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
Notebooks
Google Colab
Kaggle
Local Apps

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 Jan 28, 2024

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Update README.md

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@@ -1,6 +1,11 @@
 ---
 library_name: transformers
 license: apache-2.0
 ---
@@ -31,6 +36,4 @@ Fully compatible with [LM Studio](https://lmstudio.ai). Search for “Zysec” a
 The training is funded by [AttackIO](https://www.attackio.app), the mobile app for Cyber Security professionals.
-Official GGUF version is hosted here - [ZySec-7B-v1-GGUF on HuggingFace](https://huggingface.co/AttackIO/ZySec-7B-v1-GGUF)

 ---
 library_name: transformers
 license: apache-2.0
+tags:
+- security
+- cybersecwithai
+- threat
+- llmsecurity
 ---
 The training is funded by [AttackIO](https://www.attackio.app), the mobile app for Cyber Security professionals.
+Official GGUF version is hosted here - [ZySec-7B-v1-GGUF on HuggingFace](https://huggingface.co/AttackIO/ZySec-7B-v1-GGUF)