Image-Text-to-Text
Transformers
Safetensors
English
Chinese
groundinganything_vlm
text-generation
visual-grounding
object-detection
referring-expression-comprehension
pointing
ocr
document-layout
custom-code
conversational
custom_code
Instructions to use GroundingPI/GroundAnything-VLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GroundingPI/GroundAnything-VLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="GroundingPI/GroundAnything-VLM", 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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("GroundingPI/GroundAnything-VLM", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use GroundingPI/GroundAnything-VLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "GroundingPI/GroundAnything-VLM" # 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-VLM", "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-VLM
- SGLang
How to use GroundingPI/GroundAnything-VLM 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-VLM" \ --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-VLM", "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-VLM" \ --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-VLM", "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-VLM with Docker Model Runner:
docker model run hf.co/GroundingPI/GroundAnything-VLM
File size: 3,964 Bytes
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"architectures": [
"GroundAnythingVLMForConditionalGeneration"
],
"auto_map": {
"AutoConfig": "configuration_groundinganything.GroundAnythingVLMConfig",
"AutoModel": "modeling_groundinganything.GroundAnythingVLMModel",
"AutoModelForCausalLM": "modeling_groundinganything.GroundAnythingVLMForConditionalGeneration",
"AutoModelForImageTextToText": "modeling_groundinganything.GroundAnythingVLMForConditionalGeneration",
"AutoProcessor": "processing_groundinganything.GroundAnythingVLMProcessor"
},
"bos_token_id": null,
"dtype": "bfloat16",
"eos_token_id": 151645,
"hidden_size": 2560,
"image_token_id": 151655,
"model_type": "groundinganything_vlm",
"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": [
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],
"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": [
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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
}
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