Instructions to use microsoft/kosmos-2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/kosmos-2.5 with Transformers:

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

pipe = pipeline("image-text-to-text", model="microsoft/kosmos-2.5")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoImageProcessor, AutoModelForMultimodalLM

processor = AutoImageProcessor.from_pretrained("microsoft/kosmos-2.5")
model = AutoModelForMultimodalLM.from_pretrained("microsoft/kosmos-2.5", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use microsoft/kosmos-2.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/kosmos-2.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/kosmos-2.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/microsoft/kosmos-2.5

SGLang

How to use microsoft/kosmos-2.5 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/kosmos-2.5" \
    --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/kosmos-2.5",
		"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/kosmos-2.5" \
        --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/kosmos-2.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use microsoft/kosmos-2.5 with Docker Model Runner:
```
docker model run hf.co/microsoft/kosmos-2.5
```

kosmos-2.5 / receipt_00008.png

estyle

Upload receipt_00008.png

2e33681 verified about 2 years ago

History Blame Contribute Delete

1.85 MB

Xet Pointer Details

( Raw pointer file )

Xet hash:: 1300fa843d9eb4d3e1e554a8fae6ff59c5d3eeb64c5fe6b9fefa458ddaf4bf39
Size of remote file:: 1.85 MB
SHA256:: 58716693a6c0733358e66e64d7064a5a845424280bb55f9b5fa137b1af8c92fe

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.