Instructions to use microsoft/udop-large-512-300k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/udop-large-512-300k with Transformers:

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

pipe = pipeline("image-text-to-text", model="microsoft/udop-large-512-300k")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("microsoft/udop-large-512-300k")
model = AutoModelForMultimodalLM.from_pretrained("microsoft/udop-large-512-300k")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use microsoft/udop-large-512-300k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/udop-large-512-300k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/udop-large-512-300k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/microsoft/udop-large-512-300k

SGLang

How to use microsoft/udop-large-512-300k 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 "microsoft/udop-large-512-300k" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/udop-large-512-300k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "microsoft/udop-large-512-300k" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/udop-large-512-300k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use microsoft/udop-large-512-300k with Docker Model Runner:
```
docker model run hf.co/microsoft/udop-large-512-300k
```

Training on data with custom tags

by HuggingWat - opened Apr 12, 2024

Discussion

HuggingWat

Apr 12, 2024

My goal is to use UDOP to identify the context of a certain type of image. For this I would imagine I need it to have examples of the image and said image tagged to train it to look for that image before using text extraction to understand the context of the text around it. How do I create the appropriate new tags for my data?

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