Instructions to use ethanolivertroy/HackIDLE-NIST-Coder-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ethanolivertroy/HackIDLE-NIST-Coder-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ethanolivertroy/HackIDLE-NIST-Coder-GGUF",
	filename="hackidle-nist-coder-f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ethanolivertroy/HackIDLE-NIST-Coder-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ethanolivertroy/HackIDLE-NIST-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ethanolivertroy/HackIDLE-NIST-Coder-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 ethanolivertroy/HackIDLE-NIST-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ethanolivertroy/HackIDLE-NIST-Coder-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 ethanolivertroy/HackIDLE-NIST-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ethanolivertroy/HackIDLE-NIST-Coder-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 ethanolivertroy/HackIDLE-NIST-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ethanolivertroy/HackIDLE-NIST-Coder-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ethanolivertroy/HackIDLE-NIST-Coder-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use ethanolivertroy/HackIDLE-NIST-Coder-GGUF with Ollama:
```
ollama run hf.co/ethanolivertroy/HackIDLE-NIST-Coder-GGUF:Q4_K_M
```

Unsloth Studio new

How to use ethanolivertroy/HackIDLE-NIST-Coder-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 ethanolivertroy/HackIDLE-NIST-Coder-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 ethanolivertroy/HackIDLE-NIST-Coder-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

Pi new

How to use ethanolivertroy/HackIDLE-NIST-Coder-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ethanolivertroy/HackIDLE-NIST-Coder-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ethanolivertroy/HackIDLE-NIST-Coder-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ethanolivertroy/HackIDLE-NIST-Coder-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ethanolivertroy/HackIDLE-NIST-Coder-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ethanolivertroy/HackIDLE-NIST-Coder-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use ethanolivertroy/HackIDLE-NIST-Coder-GGUF with Docker Model Runner:
```
docker model run hf.co/ethanolivertroy/HackIDLE-NIST-Coder-GGUF:Q4_K_M
```

Lemonade

How to use ethanolivertroy/HackIDLE-NIST-Coder-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ethanolivertroy/HackIDLE-NIST-Coder-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.HackIDLE-NIST-Coder-GGUF-Q4_K_M

List all available models

lemonade list

HackIDLE-NIST-Coder (GGUF)

This is the first GGUF build of HackIDLE-NIST-Coder, a NIST-focused local model built from Qwen2.5-Coder-7B-Instruct and fine-tuned on a NIST cybersecurity corpus.

This repo is kept for reproducibility. For new testing, start with the v1.1 GGUF build:

ethanolivertroy/HackIDLE-NIST-Coder-v1.1-GGUF

Use this model as a helper. Do not treat it as a source of truth for exact control names, RMF step lists, or reference-architecture component names without checking the source publication.

Training data

This first build used 523,706 examples from 568 NIST cybersecurity documents.

Training dataset:

ethanolivertroy/nist-cybersecurity-training

Current eval status

The dated smoke eval from April 22, 2026 was run against the Ollama latest tag, which matched the v1.1 line in the local install used for that check. I have not rerun that exact eval against this older GGUF build.

The v1.1 result matters for this older build too because it sets the right expectation for the model family: the model can stay in-domain while still missing exact NIST structure.

Be careful with:

exact control names
exact RMF step ordering
exact SP 800-207 component naming
source-level answers that need to be right on the first pass

Available quantizations

Quantization	Approx. size	Use case
F16	14 GB	Full precision reference build
Q8_0	7.5 GB	Higher quality local inference
Q5_K_M	5.1 GB	Balanced size and quality
Q4_K_M	4.4 GB	Small local default for most machines

Run with llama.cpp

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make

wget https://huggingface.co/ethanolivertroy/HackIDLE-NIST-Coder-GGUF/resolve/main/hackidle-nist-coder-q4_k_m.gguf

./llama-cli \
  -m hackidle-nist-coder-q4_k_m.gguf \
  -p "Which NIST docs would you start with for contractor remote access?" \
  -n 500

License

The base model is Qwen2.5-Coder-7B-Instruct, released under Apache 2.0. The NIST source publications used for the dataset are public domain U.S. government works. This model card uses Apache 2.0 for the model artifact and documents the NIST data source separately.

Downloads last month: 93

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for ethanolivertroy/HackIDLE-NIST-Coder-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

mlx-community/Qwen2.5-Coder-7B-Instruct-4bit

Quantized

(5)

this model