Image-Text-to-Text
Transformers
Safetensors
English
llava
document-understanding
key-information-extraction
machine-unlearning
privacy
idnet
docprivacybench
Instructions to use Stanley11/Beyond-Visual-Evidence-IDNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Stanley11/Beyond-Visual-Evidence-IDNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Stanley11/Beyond-Visual-Evidence-IDNet")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Stanley11/Beyond-Visual-Evidence-IDNet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Stanley11/Beyond-Visual-Evidence-IDNet with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Stanley11/Beyond-Visual-Evidence-IDNet" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Stanley11/Beyond-Visual-Evidence-IDNet", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Stanley11/Beyond-Visual-Evidence-IDNet
- SGLang
How to use Stanley11/Beyond-Visual-Evidence-IDNet 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 "Stanley11/Beyond-Visual-Evidence-IDNet" \ --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": "Stanley11/Beyond-Visual-Evidence-IDNet", "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 "Stanley11/Beyond-Visual-Evidence-IDNet" \ --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": "Stanley11/Beyond-Visual-Evidence-IDNet", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Stanley11/Beyond-Visual-Evidence-IDNet with Docker Model Runner:
docker model run hf.co/Stanley11/Beyond-Visual-Evidence-IDNet
Download Base/config.json from Stanley11/Beyond-Visual-Evidence-IDNet: direct link, hf CLI and curl.
- Browser
- Download file 1.77 kB
-
https://huggingface.co/Stanley11/Beyond-Visual-Evidence-IDNet/resolve/main/Base/config.json
- Command line
-
hf download hf://Stanley11/Beyond-Visual-Evidence-IDNet/Base/config.json
-
curl -L -o config.json https://huggingface.co/Stanley11/Beyond-Visual-Evidence-IDNet/resolve/main/Base/config.json
1.77 kB
| { | |
| "architectures": [ | |
| "LlavaForConditionalGeneration" | |
| ], | |
| "dtype": "bfloat16", | |
| "ignore_index": -100, | |
| "image_seq_length": 576, | |
| "image_token_index": 32000, | |
| "model_type": "llava", | |
| "multimodal_projector_bias": true, | |
| "pad_token_id": 32001, | |
| "projector_hidden_act": "gelu", | |
| "text_config": { | |
| "_name_or_path": "lmsys/vicuna-7b-v1.5", | |
| "architectures": [ | |
| "LlamaForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 1, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 2, | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 11008, | |
| "max_position_embeddings": 4096, | |
| "mlp_bias": false, | |
| "model_type": "llama", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 32, | |
| "pad_token_id": null, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-05, | |
| "rope_parameters": { | |
| "rope_theta": 10000.0, | |
| "rope_type": "default" | |
| }, | |
| "tie_word_embeddings": false, | |
| "use_cache": true, | |
| "vocab_size": 32064 | |
| }, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "5.9.0", | |
| "use_cache": false, | |
| "vision_config": { | |
| "attention_dropout": 0.0, | |
| "dtype": "bfloat16", | |
| "hidden_act": "quick_gelu", | |
| "hidden_size": 1024, | |
| "image_size": 336, | |
| "initializer_factor": 1.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layer_norm_eps": 1e-05, | |
| "model_type": "clip_vision_model", | |
| "num_attention_heads": 16, | |
| "num_channels": 3, | |
| "num_hidden_layers": 24, | |
| "patch_size": 14, | |
| "projection_dim": 768, | |
| "vocab_size": 32000 | |
| }, | |
| "vision_feature_layer": -2, | |
| "vision_feature_select_strategy": "default", | |
| "vocab_size": 32064 | |
| } | |