Instructions to use darkps/ice-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use darkps/ice-AI with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf darkps/ice-AI:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf darkps/ice-AI:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf darkps/ice-AI:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf darkps/ice-AI: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 darkps/ice-AI:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf darkps/ice-AI: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 darkps/ice-AI:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf darkps/ice-AI:Q4_K_M

Use Docker

docker model run hf.co/darkps/ice-AI:Q4_K_M

LM Studio
Jan

vLLM

How to use darkps/ice-AI with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/darkps/ice-AI:Q4_K_M

Ollama
How to use darkps/ice-AI with Ollama:
```
ollama run hf.co/darkps/ice-AI:Q4_K_M
```

Unsloth Studio

How to use darkps/ice-AI 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 darkps/ice-AI 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 darkps/ice-AI to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for darkps/ice-AI to start chatting

How to use darkps/ice-AI with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf darkps/ice-AI:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "darkps/ice-AI:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use darkps/ice-AI with Docker Model Runner:
```
docker model run hf.co/darkps/ice-AI:Q4_K_M
```

Lemonade

How to use darkps/ice-AI with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull darkps/ice-AI:Q4_K_M

Run and chat with the model

lemonade run user.ice-AI-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use darkps/ice-AI with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf darkps/ice-AI: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 darkps/ice-AI:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use darkps/ice-AI with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf darkps/ice-AI:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "darkps/ice-AI:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

ICE AI

ICE 0001

The "ice" model is a very robust, medium-sized model for human-like conversations, designed for quick chats and small code snippets.

The ice model was released with massive equations:

Major Improvements

It was trained on 5.47 billion Codex conversations.
It was also trained on more than 30 billion human conversations to better adapt to different Arabic dialects and multiple languages.

Key Specifications

Model Family: ICE AI
ID: ice-0001
Model Size: 8B Parameters
Context Length: 32,768 tokens
Format: GGUF (optimized for efficient local deployment)
Inference Support: CPU / GPU
Primary Focus: Human-like conversational AI

Training

Trained on approximately 36 trillion tokens across 119 languages and dialects, with a strong focus on multiple Arabic dialects, international languages, and programming/code data.

Recommended Usage

ICE AI performs best when used for:

General conversations
multilingual chat
Software development
Code generation
Code debugging
Technical questions
Scripting and automation
Local offline AI deployment

⚠️ Notes

Designed for conversational and coding tasks.
Output quality may vary depending on the quantization level and hardware.
Best results are achieved with structured prompts.
Larger context sizes may require substantial RAM/VRAM.

About Dark

DarkPs is an AI organization owned by FanuonAI, developing and maintaining open-source AI models such as DarkIT, ICE AI, and DarkCoder.

Platform: https://dark.ps

Downloads last month: -

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

4-bit

8-bit