Image-Text-to-Text
Transformers
Safetensors
English
qwen3_5
decision-model
typed-decisions
one-pass
option-probabilities
conversational
Instructions to use thegovind/blink-mimo-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Card: link Screen click and the Computer use docs
Browse files
README.md
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## Screenshots (opt-in, self-hosted)
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Image input is off by default. Start `serve.py` with `--vision`, or set `BLINK_VISION=1` for direct `blink.py` calls. Image mode needs `torchvision==0.28.0`; it uses MiMo's unchanged tower.
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This is a self-hosted blink extension. TypeSafe's hosted Jev is text-only and its published API has no image field. Put a `data:image/png;base64,...` URI (JPEG or WebP also work) in a string in `state`, or send data URIs in a top-level `images` list. Image URLs are not fetched. `--image-layout first|inline` places image tokens; `first` is the default, not a switch that enables images. For renamed folders or Docker's `/blink`, use `--model-name blink-mimo-9b` or `BLINK_MODEL_NAME=blink-mimo-9b`; model checks still apply.
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## Screenshots (opt-in, self-hosted)
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Try [Screen click](https://huggingface.co/spaces/thegovind/blink?tab=use-cases&case=screen), read by this model in the Space. See [Computer use](https://thegovind.github.io/blink/computer-use/) for setup and links.
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Image input is off by default. Start `serve.py` with `--vision`, or set `BLINK_VISION=1` for direct `blink.py` calls. Image mode needs `torchvision==0.28.0`; it uses MiMo's unchanged tower.
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This is a self-hosted blink extension. TypeSafe's hosted Jev is text-only and its published API has no image field. Put a `data:image/png;base64,...` URI (JPEG or WebP also work) in a string in `state`, or send data URIs in a top-level `images` list. Image URLs are not fetched. `--image-layout first|inline` places image tokens; `first` is the default, not a switch that enables images. For renamed folders or Docker's `/blink`, use `--model-name blink-mimo-9b` or `BLINK_MODEL_NAME=blink-mimo-9b`; model checks still apply.
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