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
```

GroundAnything / config.json

GroundingPI

Skywalker0410

Publish model weights, paper figures, and complete model card (#1)

37f388f about 4 hours ago

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	{
	"architectures": [
	"GroundAnythingForConditionalGeneration"
	],
	"auto_map": {
	"AutoConfig": "configuration_groundinganything.GroundAnythingConfig",
	"AutoModel": "modeling_groundinganything.GroundAnythingModel",
	"AutoModelForCausalLM": "modeling_groundinganything.GroundAnythingForConditionalGeneration",
	"AutoModelForImageTextToText": "modeling_groundinganything.GroundAnythingForConditionalGeneration",
	"AutoProcessor": "processing_groundinganything.GroundAnythingProcessor"
	},
	"bos_token_id": null,
	"dtype": "bfloat16",
	"eos_token_id": 151645,
	"hidden_size": 2560,
	"image_token_id": 151655,
	"model_type": "groundinganything",
	"pad_token_id": 151643,
	"text_config": {
	"_name_or_path": "Qwen3-4B-Instruct-2507",
	"architectures": [
	"Qwen3ForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"bos_token_id": 151643,
	"dtype": "bfloat16",
	"eos_token_id": 151645,
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 2560,
	"initializer_range": 0.02,
	"intermediate_size": 9728,
	"layer_types": [
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention"
	],
	"max_position_embeddings": 262144,
	"max_window_layers": 36,
	"model_type": "qwen3",
	"num_attention_heads": 32,
	"num_hidden_layers": 36,
	"num_key_value_heads": 8,
	"pad_token_id": 151643,
	"rms_norm_eps": 1e-06,
	"rope_parameters": {
	"rope_theta": 5000000,
	"rope_type": "default"
	},
	"sliding_window": null,
	"tie_word_embeddings": false,
	"use_cache": false,
	"use_sliding_window": false,
	"vocab_size": 152670
	},
	"tie_word_embeddings": false,
	"transformers_version": "5.7.0",
	"use_cache": false,
	"video_token_id": 151656,
	"vision_config": {
	"activation_func": "gelu_pytorch_tanh",
	"attention_dropout": 0.0,
	"attn_bias": false,
	"dtype": "bfloat16",
	"frame_windows_size": 4,
	"hidden_act": "gelu",
	"hidden_size": 1024,
	"image_size": 448,
	"init_pos_emb_height": 64,
	"init_pos_emb_time": 4,
	"init_pos_emb_width": 64,
	"initializer_range": 0.02,
	"intermediate_size": 4096,
	"layer_norm_eps": 1e-06,
	"layer_norm_type": "layer_norm",
	"linear_bias": false,
	"max_position_embeddings": 8192,
	"merge_kernel_size": [
	2,
	2
	],
	"merge_type": "sd2_tpool",
	"mlp_type": "mlp2",
	"model_type": "groundinganything_vision",
	"norm_type": "rmsnorm",
	"num_attention_heads": 12,
	"num_channels": 3,
	"num_hidden_layers": 27,
	"out_hidden_size": 2560,
	"patch_embed_proj_bias": false,
	"patch_position_encoding_type": "absolute",
	"patch_size": 14,
	"pos_emb_interpolation_mode": "bilinear",
	"pos_emb_type": "divided_fixed",
	"projector_hidden_act": "gelu",
	"projector_hidden_size": 4096,
	"projector_ln_eps": 1e-05,
	"qkv_hidden_size": 1536,
	"rope_theta": 10000.0,
	"spatial_merge_size": 2,
	"tokens_per_second": 1,
	"use_head": false,
	"use_patch_position_encoding": false
	},
	"vision_end_token_id": 151653,
	"vision_start_token_id": 151652
	}