Instructions to use nvidia/Flora-Captioner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Omar L commited on 6 days ago

Commit

fef0bbf

verified ·

1 Parent(s): f84c731

Add Flora inference examples

Browse files

Adds the TempSeg-to-Captioner inference examples from flora-share/Flora-Captioner-Release-Infer@657752f279164052d2acb22da11dfcf97207d680. Binds the examples to immutable NVIDIA revisions with corrected model-card checksums. This PR and the corresponding nvidia/Flora-TempSeg PR are a paired update.

The existing model card, weights, provenance, licenses, configuration, and templates are preserved. Only the model-card checksum entry changes among existing files. Static syntax, code-manifest, vendor-manifest, and model-asset identity checks passed; CUDA inference was not run. Keep the referenced PR commits available; if the merge strategy does not retain them, update both revision pins before deleting the PR refs.

Files changed (1) hide show

SOURCE_MANIFEST.sha256 +1 -1

SOURCE_MANIFEST.sha256 CHANGED Viewed

@@ -1,5 +1,5 @@
 b1979e729def7828c8d7600692ba17a3913bdac6ddd152c22bfcabd3f0ad924e  MODEL_PROVENANCE.json
-550949219039f7428a71f23cfc286618ba353c473d2accf15498ff9ca441e2d2  README.md
 d28ca68768ac2ce0ad42eaaf54d5bac4f8a298f7d017f5a04f3866094c65a839  chat_template.jinja
 81ac7cb6c4b9d01e4232891cd9c0cd041f7e3270b342f7641eb7043a345e98af  config.json
 fb58199b78abc0e3c284d170ecddf488501ca9fe91d9e435d6801f85b1a50ff0  generation_config.json

 b1979e729def7828c8d7600692ba17a3913bdac6ddd152c22bfcabd3f0ad924e  MODEL_PROVENANCE.json
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 d28ca68768ac2ce0ad42eaaf54d5bac4f8a298f7d017f5a04f3866094c65a839  chat_template.jinja
 81ac7cb6c4b9d01e4232891cd9c0cd041f7e3270b342f7641eb7043a345e98af  config.json
 fb58199b78abc0e3c284d170ecddf488501ca9fe91d9e435d6801f85b1a50ff0  generation_config.json