Instructions to use mlx-community/Gemma-SEA-LION-v4-27B-IT-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Gemma-SEA-LION-v4-27B-IT-bf16 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-SEA-LION-v4-27B-IT-bf16")

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-SEA-LION-v4-27B-IT-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="mlx-community/Gemma-SEA-LION-v4-27B-IT-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-SEA-LION-v4-27B-IT-bf16")
model = AutoModelForMultimodalLM.from_pretrained("mlx-community/Gemma-SEA-LION-v4-27B-IT-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?"}
        ]
    },
]
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-SEA-LION-v4-27B-IT-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-SEA-LION-v4-27B-IT-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-SEA-LION-v4-27B-IT-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlx-community/Gemma-SEA-LION-v4-27B-IT-bf16

SGLang

How to use mlx-community/Gemma-SEA-LION-v4-27B-IT-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-SEA-LION-v4-27B-IT-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-SEA-LION-v4-27B-IT-bf16",
		"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-SEA-LION-v4-27B-IT-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-SEA-LION-v4-27B-IT-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

MLX LM

How to use mlx-community/Gemma-SEA-LION-v4-27B-IT-bf16 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-SEA-LION-v4-27B-IT-bf16"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/Gemma-SEA-LION-v4-27B-IT-bf16"
# 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-SEA-LION-v4-27B-IT-bf16",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use mlx-community/Gemma-SEA-LION-v4-27B-IT-bf16 with Docker Model Runner:
```
docker model run hf.co/mlx-community/Gemma-SEA-LION-v4-27B-IT-bf16
```

Gemma-SEA-LION-v4-27B-IT-bf16

File size: 840 Bytes

c017e4f

{
    "architectures": [
        "Gemma3ForConditionalGeneration"
    ],
    "boi_token_index": 255999,
    "eoi_token_index": 256000,
    "eos_token_id": [
        1,
        106
    ],
    "image_token_index": 262144,
    "initializer_range": 0.02,
    "mm_tokens_per_image": 256,
    "model_type": "gemma3",
    "text_config": {
        "head_dim": 128,
        "hidden_size": 5376,
        "intermediate_size": 21504,
        "model_type": "gemma3_text",
        "num_attention_heads": 32,
        "num_hidden_layers": 62,
        "num_key_value_heads": 16,
        "query_pre_attn_scalar": 168,
        "rope_scaling": {
            "factor": 8.0,
            "rope_type": "linear"
        },
        "sliding_window": 1024,
        "vocab_size": 262208
    },
    "torch_dtype": "bfloat16",
    "transformers_version": "4.50.0.dev0"
}