Instructions to use CaraJ/ORM-T2I-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CaraJ/ORM-T2I-R1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="CaraJ/ORM-T2I-R1") 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("CaraJ/ORM-T2I-R1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use CaraJ/ORM-T2I-R1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CaraJ/ORM-T2I-R1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CaraJ/ORM-T2I-R1", "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/CaraJ/ORM-T2I-R1
- SGLang
How to use CaraJ/ORM-T2I-R1 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 "CaraJ/ORM-T2I-R1" \ --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": "CaraJ/ORM-T2I-R1", "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 "CaraJ/ORM-T2I-R1" \ --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": "CaraJ/ORM-T2I-R1", "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 CaraJ/ORM-T2I-R1 with Docker Model Runner:
docker model run hf.co/CaraJ/ORM-T2I-R1
Add metadata
Browse filesThis PR adds the library name, pipeline tag and base model metadata.
README.md
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This is the output reward model (ORM) used in [T2I-R1](https://github.com/CaraJ7/T2I-R1).
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This model is fine-tuned from [lmms-lab/llava-onevision-qwen2-7b-ov](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov).
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---
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library_name: transformers
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pipeline_tag: image-text-to-text
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base_model:
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- lmms-lab/llava-onevision-qwen2-7b-ov
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---
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This is the output reward model (ORM) used in [T2I-R1](https://github.com/CaraJ7/T2I-R1).
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This model is fine-tuned from [lmms-lab/llava-onevision-qwen2-7b-ov](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov).
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