Instructions to use Yunhao-Feng/AdaGuard-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yunhao-Feng/AdaGuard-0.6B with Transformers:

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

pipe = pipeline("text-generation", model="Yunhao-Feng/AdaGuard-0.6B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Yunhao-Feng/AdaGuard-0.6B")
model = AutoModelForCausalLM.from_pretrained("Yunhao-Feng/AdaGuard-0.6B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Yunhao-Feng/AdaGuard-0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Yunhao-Feng/AdaGuard-0.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Yunhao-Feng/AdaGuard-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Yunhao-Feng/AdaGuard-0.6B

SGLang

How to use Yunhao-Feng/AdaGuard-0.6B 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 "Yunhao-Feng/AdaGuard-0.6B" \
    --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": "Yunhao-Feng/AdaGuard-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Yunhao-Feng/AdaGuard-0.6B" \
        --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": "Yunhao-Feng/AdaGuard-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Yunhao-Feng/AdaGuard-0.6B with Docker Model Runner:
```
docker model run hf.co/Yunhao-Feng/AdaGuard-0.6B
```

Add paper and project page links

by nielsr HF Staff - opened Sep 30

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+12

-12

nielsr

Sep 30

This PR adds direct links to the paper page (Hugging Face) and the project website (AgentGuard) in the model card, making it easier for readers to access the publication and additional resources. No metadata changes are required as the existing tags, library_name, license, and pipeline_tag are already correct.

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