Instructions to use thegovind/blink-mimo-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thegovind/blink-mimo-9b with Transformers:

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

pipe = pipeline("image-text-to-text", model="thegovind/blink-mimo-9b")
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("thegovind/blink-mimo-9b")
model = AutoModelForMultimodalLM.from_pretrained("thegovind/blink-mimo-9b", 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

vLLM

How to use thegovind/blink-mimo-9b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thegovind/blink-mimo-9b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thegovind/blink-mimo-9b",
		"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/thegovind/blink-mimo-9b

SGLang

How to use thegovind/blink-mimo-9b 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 "thegovind/blink-mimo-9b" \
    --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": "thegovind/blink-mimo-9b",
		"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 "thegovind/blink-mimo-9b" \
        --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": "thegovind/blink-mimo-9b",
		"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 thegovind/blink-mimo-9b with Docker Model Runner:
```
docker model run hf.co/thegovind/blink-mimo-9b
```

blink-mimo-9b / LICENSE.md

thegovind

Card and licence corrections: audit scope, reproduction wording, MiMo lineage

8f77fd0 verified 4 days ago

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blink model weights: non-commercial research licence

These weights are a modification of XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B (its model card declares MIT; see LICENSE-MiMo.md). It is based on Qwen/Qwen3.5-9B (Apache License 2.0; see LICENSE-Qwen). The modification (fine-tuning with LoRA adapters merged into the weights) was made by thegovind.

The fine-tuning data included third-party datasets released under different terms, among them non-commercial (CC BY-NC 3.0 / 4.0) and share-alike (CC BY-SA 3.0 / 4.0) licences, and sources that state no licence (listed in README.md). Whether and how those terms apply to trained weights is unsettled.

The author of the modification permits you to use, copy and run it for non-commercial research and evaluation only, provided LICENSE.md, LICENSE-MiMo.md and LICENSE-Qwen are kept with every copy. No licence for commercial use is granted, and nothing here grants rights in any third-party data. You remain responsible for complying with MiMo's declared MIT terms and the Apache License 2.0 for the Qwen base weights and with the terms of the upstream datasets.

THE WEIGHTS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND.

This is a personal research release. It is not an official product of any company, and it is not affiliated with TypeSafe AI, Xiaomi, Alibaba Cloud or the Qwen team.