Instructions to use ZySec-AI/SecurityLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZySec-AI/SecurityLLM with Transformers:

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

pipe = pipeline("text-generation", model="ZySec-AI/SecurityLLM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ZySec-AI/SecurityLLM")
model = AutoModelForCausalLM.from_pretrained("ZySec-AI/SecurityLLM")
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]:]))

Inference
Local Apps Settings

vLLM

How to use ZySec-AI/SecurityLLM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ZySec-AI/SecurityLLM

SGLang

How to use ZySec-AI/SecurityLLM 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 "ZySec-AI/SecurityLLM" \
    --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": "ZySec-AI/SecurityLLM",
		"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 "ZySec-AI/SecurityLLM" \
        --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": "ZySec-AI/SecurityLLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ZySec-AI/SecurityLLM with Docker Model Runner:
```
docker model run hf.co/ZySec-AI/SecurityLLM
```

venkycs commited on Feb 26, 2024

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@@ -38,7 +38,7 @@ Details of dataset distribution here - [Dataset Distribution](https://huggingfac
 Fully compatible with [LM Studio](https://lmstudio.ai). Search for “Zysec” and here is what you get. Here is a sample output of ZySec writing email to John about database security using LM Studio:
-<img src="https://huggingface.co/aihub-app/ZySec-7B-v1/resolve/main/sample-output.png?download=true" alt="Sample Output" width="90%"/>
 ---
@@ -47,7 +47,7 @@ The training is funded by [AttackIO](https://www.attackio.app), the mobile app f
 Official GGUF version is hosted here - [ZySec-7B-v1-GGUF on HuggingFace](https://huggingface.co/aihub-app/ZySec-7B-v1-GGUF)
-## [ZySec AI: Unleashing the Potential of the ZySec 7B Model](https://github.com/ZySec-AI/ZySec)
 Project ZySec, an integral part of ZySec AI, stands at the forefront of integrating Artificial Intelligence into Cybersecurity. Centered around the innovative ZySec 7B model, it's designed to revolutionize the cybersecurity landscape with AI-driven solutions. ZySec AI isn't just a tool, it's a transformative approach, blending AI's cutting-edge capabilities with the unique intricacies of cybersecurity, while ensuring privacy and security.

 Fully compatible with [LM Studio](https://lmstudio.ai). Search for “Zysec” and here is what you get. Here is a sample output of ZySec writing email to John about database security using LM Studio:
+<img src="https://huggingface.co/aihub-app/ZySec-8B-v2/resolve/main/sample-output-v2.jpg" alt="Sample Output" width="90%"/>
 ---
 Official GGUF version is hosted here - [ZySec-7B-v1-GGUF on HuggingFace](https://huggingface.co/aihub-app/ZySec-7B-v1-GGUF)
+## [ZySec AI: Unleashing the Potential of the ZySec Series Model](https://github.com/ZySec-AI/ZySec)
 Project ZySec, an integral part of ZySec AI, stands at the forefront of integrating Artificial Intelligence into Cybersecurity. Centered around the innovative ZySec 7B model, it's designed to revolutionize the cybersecurity landscape with AI-driven solutions. ZySec AI isn't just a tool, it's a transformative approach, blending AI's cutting-edge capabilities with the unique intricacies of cybersecurity, while ensuring privacy and security.