Instructions to use swainsubhankar/STEMTOX-PaliGemma2-10B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use swainsubhankar/STEMTOX-PaliGemma2-10B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/paligemma2-10b-pt-224")
model = PeftModel.from_pretrained(base_model, "swainsubhankar/STEMTOX-PaliGemma2-10B")

Transformers

How to use swainsubhankar/STEMTOX-PaliGemma2-10B with Transformers:

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

pipe = pipeline("image-text-to-text", model="swainsubhankar/STEMTOX-PaliGemma2-10B")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("swainsubhankar/STEMTOX-PaliGemma2-10B", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use swainsubhankar/STEMTOX-PaliGemma2-10B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "swainsubhankar/STEMTOX-PaliGemma2-10B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "swainsubhankar/STEMTOX-PaliGemma2-10B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/swainsubhankar/STEMTOX-PaliGemma2-10B

SGLang

How to use swainsubhankar/STEMTOX-PaliGemma2-10B 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 "swainsubhankar/STEMTOX-PaliGemma2-10B" \
    --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": "swainsubhankar/STEMTOX-PaliGemma2-10B",
		"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 "swainsubhankar/STEMTOX-PaliGemma2-10B" \
        --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": "swainsubhankar/STEMTOX-PaliGemma2-10B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use swainsubhankar/STEMTOX-PaliGemma2-10B with Docker Model Runner:
```
docker model run hf.co/swainsubhankar/STEMTOX-PaliGemma2-10B
```

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STEMTOX: From Collaborative Tags to Fine-Grained Toxic Meme Detection via Entropy-Guided Multi-Task Learning

🎉 Accepted at Transactions of the Association for Computational Linguistics (TACL 2026).

STEMTOX Architecture

Citation

@misc{swain2026stemtoxsocialtagsfinegrained,
  title={STEMTOX: From Social Tags to Fine-Grained Toxic Meme Detection via Entropy-Guided Multi-Task Learning},
  author={Subhankar Swain and Naquee Rizwan and Vishwa Gangadhar S and Nayandeep Deb and Animesh Mukherjee},
  year={2026},
  eprint={2508.04166},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2508.04166}
}

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Model tree for swainsubhankar/STEMTOX-PaliGemma2-10B

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google/paligemma2-10b-pt-224

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Dataset used to train swainsubhankar/STEMTOX-PaliGemma2-10B

Paper for swainsubhankar/STEMTOX-PaliGemma2-10B

STEMTOX: From Social Tags to Fine-Grained Toxic Meme Detection via Entropy-Guided Multi-Task Learning

Paper • 2508.04166 • Published Mar 16 • 1

swainsubhankar
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STEMTOX-PaliGemma2-10B