Instructions to use aimagelab/ReflectiVA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aimagelab/ReflectiVA with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="aimagelab/ReflectiVA")
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)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("aimagelab/ReflectiVA", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aimagelab/ReflectiVA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aimagelab/ReflectiVA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aimagelab/ReflectiVA",
		"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/aimagelab/ReflectiVA

SGLang

How to use aimagelab/ReflectiVA 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 "aimagelab/ReflectiVA" \
    --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": "aimagelab/ReflectiVA",
		"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 "aimagelab/ReflectiVA" \
        --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": "aimagelab/ReflectiVA",
		"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 aimagelab/ReflectiVA with Docker Model Runner:
```
docker model run hf.co/aimagelab/ReflectiVA
```

Add links to Github repository, project page and dataset

by nielsr HF Staff - opened Apr 3, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

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Files changed (1) hide show

README.md +3 -2

README.md CHANGED Viewed

@@ -1,8 +1,9 @@
 ---
 library_name: transformers
-pipeline_tag: image-text-to-text
 license: apache-2.0
 ---
 # Model Card: Reflective LLaVA (ReflectiVA)
 Multimodal LLMs (MLLMs) are the natural extension of large language models to handle multimodal inputs, combining text and image data.
@@ -20,7 +21,7 @@ superior performance compared to existing methods.
 In this model space, you will find the Overall Model (stage two) weights of ```ReflectiVA```.
-For more information, visit our [ReflectiVA repository](https://github.com/aimagelab/ReflectiVA).
 ## Citation
 If you make use of our work, please cite our repo:

 ---
 library_name: transformers
 license: apache-2.0
+pipeline_tag: image-text-to-text
 ---
 # Model Card: Reflective LLaVA (ReflectiVA)
 Multimodal LLMs (MLLMs) are the natural extension of large language models to handle multimodal inputs, combining text and image data.
 In this model space, you will find the Overall Model (stage two) weights of ```ReflectiVA```.
+For more information, visit our [ReflectiVA repository](https://github.com/aimagelab/ReflectiVA), our [project page](https://aimagelab.github.io/ReflectiVA/) and the [dataset](https://huggingface.co/datasets/aimagelab/ReflectiVA-Data).
 ## Citation
 If you make use of our work, please cite our repo: