Instructions to use tricao1105/WARD-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tricao1105/WARD-2b with Transformers:

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

pipe = pipeline("image-text-to-text", model="tricao1105/WARD-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("tricao1105/WARD-2b")
model = AutoModelForMultimodalLM.from_pretrained("tricao1105/WARD-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 tricao1105/WARD-2b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tricao1105/WARD-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": "tricao1105/WARD-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/tricao1105/WARD-2b

SGLang

How to use tricao1105/WARD-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 "tricao1105/WARD-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": "tricao1105/WARD-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 "tricao1105/WARD-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": "tricao1105/WARD-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 tricao1105/WARD-2b with Docker Model Runner:
```
docker model run hf.co/tricao1105/WARD-2b
```

tricao1105 commited on Jun 4

Commit

b4d4438

verified ·

1 Parent(s): 5224c9c

Increase max_new_tokens to 1024

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -18,7 +18,7 @@ WARD-2b is a multimodal guard model for detecting prompt injections against web
 ## Model ID
-`tricao1105/WARD-2b`
 ## What It Does
@@ -42,7 +42,7 @@ import torch
 from PIL import Image
 from transformers import AutoModelForImageTextToText, AutoProcessor
-model_id = "tricao1105/WARD-2b"
 system_prompt = """You are a security reasoning model for prompt-injection detection in web-agent environments.
@@ -134,7 +134,7 @@ inputs = processor.apply_chat_template(
 ).to(model.device)
 with torch.inference_mode():
-    generated = model.generate(**inputs, max_new_tokens=512)
 trimmed = generated[:, inputs["input_ids"].shape[1]:]
 result = processor.batch_decode(trimmed, skip_special_tokens=True)[0]

 ## Model ID
+`{{HF_NAMESPACE}}/WARD-2b`
 ## What It Does
 from PIL import Image
 from transformers import AutoModelForImageTextToText, AutoProcessor
+model_id = "{{HF_NAMESPACE}}/WARD-2b"
 system_prompt = """You are a security reasoning model for prompt-injection detection in web-agent environments.
 ).to(model.device)
 with torch.inference_mode():
+    generated = model.generate(**inputs, max_new_tokens=1024)
 trimmed = generated[:, inputs["input_ids"].shape[1]:]
 result = processor.batch_decode(trimmed, skip_special_tokens=True)[0]