Instructions to use OwenTanKL/paligemma_traffic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OwenTanKL/paligemma_traffic with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/paligemma2-3b-pt-224")
model = PeftModel.from_pretrained(base_model, "OwenTanKL/paligemma_traffic")

Transformers

How to use OwenTanKL/paligemma_traffic with Transformers:

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

pipe = pipeline("text-generation", model="OwenTanKL/paligemma_traffic")

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OwenTanKL/paligemma_traffic with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OwenTanKL/paligemma_traffic

SGLang

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

Docker Model Runner
How to use OwenTanKL/paligemma_traffic with Docker Model Runner:
```
docker model run hf.co/OwenTanKL/paligemma_traffic
```

paligemma_traffic

This model is a fine-tuned version of google/paligemma2-3b-pt-224 on a public Traffic Image Dataset from Roboflow.

Model description

More information needed

Intended uses & limitations

Use: To annotate images by identifying various traffic objects Limitations: Results are subject to evaluation.

Training and evaluation data

Roboflow.

Data is split on a 80/10/10 train-evaluation-test ratio.

Training procedure

Fine-tuned using LoRA for 1 epoch.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2
num_epochs: 1

Training results

Framework versions

PEFT 0.17.1
Transformers 4.57.1
Pytorch 2.9.0+cu128
Tokenizers 0.22.1

Downloads last month: 11

Model tree for OwenTanKL/paligemma_traffic

Base model

google/paligemma2-3b-pt-224

Adapter

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