Instructions to use PerceptronAI/Isaac-0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PerceptronAI/Isaac-0.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="PerceptronAI/Isaac-0.1", trust_remote_code=True)
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("PerceptronAI/Isaac-0.1", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PerceptronAI/Isaac-0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PerceptronAI/Isaac-0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PerceptronAI/Isaac-0.1",
		"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/PerceptronAI/Isaac-0.1

SGLang

How to use PerceptronAI/Isaac-0.1 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 "PerceptronAI/Isaac-0.1" \
    --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": "PerceptronAI/Isaac-0.1",
		"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 "PerceptronAI/Isaac-0.1" \
        --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": "PerceptronAI/Isaac-0.1",
		"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 PerceptronAI/Isaac-0.1 with Docker Model Runner:
```
docker model run hf.co/PerceptronAI/Isaac-0.1
```

Isaac-0.1

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{
  "auto_map": {
    "AutoProcessor": "modular_isaac.IsaacProcessor"
  },
  "config": null,
  "image_processor": {
    "auto_map": {
      "AutoProcessor": "modular_isaac.IsaacProcessor",
      "AutoImageProcessor": "modular_isaac.IsaacImageProcessorFast"
    },
    "data_format": "channels_first",
    "disable_grouping": false,
    "do_center_crop": false,
    "do_convert_rgb": true,
    "do_normalize": true,
    "do_pad": false,
    "do_rescale": true,
    "do_resize": true,
    "image_mean": [
      0.5,
      0.5,
      0.5
    ],
    "image_processor_type": "IsaacImageProcessorFast",
    "image_std": [
      0.5,
      0.5,
      0.5
    ],
    "max_num_patches": 6144,
    "min_num_patches": 256,
    "patch_size": 16,
    "pixel_shuffle_scale": 2,
    "resample": 2,
    "rescale_factor": 0.00392156862745098
  },
  "max_sequence_length": 16384,
  "processor_class": "IsaacProcessor",
  "vision_token": "<image>"
}