Instructions to use gokaygokay/PaliGemma-PixelProse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gokaygokay/PaliGemma-PixelProse with Transformers:

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

pipe = pipeline("image-text-to-text", model="gokaygokay/PaliGemma-PixelProse")

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

processor = AutoProcessor.from_pretrained("gokaygokay/PaliGemma-PixelProse")
model = AutoModelForMultimodalLM.from_pretrained("gokaygokay/PaliGemma-PixelProse", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gokaygokay/PaliGemma-PixelProse with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/gokaygokay/PaliGemma-PixelProse

SGLang

How to use gokaygokay/PaliGemma-PixelProse 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 "gokaygokay/PaliGemma-PixelProse" \
    --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": "gokaygokay/PaliGemma-PixelProse",
		"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 "gokaygokay/PaliGemma-PixelProse" \
        --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": "gokaygokay/PaliGemma-PixelProse",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gokaygokay/PaliGemma-PixelProse with Docker Model Runner:
```
docker model run hf.co/gokaygokay/PaliGemma-PixelProse
```

Fine-tuned version of PaliGemma 224x224 on 7500 random examples from PixelProse dataset.

pip install git+https://github.com/huggingface/transformers

from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
from PIL import Image
import requests
import torch

model_id = "gokaygokay/PaliGemma-PixelProse"

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
image = Image.open(requests.get(url, stream=True).raw)

model = PaliGemmaForConditionalGeneration.from_pretrained(model_id).to('cuda').eval()
processor = AutoProcessor.from_pretrained(model_id)

## prefix
prompt = "caption en"
model_inputs = processor(text=prompt, images=image, return_tensors="pt").to('cuda')
input_len = model_inputs["input_ids"].shape[-1]

with torch.inference_mode():
    generation = model.generate(**model_inputs, repetition_penalty=1.05, max_new_tokens=512, do_sample=False)
    generation = generation[0][input_len:]
    decoded = processor.decode(generation, skip_special_tokens=True)
    print(decoded)

Citation and attribution

This model release is maintained by Gökay Aydoğan. If you reference this repository in academic work, please cite it as follows and also cite the upstream models, datasets, or projects it builds upon.

@software{aydogan2024paligemma_pixelprose,
  author = {Aydoğan, Gökay},
  title = {{PaliGemma-PixelProse}},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/gokaygokay/PaliGemma-PixelProse},
  note = {Model repository; cite the base model and upstream datasets as required.}
}

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Safetensors

Model size

3B params

Tensor type

F32

gokaygokay
/

PaliGemma-PixelProse

Citation and attribution

Dataset used to train gokaygokay/PaliGemma-PixelProse