Instructions to use MiniMaxAI/MiniMax-H3-Image-PE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MiniMaxAI/MiniMax-H3-Image-PE with Transformers:

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

pipe = pipeline("text-generation", model="MiniMaxAI/MiniMax-H3-Image-PE")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("MiniMaxAI/MiniMax-H3-Image-PE")
model = AutoModelForMultimodalLM.from_pretrained("MiniMaxAI/MiniMax-H3-Image-PE", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MiniMaxAI/MiniMax-H3-Image-PE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MiniMaxAI/MiniMax-H3-Image-PE"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MiniMaxAI/MiniMax-H3-Image-PE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MiniMaxAI/MiniMax-H3-Image-PE

SGLang

How to use MiniMaxAI/MiniMax-H3-Image-PE 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 "MiniMaxAI/MiniMax-H3-Image-PE" \
    --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": "MiniMaxAI/MiniMax-H3-Image-PE",
		"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 "MiniMaxAI/MiniMax-H3-Image-PE" \
        --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": "MiniMaxAI/MiniMax-H3-Image-PE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MiniMaxAI/MiniMax-H3-Image-PE with Docker Model Runner:
```
docker model run hf.co/MiniMaxAI/MiniMax-H3-Image-PE
```

MiniMax-H3-Image-PE

File size: 1,034 Bytes

5cb2132

{
  "reasoning_effort": "xhigh",
  "temperature": 1.0,
  "top_p": 0.95,
  "top_k": 20,
  "min_p": 0.0,
  "presence_penalty": 0.0,
  "repetition_penalty": 1.0,
  "chat_template_kwargs": {
    "enable_thinking": true,
    "preserve_thinking": true
  },
  "response_format": {
    "type": "json_schema",
    "json_schema": {
      "name": "expanded_caption_and_aspect_ratio",
      "strict": true,
      "schema": {
        "type": "object",
        "properties": {
          "expanded_caption": {
            "type": "string"
          },
          "aspect_ratio": {
            "type": "string",
            "enum": [
              "1:1",
              "5:4",
              "4:3",
              "3:2",
              "16:9",
              "21:9",
              "4:5",
              "3:4",
              "2:3",
              "9:16"
            ]
          }
        },
        "required": [
          "expanded_caption",
          "aspect_ratio"
        ],
        "additionalProperties": false
      }
    }
  },
  "max_tokens": 4096
}