Image-Text-to-Text
Transformers
Safetensors
image-forensics
image-manipulation-detection
visual-tool-use
qwen3-vl
Instructions to use ForgeryVCR-Team/ForgeryVCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ForgeryVCR-Team/ForgeryVCR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ForgeryVCR-Team/ForgeryVCR")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ForgeryVCR-Team/ForgeryVCR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ForgeryVCR-Team/ForgeryVCR with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ForgeryVCR-Team/ForgeryVCR" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ForgeryVCR-Team/ForgeryVCR", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ForgeryVCR-Team/ForgeryVCR
- SGLang
How to use ForgeryVCR-Team/ForgeryVCR 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 "ForgeryVCR-Team/ForgeryVCR" \ --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": "ForgeryVCR-Team/ForgeryVCR", "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 "ForgeryVCR-Team/ForgeryVCR" \ --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": "ForgeryVCR-Team/ForgeryVCR", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ForgeryVCR-Team/ForgeryVCR with Docker Model Runner:
docker model run hf.co/ForgeryVCR-Team/ForgeryVCR
| library_name: transformers | |
| pipeline_tag: image-text-to-text | |
| tags: | |
| - image-forensics | |
| - image-manipulation-detection | |
| - visual-tool-use | |
| - qwen3-vl | |
| # ForgeryVCR | |
| ForgeryVCR is a visual-centric image-forensics agent built on | |
| Qwen3-VL-4B-Instruct. It actively calls ELA, FFT, NPP, and Zoom-In tools, | |
| classifies an image as authentic or tampered, and localizes manipulated regions | |
| with bounding boxes. | |
| The released merged GRPO checkpoint is stored under `GRPO/`. | |
| ## Download | |
| ```bash | |
| hf download youqiwong/ForgeryVCR \ | |
| --repo-type model \ | |
| --include "GRPO/*" \ | |
| --local-dir weights/ForgeryVCR | |
| ``` | |
| Use the checkpoint from the | |
| [ForgeryVCR repository](https://github.com/youqiwong/ForgeryVCR): | |
| ```bash | |
| INFERENCE_MODEL_PATH=weights/ForgeryVCR/GRPO \ | |
| INFERENCE_DATASETS="all" \ | |
| bash scripts/run_inference.sh | |
| ``` | |
| The full tool definitions, prompts, preprocessing, SAM2 mask generation, and | |
| evaluation code are maintained in the project repository. | |