Image-Text-to-Text
Transformers
PyTorch
Safetensors
florence2
GUI
VLM
Agent
GUI-Grounding
custom_code
Instructions to use HongxinLi/GoClick-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HongxinLi/GoClick-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="HongxinLi/GoClick-Large", trust_remote_code=True)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("HongxinLi/GoClick-Large", trust_remote_code=True) model = AutoModelForImageTextToText.from_pretrained("HongxinLi/GoClick-Large", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use HongxinLi/GoClick-Large with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HongxinLi/GoClick-Large" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HongxinLi/GoClick-Large", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HongxinLi/GoClick-Large
- SGLang
How to use HongxinLi/GoClick-Large 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 "HongxinLi/GoClick-Large" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HongxinLi/GoClick-Large", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "HongxinLi/GoClick-Large" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HongxinLi/GoClick-Large", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HongxinLi/GoClick-Large with Docker Model Runner:
docker model run hf.co/HongxinLi/GoClick-Large
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4cc3a82 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | {
"_name_or_path": "florence2",
"architectures": [
"Florence2ForConditionalGeneration"
],
"auto_map": {
"AutoConfig": "configuration_florence2.Florence2Config",
"AutoModelForCausalLM": "modeling_florence2.Florence2ForConditionalGeneration"
},
"bos_token_id": 0,
"eos_token_id": 2,
"ignore_index": -100,
"model_type": "florence2",
"pad_token_id": 1,
"projection_dim": 1024,
"text_config": {
"vocab_size": 51289,
"activation_dropout": 0.1,
"activation_function": "gelu",
"add_bias_logits": false,
"add_final_layer_norm": false,
"attention_dropout": 0.1,
"bos_token_id": 0,
"classif_dropout": 0.1,
"classifier_dropout": 0.0,
"d_model": 1024,
"decoder_attention_heads": 16,
"decoder_ffn_dim": 4096,
"decoder_layerdrop": 0.0,
"decoder_layers": 12,
"decoder_start_token_id": 2,
"dropout": 0.1,
"early_stopping": true,
"encoder_attention_heads": 16,
"encoder_ffn_dim": 4096,
"encoder_layerdrop": 0.0,
"encoder_layers": 12,
"eos_token_id": 2,
"forced_eos_token_id": 2,
"forced_bos_token_id": 0,
"gradient_checkpointing": false,
"init_std": 0.02,
"is_encoder_decoder": true,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"max_position_embeddings": 4096,
"no_repeat_ngram_size": 3,
"normalize_before": false,
"num_hidden_layers": 12,
"pad_token_id": 1,
"scale_embedding": false,
"num_beams": 3
},
"vision_config": {
"model_type": "davit",
"drop_path_rate": 0.1,
"patch_size": [7, 3, 3, 3],
"patch_stride": [4, 2, 2, 2],
"patch_padding": [3, 1, 1, 1],
"patch_prenorm": [false, true, true, true],
"enable_checkpoint": false,
"dim_embed": [256, 512, 1024, 2048],
"num_heads": [8, 16, 32, 64],
"num_groups": [8, 16, 32, 64],
"depths": [1, 1, 9, 1],
"window_size": 12,
"projection_dim": 1024,
"visual_temporal_embedding": {
"type": "COSINE",
"max_temporal_embeddings": 100
},
"image_pos_embed": {
"type": "learned_abs_2d",
"max_pos_embeddings": 50
},
"image_feature_source": ["spatial_avg_pool", "temporal_avg_pool"]
},
"vocab_size": 51289,
"torch_dtype": "float16",
"transformers_version": "4.41.0.dev0",
"is_encoder_decoder": true
} |