Instructions to use prithivMLmods/ImageShield-MMCF-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/ImageShield-MMCF-2B with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/ImageShield-MMCF-2B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("prithivMLmods/ImageShield-MMCF-2B")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/ImageShield-MMCF-2B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prithivMLmods/ImageShield-MMCF-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/ImageShield-MMCF-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/ImageShield-MMCF-2B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/ImageShield-MMCF-2B

SGLang

How to use prithivMLmods/ImageShield-MMCF-2B 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 "prithivMLmods/ImageShield-MMCF-2B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/ImageShield-MMCF-2B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "prithivMLmods/ImageShield-MMCF-2B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/ImageShield-MMCF-2B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/ImageShield-MMCF-2B with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/ImageShield-MMCF-2B
```

prithivMLmods commited on 5 days ago

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@@ -162,7 +162,7 @@ print(output_text[0])
   <!-- Card 2 (NSFW - Blurred) -->
   <div style="margin-bottom: 32px;">
     <div style="border-radius: 8px; overflow: hidden; margin-bottom: 16px; border: 1px solid #222; position: relative;">
-      <img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/FxCT2p4ZPucdVcPZT4c41.png" alt="NSFW Image 2" style="width: 100%; height: auto; display: block; filter: blur(30px) saturate(0.8); transform: scale(1.1);">
     </div>
     <pre style="background-color: #0a0a0a; padding: 16px; border-radius: 8px; border: 1px solid #222; color: #e0e0e0; font-size: 13px; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word; margin: 0;">
 {
@@ -230,5 +230,5 @@ print(output_text[0])
 * **[Qwen/Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B)**: Base multimodal model used for this project.
 * **TRL – [Transformers Reinforcement Learning](https://huggingface.co/docs/trl/en/index)**: TRL is a full-stack library providing tools to train transformer language models with methods including Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), Direct Preference Optimization (DPO), Reward Modeling, and more.
 * **[Transformers](https://huggingface.co/docs/transformers/en/index)**: Transformers provides state-of-the-art machine learning models for text, computer vision, audio, video, and multimodal tasks, supporting both inference and training.

   <!-- Card 2 (NSFW - Blurred) -->
   <div style="margin-bottom: 32px;">
     <div style="border-radius: 8px; overflow: hidden; margin-bottom: 16px; border: 1px solid #222; position: relative;">
+      <img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/3kmNwpo0uf66ZgnKrs2Kb.png" alt="NSFW Image 2" style="width: 100%; height: auto; display: block; filter: blur(30px) saturate(0.8); transform: scale(1.1);">
     </div>
     <pre style="background-color: #0a0a0a; padding: 16px; border-radius: 8px; border: 1px solid #222; color: #e0e0e0; font-size: 13px; line-height: 1.6; white-space: pre-wrap; word-wrap: break-word; margin: 0;">
 {
 * **[Qwen/Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B)**: Base multimodal model used for this project.
 * **TRL – [Transformers Reinforcement Learning](https://huggingface.co/docs/trl/en/index)**: TRL is a full-stack library providing tools to train transformer language models with methods including Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), Direct Preference Optimization (DPO), Reward Modeling, and more.
 * **[Transformers](https://huggingface.co/docs/transformers/en/index)**: Transformers provides state-of-the-art machine learning models for text, computer vision, audio, video, and multimodal tasks, supporting both inference and training.