: gemma
 tags:
+- gemma3
+- gemma
+- google
 - mlx
+pipeline_tag: image-text-to-text
+library_name: transformers
+extra_gated_heading: Access Gemma on Hugging Face
+extra_gated_prompt: >-
+  To access Gemma on Hugging Face, you’re required to review and agree to
+  Google’s usage license. To do this, please ensure you’re logged in to Hugging
+  Face and click below. Requests are processed immediately.
+extra_gated_button_content: Acknowledge license
 ---
+# mlx-community/gemma-3-27b-it-qat-bf16
+This model was converted to MLX format from [`google/gemma-3-27b-it-qat-q4_0-unquantized`]() using mlx-vlm version **0.1.23**.
+Refer to the [original model card](https://huggingface.co/google/gemma-3-27b-it-qat-q4_0-unquantized) for more details on the model.
 ## Use with mlx
 ```bash
 ```
 ```bash
+python -m mlx_vlm.generate --model mlx-community/gemma-3-27b-it-qat-bf16 --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
 ```