Instructions to use aimagelab/ReflectiVA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Update config.json
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config.json
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{
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"_name_or_path": "
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"architectures": [
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"LlavaLlamaForCausalLM"
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],
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"mm_use_im_start_end": false,
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"mm_vision_select_feature": "patch",
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"mm_vision_select_layer": -2,
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"mm_vision_tower": "
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"model_type": "llava_llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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{
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"_name_or_path": "reflectiva",
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"architectures": [
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"LlavaLlamaForCausalLM"
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],
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"mm_use_im_start_end": false,
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"mm_vision_select_feature": "patch",
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"mm_vision_select_layer": -2,
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"mm_vision_tower": "openai/clip-vit-large-patch14-336",
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"model_type": "llava_llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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