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

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 new

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

SecurityLLM-GGUF / README.md

Orionfold

Add a ## Notebooks section (Builder / User, Open in Colab / Kaggle)

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metadata

license: apache-2.0
library_name: gguf
base_model: ZySec-AI/SecurityLLM
pipeline_tag: text-generation
model_creator: Orionfold LLC
language:
  - en
tags:
  - gguf
  - spark-tested
  - orionfold
  - base_model:ZySec-AI/SecurityLLM

SecurityLLM GGUF

GGUF quantizations of ZySec-AI/SecurityLLM, verified end-to-end on the NVIDIA DGX Spark (GB10, 128 GB unified memory).

Notebooks

Two runnable notebooks ship with this model — open either on a free cloud GPU:

Notebook	What it does	Open
Builder	Reproduce this model's build and DGX Spark benchmarks end-to-end with `fieldkit`.
User	Load the published model and call it from your own app in a few lines.

Spark-tested

Every Orionfold quant ships with a measurement quad on the NVIDIA DGX Spark (GB10, 128 GB unified memory): perplexity, sustained tok/s, thermal envelope, and CyberMetric (n=50, mcq_letter) accuracy. The numbers below are the actual run, not a wishlist.

Variant	Size	Perplexity (wikitext-2)	tok/s on Spark	CyberMetric (n=50, mcq_letter)
Q4_K_M	4.1 GB	7.400	47.7	40.0%
Q5_K_M	4.8 GB	7.314	40.0	38.0%
Q6_K	5.5 GB	7.313	35.0	36.0%
Q8_0	7.2 GB	7.307	30.3	36.0%
F16	13.5 GB	7.301	17.4	34.0%

Thermal envelope: sustained-load minutes before thermal throttle on a single GB10 = 5 min. Beyond this, expect tok/s degradation; the duty-cycle disclosure is per Orionfold's quant-card standard.

Variants

Variant	Recommended use
Q4_K_M	Best balance — fits comfortably in Spark unified memory at 70B; default pick.
Q5_K_M	Higher quality than Q4_K_M with modest size bump.
Q6_K	Near-lossless; recommended if memory headroom allows.
Q8_0	Effectively lossless; reach for this when quality matters more than throughput.
F16	Reference — no quantization. Use only for measurement / baseline.

How to run

Pull a variant:

huggingface-cli download Orionfold/SecurityLLM-GGUF model-Q5_K_M.gguf \
  --local-dir ./models/securityllm

Serve it via llama-server (OpenAI-compatible API):

llama-server -m ./models/securityllm/model-Q5_K_M.gguf \
  -c 4096 -ngl 99 -t 8 \
  --host 0.0.0.0 --port 8080

Or run in-process via llama-cpp-python:

from llama_cpp import Llama
llm = Llama(
    model_path="./models/securityllm/model-Q5_K_M.gguf",
    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"])

LM Studio and Ollama (via a Modelfile) load the GGUF directly with no additional setup.

Methods

Full methodology and Spark-side measurement protocol: Vertical-curator quants on Spark — SecurityLLM-GGUF + CyberMetric mini-eval.

Other Orionfold vertical curators

Same Spark-tested recipe across the curator-on-Spark series:

finance-chat-GGUF — AdaptLLM finance-chat (Llama-2-7B lineage) for FinanceBench-shaped queries
Saul-7B-Instruct-v1-GGUF — Equall Saul-7B legal-instruct for LegalBench-shaped queries
II-Medical-8B-GGUF — Qwen3-8B + DAPO reasoning for MedMCQA-shaped queries

Each card lists its own measurement quad; the headline numbers are recorded as the actual sweep ran, never pre-corrected.

Published by Orionfold LLC · orionfold.com · Methods documented at ainative.business/field-notes.

Want to know when the next Orionfold vertical curator drops? Join the launch list at orionfold.com.