Instructions to use thesysdev/OUI-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

andhakanoon commited on Sep 8

Commit

d6f56af

verified ·

1 Parent(s): 83fdfd2

model card: drop briefs section

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@@ -151,20 +151,6 @@ print(processor.decode(out.sequences[0, ids.shape[1]:], skip_special_tokens=True
 The LoRA adapter that produced these weights is in `adapter/` (PEFT format, base
 `google/diffusiongemma-26B-A4B-it`), for anyone who prefers to load base plus adapter.
-### Briefs
-Briefs in the form the model was trained on work best:
-```
-A fitness app's daily summary. Build a single screen for this. It must show:
-1. step count today against the daily goal
-2. calories burned
-3. a way to log a new workout
-Cover every numbered item.
-```
-Plain-prose briefs also work; component names in the brief ("a table", "a chart") do not help.
 ## Intended use
 Generating UI screens for OpenUI-based applications where latency matters and a small,

 The LoRA adapter that produced these weights is in `adapter/` (PEFT format, base
 `google/diffusiongemma-26B-A4B-it`), for anyone who prefers to load base plus adapter.
 ## Intended use
 Generating UI screens for OpenUI-based applications where latency matters and a small,