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

Libraries

How to use Ryukijano/ryukijano-paligemma-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="Ryukijano/ryukijano-paligemma-finetuned")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Ryukijano/ryukijano-paligemma-finetuned", device_map="auto")

PEFT
How to use Ryukijano/ryukijano-paligemma-finetuned with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ryukijano/ryukijano-paligemma-finetuned with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Ryukijano/ryukijano-paligemma-finetuned

SGLang

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

Docker Model Runner
How to use Ryukijano/ryukijano-paligemma-finetuned with Docker Model Runner:
```
docker model run hf.co/Ryukijano/ryukijano-paligemma-finetuned
```

Model Trained Using AutoTrain

This model was trained using AutoTrain. For more information, please visit AutoTrain.

Usage

# you will need to adjust code if you didnt use peft

from PIL import Image
from transformers import PaliGemmaForConditionalGeneration, PaliGemmaProcessor
import torch
import requests
from peft import PeftModel

base_model_id = BASE_MODEL_ID
peft_model_id = THIS_MODEL_ID
max_new_tokens = 100
text = "Whats on the flower?"
img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/bee.JPG?download=true"
image = Image.open(requests.get(img_url, stream=True).raw)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
base_model = PaliGemmaForConditionalGeneration.from_pretrained(base_model_id)
processor = PaliGemmaProcessor.from_pretrained(base_model_id)

model = PeftModel.from_pretrained(base_model, peft_model_id)
model.merge_and_unload()

model = model.eval().to(device)

inputs = processor(text=text, images=image, return_tensors="pt").to(device)
with torch.inference_mode():
    generated_ids = model.generate(
        **inputs,
        max_new_tokens=max_new_tokens,
        do_sample=False,
    )
result = processor.batch_decode(generated_ids, skip_special_tokens=True)
print(result)

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Model tree for Ryukijano/ryukijano-paligemma-finetuned

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google/paligemma-3b-pt-224

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Model Trained Using AutoTrain

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