Text Generation
Transformers
Safetensors
English
diffusion_gemma
image-text-to-text
diffusion-language-model
generative-ui
openui
openui-lang
gemma
conversational
Instructions to use thesysdev/OUI-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thesysdev/OUI-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="thesysdev/OUI-1") 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("thesysdev/OUI-1") model = AutoModelForMultimodalLM.from_pretrained("thesysdev/OUI-1", 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 thesysdev/OUI-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "thesysdev/OUI-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thesysdev/OUI-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/thesysdev/OUI-1
- SGLang
How to use thesysdev/OUI-1 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 "thesysdev/OUI-1" \ --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": "thesysdev/OUI-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "thesysdev/OUI-1" \ --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": "thesysdev/OUI-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use thesysdev/OUI-1 with Docker Model Runner:
docker model run hf.co/thesysdev/OUI-1
Commit History
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adapter card: license is Apache 2.0, same as the Gemma 4 base
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card: license is Apache 2.0, same as the Gemma 4 base
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scoreboard chart: light theme
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model card: drop the training-data section
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scoreboard chart: current version
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adapter card: OUI-1 capitalisation
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model card: OUI-1 capitalisation
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model card: benchmark renamed to the Generative UI Benchmark
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drop the diffusers pipeline files; the model is served through vLLM and transformers
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adapter card
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model card: verified how-to-use (transformers class, tool_choice, memory, adapter tie)
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diffusers scheduler config, same as the base repo
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diffusers pipeline index, same as the base repo
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model card: drop briefs section
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scoreboard chart for the card
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model card: lean results with the scoreboard chart
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card: how to run (vLLM, OpenAI client, function calling, transformers)
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card: real step sweep, serve line without ignored hf-overrides
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LoRA adapter on google/diffusiongemma-26B-A4B-it
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oui-1 merged weights (bf16)
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Upload README.md with huggingface_hub
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initial commit
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