Instructions to use google/paligemma2-10b-ft-docci-448 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/paligemma2-10b-ft-docci-448 with Transformers:

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

pipe = pipeline("image-text-to-text", model="google/paligemma2-10b-ft-docci-448")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("google/paligemma2-10b-ft-docci-448")
model = AutoModelForImageTextToText.from_pretrained("google/paligemma2-10b-ft-docci-448")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use google/paligemma2-10b-ft-docci-448 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/paligemma2-10b-ft-docci-448"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "google/paligemma2-10b-ft-docci-448",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/google/paligemma2-10b-ft-docci-448

SGLang

How to use google/paligemma2-10b-ft-docci-448 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 "google/paligemma2-10b-ft-docci-448" \
    --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": "google/paligemma2-10b-ft-docci-448",
		"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 "google/paligemma2-10b-ft-docci-448" \
        --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": "google/paligemma2-10b-ft-docci-448",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use google/paligemma2-10b-ft-docci-448 with Docker Model Runner:
```
docker model run hf.co/google/paligemma2-10b-ft-docci-448
```

This version of PaliGemma fails to generate the EOS token, the generation goes on to the max_length

by heydariAI - opened Mar 7, 2025

Discussion

heydariAI

Mar 7, 2025

Hello there, im using this version of PaliGemma and for some test cases, it doesnt generate the eos token so it goes until the generation reaches the max_length. im using the provided code script in the repository itself

GopiUppari

Google org Mar 10, 2025

Hi @heydariAI ,

<eos> and it's token id is 1.

The model did generate the EOS token, but it wasn't visible in the output because skip_special_tokens=True was used in processor.decode(). If you set skip_special_tokens=False, the EOS token will be included in the output. For more details, could you please refer to this gist file

Thank you.

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