Instructions to use AI4SGI/ExoMind with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AI4SGI/ExoMind with Transformers:

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

pipe = pipeline("image-text-to-text", model="AI4SGI/ExoMind")
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("AI4SGI/ExoMind")
model = AutoModelForMultimodalLM.from_pretrained("AI4SGI/ExoMind", 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 AI4SGI/ExoMind with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AI4SGI/ExoMind"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AI4SGI/ExoMind",
		"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/AI4SGI/ExoMind

SGLang

How to use AI4SGI/ExoMind 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 "AI4SGI/ExoMind" \
    --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": "AI4SGI/ExoMind",
		"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 "AI4SGI/ExoMind" \
        --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": "AI4SGI/ExoMind",
		"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 AI4SGI/ExoMind with Docker Model Runner:
```
docker model run hf.co/AI4SGI/ExoMind
```

Leon02 commited on 5 days ago

Commit

d37e2cb

verified ·

1 Parent(s): 1fa71c9

Add ExoMind model-card assets

Browse files

Files changed (5) hide show

.gitattributes +3 -0
assets/ExoMind.png +0 -0
assets/fig1-benchmark.png +3 -0
assets/fig2-paradigm.png +3 -0
assets/fig8-benchmarks.png +3 -0

.gitattributes CHANGED Viewed

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/fig1-benchmark.png filter=lfs diff=lfs merge=lfs -text
+assets/fig2-paradigm.png filter=lfs diff=lfs merge=lfs -text
+assets/fig8-benchmarks.png filter=lfs diff=lfs merge=lfs -text

assets/ExoMind.png ADDED Viewed

assets/fig1-benchmark.png ADDED Viewed

Git LFS Details

SHA256: 095ac815a1283a9030cdbc42457b11b8b8c964d7c77af06bc371cf76894ffb0d
Pointer size: 131 Bytes
Size of remote file: 559 kB

assets/fig2-paradigm.png ADDED Viewed

Git LFS Details

SHA256: 4107b0e312950fa1ed5dc8bec34a60639999c043d56d0cd5fbda1128d3e3063a
Pointer size: 131 Bytes
Size of remote file: 798 kB

assets/fig8-benchmarks.png ADDED Viewed

Git LFS Details

SHA256: 7a515513f7f7d9d91702c289d7a89c1f5aa5998d3b15b8fdda4fdf2b81cbb4d9
Pointer size: 132 Bytes
Size of remote file: 1.43 MB