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

ee6ee3d

verified ·

1 Parent(s): 804f58a

drop the diffusers pipeline files; the model is served through vLLM and transformers

Browse files

Files changed (2) hide show

model_index.json +0 -16
scheduler/scheduler_config.json +0 -9

model_index.json DELETED Viewed

@@ -1,16 +0,0 @@
-{
-  "_class_name": "DiffusionGemmaPipeline",
-  "_diffusers_version": "0.39.0.dev0",
-  "model": [
-    "transformers",
-    "DiffusionGemmaForBlockDiffusion"
-  ],
-  "processor": [
-    "transformers",
-    "Gemma4Processor"
-  ],
-  "scheduler": [
-    "diffusers",
-    "BlockRefinementScheduler"
-  ]
-}

scheduler/scheduler_config.json DELETED Viewed

@@ -1,9 +0,0 @@
-{
-  "_class_name": "BlockRefinementScheduler",
-  "_diffusers_version": "0.39.0.dev0",
-  "block_length": 32,
-  "editing_threshold": null,
-  "minimal_topk": 1,
-  "num_inference_steps": 32,
-  "threshold": 0.95
-}