Instructions to use lablab-ai-amd-developer-hackathon/CyberSecQwen-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lablab-ai-amd-developer-hackathon/CyberSecQwen-4B with Transformers:

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

pipe = pipeline("text-generation", model="lablab-ai-amd-developer-hackathon/CyberSecQwen-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lablab-ai-amd-developer-hackathon/CyberSecQwen-4B")
model = AutoModelForCausalLM.from_pretrained("lablab-ai-amd-developer-hackathon/CyberSecQwen-4B")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use lablab-ai-amd-developer-hackathon/CyberSecQwen-4B with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use lablab-ai-amd-developer-hackathon/CyberSecQwen-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lablab-ai-amd-developer-hackathon/CyberSecQwen-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lablab-ai-amd-developer-hackathon/CyberSecQwen-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lablab-ai-amd-developer-hackathon/CyberSecQwen-4B

SGLang

How to use lablab-ai-amd-developer-hackathon/CyberSecQwen-4B 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 "lablab-ai-amd-developer-hackathon/CyberSecQwen-4B" \
    --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": "lablab-ai-amd-developer-hackathon/CyberSecQwen-4B",
		"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 "lablab-ai-amd-developer-hackathon/CyberSecQwen-4B" \
        --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": "lablab-ai-amd-developer-hackathon/CyberSecQwen-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lablab-ai-amd-developer-hackathon/CyberSecQwen-4B with Docker Model Runner:
```
docker model run hf.co/lablab-ai-amd-developer-hackathon/CyberSecQwen-4B
```

athena129 commited on 14 days ago

Commit

aa0deee

verified ·

1 Parent(s): c755f5d

Add lablab.ai hackathon submission attribution

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README.md +2 -0

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@@ -50,6 +50,8 @@ model-index:
 # CyberSecQwen-4B — Model Card
 ## Model Information
 CyberSecQwen-4B is a 4B-parameter language model specialized for defensive cybersecurity tasks, fine-tuned from [Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507). It is purpose-built for two evaluation skills measured by [CTI-Bench](https://github.com/xashru/cti-bench): mapping CVE descriptions to their CWE category (CTI-RCM) and answering cyber threat intelligence multiple-choice questions (CTI-MCQ).

 # CyberSecQwen-4B — Model Card
+> 🏆 **AMD Developer Hackathon submission.** Full project writeup, demo video, and judging context at **[lablab.ai/ai-hackathons/amd-developer/athena19/cybersecqwen-4b-cti-specialist-fine-tuned-on-amd](https://lablab.ai/ai-hackathons/amd-developer/athena19/cybersecqwen-4b-cti-specialist-fine-tuned-on-amd)**.
 ## Model Information
 CyberSecQwen-4B is a 4B-parameter language model specialized for defensive cybersecurity tasks, fine-tuned from [Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507). It is purpose-built for two evaluation skills measured by [CTI-Bench](https://github.com/xashru/cti-bench): mapping CVE descriptions to their CWE category (CTI-RCM) and answering cyber threat intelligence multiple-choice questions (CTI-MCQ).