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

Libraries

How to use Orionfold/SecurityLLM-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Orionfold/SecurityLLM-GGUF",
	filename="model-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Orionfold/SecurityLLM-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Orionfold/SecurityLLM-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Orionfold/SecurityLLM-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Orionfold/SecurityLLM-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Orionfold/SecurityLLM-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Orionfold/SecurityLLM-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Orionfold/SecurityLLM-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Orionfold/SecurityLLM-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Orionfold/SecurityLLM-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Orionfold/SecurityLLM-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Orionfold/SecurityLLM-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Orionfold/SecurityLLM-GGUF:Q4_K_M

Ollama
How to use Orionfold/SecurityLLM-GGUF with Ollama:
```
ollama run hf.co/Orionfold/SecurityLLM-GGUF:Q4_K_M
```

Unsloth Studio

How to use Orionfold/SecurityLLM-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Orionfold/SecurityLLM-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Orionfold/SecurityLLM-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Orionfold/SecurityLLM-GGUF to start chatting

Docker Model Runner
How to use Orionfold/SecurityLLM-GGUF with Docker Model Runner:
```
docker model run hf.co/Orionfold/SecurityLLM-GGUF:Q4_K_M
```

Lemonade

How to use Orionfold/SecurityLLM-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Orionfold/SecurityLLM-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SecurityLLM-GGUF-Q4_K_M

List all available models

lemonade list

Orionfold commited on 24 days ago

Commit

0824439

verified ·

1 Parent(s): d456984

README: replace placeholder finance prompt with cyber-domain example (KDF MCQ matching CyberMetric shape + article)

Browse files

Files changed (1) hide show

README.md +9 -1

README.md CHANGED Viewed

@@ -66,7 +66,15 @@ llm = Llama(
     n_ctx=4096, n_gpu_layers=99, chat_format="zephyr",
 )
 out = llm.create_chat_completion(
-    messages=[{"role": "user", "content": "Explain working capital."}],
     temperature=0.0,
 )
 print(out["choices"][0]["message"]["content"])

     n_ctx=4096, n_gpu_layers=99, chat_format="zephyr",
 )
 out = llm.create_chat_completion(
+    messages=[
+        {"role": "user",
+         "content": "What is the primary purpose of a key-derivation function (KDF)?\n\n"
+                    "A) Generate public keys\n"
+                    "B) Authenticate digital signatures\n"
+                    "C) Encrypt data using a password\n"
+                    "D) Transform a secret into keys and Initialization Vectors\n\n"
+                    "Reply with only the single letter A, B, C, or D."}
+    ],
     temperature=0.0,
 )
 print(out["choices"][0]["message"]["content"])