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 RF-Entangle/tokenizer_config.json from Stanley11/Beyond-Visual-Evidence-IDNet: direct link, hf CLI and curl.
- Browser
- Download file 584 Bytes
-
https://huggingface.co/Stanley11/Beyond-Visual-Evidence-IDNet/resolve/main/RF-Entangle/tokenizer_config.json
- Command line
-
hf download hf://Stanley11/Beyond-Visual-Evidence-IDNet/RF-Entangle/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Stanley11/Beyond-Visual-Evidence-IDNet/resolve/main/RF-Entangle/tokenizer_config.json
584 Bytes
| { | |
| "add_prefix_space": null, | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "</s>", | |
| "image_token": "<image>", | |
| "is_local": true, | |
| "legacy": false, | |
| "local_files_only": false, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "model_specific_special_tokens": { | |
| "image_token": "<image>" | |
| }, | |
| "pad_token": "<pad>", | |
| "padding_side": "left", | |
| "processor_class": "LlavaProcessor", | |
| "sp_model_kwargs": {}, | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "<unk>", | |
| "use_default_system_prompt": false | |
| } | |