Instructions to use laicsiifes/paligemma-flickr30k_pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use laicsiifes/paligemma-flickr30k_pt with Transformers:

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

pipe = pipeline("image-text-to-text", model="laicsiifes/paligemma-flickr30k_pt")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("laicsiifes/paligemma-flickr30k_pt", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use laicsiifes/paligemma-flickr30k_pt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "laicsiifes/paligemma-flickr30k_pt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "laicsiifes/paligemma-flickr30k_pt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/laicsiifes/paligemma-flickr30k_pt

SGLang

How to use laicsiifes/paligemma-flickr30k_pt 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 "laicsiifes/paligemma-flickr30k_pt" \
    --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": "laicsiifes/paligemma-flickr30k_pt",
		"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 "laicsiifes/paligemma-flickr30k_pt" \
        --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": "laicsiifes/paligemma-flickr30k_pt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use laicsiifes/paligemma-flickr30k_pt with Docker Model Runner:
```
docker model run hf.co/laicsiifes/paligemma-flickr30k_pt
```

paligemma-flickr30k_pt / example.png

hilariooliveira

refine documentation (#2)

d050984 verified 9 months ago

History Blame Contribute Delete

444 kB

Xet Pointer Details

( Raw pointer file )

Xet hash:: c68e939af7e56d94611e7fa7c8e06b16a6c09849e7c71722e5f1ba1348a4114d
Size of remote file:: 444 kB
SHA256:: 873cc61723bc6a8b035f1e964149fda0a277209acf7e8f1b5d029156a74ec548

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