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
File size: 1,773 Bytes
4b500e7 | 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 | {
"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
}
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