Image-Text-to-Text
Transformers
Safetensors
qwen3_5
qwen3.5
video
video-captioning
data-engine
conversational
Instructions to use nvidia/Flora-Captioner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/Flora-Captioner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="nvidia/Flora-Captioner") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("nvidia/Flora-Captioner") model = AutoModelForMultimodalLM.from_pretrained("nvidia/Flora-Captioner", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nvidia/Flora-Captioner with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nvidia/Flora-Captioner" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nvidia/Flora-Captioner", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/nvidia/Flora-Captioner
- SGLang
How to use nvidia/Flora-Captioner 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 "nvidia/Flora-Captioner" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nvidia/Flora-Captioner", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "nvidia/Flora-Captioner" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nvidia/Flora-Captioner", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use nvidia/Flora-Captioner with Docker Model Runner:
docker model run hf.co/nvidia/Flora-Captioner
Download batch_utils/vendor/MANIFEST.json from nvidia/Flora-Captioner: direct link, hf CLI and curl.
- Browser
- Download file 3.23 kB
-
https://huggingface.co/nvidia/Flora-Captioner/resolve/main/batch_utils/vendor/MANIFEST.json
- Command line
-
hf download hf://nvidia/Flora-Captioner/batch_utils/vendor/MANIFEST.json
-
curl -L -o MANIFEST.json https://huggingface.co/nvidia/Flora-Captioner/resolve/main/batch_utils/vendor/MANIFEST.json
3.23 kB
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| "production_adapter/core/flora_runtime/worker_transport.py": "30fb64869eb6a27ab96bf394c105f19fcb42590be4424c66b76b89e17631693a", | |
| "production_gdn_fix.py": "a3f52604bfa2ec58901df63215df5e47d37e1d7974aa56d36a1e4efe8cad3fc6", | |
| "pts_input_adapter.py": "d784d6b77005eb6945dee334e4c9136fbededfa2049d5945244f198517984767", | |
| "robot_caption_geometry.py": "1c874d3c63359663276d31a3e9c3112a5982ad4000f44c808541b2471f5f7d2d", | |
| "support/canonical_rgb.py": "1e3230c60b45d8e017060d8a5dd2b5f48e2199192f09dcff4d04c05b4349e35a", | |
| "support/composers.py": "180b97a6a634979c1b0dbe720ed7a1abd818dc7ee31fdc4eeca7a8277e43d1b6", | |
| "support/plans.py": "d75b918804c9ec10f8642d362a54a74c3eb875abf22e8a60228a0d6b2cbb570e", | |
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| "tempseg_fp32_loader.py": "7ee9889f5aa5909abf3ecd1c34e3ac2f81a32a2098d94ae3ba9f33831e7a07c0", | |
| "verify_gdn.py": "96596ff2db4425f4dea71798aeabb581aa83e1ad8a878cfcb9efb8dbac80cda6" | |
| } | |