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

Libraries

How to use GroundingPI/GroundAnything with Transformers:

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

pipe = pipeline("image-text-to-text", model="GroundingPI/GroundAnything", trust_remote_code=True)
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 AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("GroundingPI/GroundAnything", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GroundingPI/GroundAnything with vLLM:

Install from pip and serve model

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

SGLang

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

Keep inline title logos compact without image margins

by Skywalker0410 - opened 37 minutes ago

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

-2

Files changed (2) hide show

README.md +1 -1
checksums.sha256 +1 -1

README.md CHANGED Viewed

@@ -19,7 +19,7 @@ tags:
 inference: false
 ---
-# <img src="https://huggingface.co/GroundingPI/GroundAnything/resolve/0f8e30894c3ca86378d01ae51ec69c217c78151b/assets/logo.png" width="40" style="display: inline-block; vertical-align: middle;" alt="GroundAnything logo" /> GroundAnything: Reconciling Parallel Decoding with Precise Visual Grounding at Flash Speed
 **Model family:** [GroundAnything — DLM / parallel decoding](https://huggingface.co/GroundingPI/GroundAnything) · [GroundAnything-VLM — autoregressive](https://huggingface.co/GroundingPI/GroundAnything-VLM).

 inference: false
 ---
+# <img src="https://huggingface.co/GroundingPI/GroundAnything/resolve/0f8e30894c3ca86378d01ae51ec69c217c78151b/assets/logo.png" width="40" style="display: inline-block; vertical-align: middle; margin: 0;" alt="GroundAnything logo" /> GroundAnything: Reconciling Parallel Decoding with Precise Visual Grounding at Flash Speed
 **Model family:** [GroundAnything — DLM / parallel decoding](https://huggingface.co/GroundingPI/GroundAnything) · [GroundAnything-VLM — autoregressive](https://huggingface.co/GroundingPI/GroundAnything-VLM).

checksums.sha256 CHANGED Viewed

@@ -1,5 +1,5 @@
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-2d35aefda0bfa737399fc86f80847304eeeba16a0889eff3d1d6202d6b3a50f8  README.md
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