Instructions to use MSALab/PerceptionDLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MSALab/PerceptionDLM with Transformers:

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

pipe = pipeline("image-text-to-text", model="MSALab/PerceptionDLM", 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 AutoModel
model = AutoModel.from_pretrained("MSALab/PerceptionDLM", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MSALab/PerceptionDLM with vLLM:

Install from pip and serve model

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

SGLang

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

PerceptionDLM

File size: 819 Bytes

cadf670

{
  "chat_template": "{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|start_header_id|>system<|end_header_id|>\nYou are a helpful assistant.<|eot_id|>\n{% endif %}<|start_header_id|>{{ message['role'] }}<|end_header_id|>\n{% if message['role'] == 'assistant' %}{% generation %}{{ message['content'][0]['text'] }}<|eot_id|>{% endgeneration %}{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}<img><IMG_CONTEXT></img>{% elif content['type'] == 'video' or 'video' in content %}<video><VIDEO_CONTEXT></video>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|eot_id|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|start_header_id|>assistant<|end_header_id|>\n{% endif %}"
}