Instructions to use darkai-1/darkit-v2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use darkai-1/darkit-v2.0 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="darkai-1/darkit-v2.0",
	filename="Q4_K_M.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 darkai-1/darkit-v2.0 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf darkai-1/darkit-v2.0:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf darkai-1/darkit-v2.0:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf darkai-1/darkit-v2.0:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf darkai-1/darkit-v2.0: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 darkai-1/darkit-v2.0:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf darkai-1/darkit-v2.0: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 darkai-1/darkit-v2.0:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf darkai-1/darkit-v2.0:Q4_K_M

Use Docker

docker model run hf.co/darkai-1/darkit-v2.0:Q4_K_M

LM Studio
Jan

vLLM

How to use darkai-1/darkit-v2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "darkai-1/darkit-v2.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "darkai-1/darkit-v2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/darkai-1/darkit-v2.0:Q4_K_M

Ollama
How to use darkai-1/darkit-v2.0 with Ollama:
```
ollama run hf.co/darkai-1/darkit-v2.0:Q4_K_M
```

Unsloth Studio new

How to use darkai-1/darkit-v2.0 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 darkai-1/darkit-v2.0 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 darkai-1/darkit-v2.0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for darkai-1/darkit-v2.0 to start chatting

Pi new

How to use darkai-1/darkit-v2.0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf darkai-1/darkit-v2.0: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": "darkai-1/darkit-v2.0:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use darkai-1/darkit-v2.0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf darkai-1/darkit-v2.0: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 darkai-1/darkit-v2.0:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use darkai-1/darkit-v2.0 with Docker Model Runner:
```
docker model run hf.co/darkai-1/darkit-v2.0:Q4_K_M
```

Lemonade

How to use darkai-1/darkit-v2.0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull darkai-1/darkit-v2.0:Q4_K_M

Run and chat with the model

lemonade run user.darkit-v2.0-Q4_K_M

List all available models

lemonade list

DarkIT v2.0

DarkIT is a high-performance large language model designed for advanced programming, reasoning, and natural conversation.

is optimized to deliver strong results in:

Code generation and debugging
Logical reasoning & problem solving
Instruction following in complex tasks
No request is refused

Key Specifications

Model Family: DarkIT Coder
Model Size: 15B parameters (optimized inference build)
Context Length: 256K tokens
Format: GGUF (quantized for efficient local deployment)
Target Use: Local AI inference (CPU & RAM / GPU)

Performance Notes

Optimized for speed and memory efficiency
Stable output generation across long prompts
Strong balance between creativity and correctness
Suitable for both chat and developer workflows

⚠️ Notes

Designed for inference-only deployment
Performance may vary depending on hardware and quantization level
Best results with structured prompts

DarkAI is an independent AI research initiative focused on building efficient, powerful, and scalable language models for real-world applications.

Company Website: DarkAI
Owner: DARK on Telegram

Downloads last month: 1,762

GGUF

Model size

15B params

Architecture

qwen3

Hardware compatibility

4-bit

5-bit

8-bit