Instructions to use darkps/darkit-v2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use darkps/darkit-v2.5 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="darkps/darkit-v2.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import darkit-v2.5
model = darkit-v2.5.from_pretrained("darkps/darkit-v2.5", dtype="auto")

llama-cpp-python

How to use darkps/darkit-v2.5 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="darkps/darkit-v2.5",
	filename="Q3_K_M.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 darkps/darkit-v2.5 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/darkit-v2.5:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf darkps/darkit-v2.5: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/darkit-v2.5:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf darkps/darkit-v2.5: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/darkit-v2.5:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf darkps/darkit-v2.5: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/darkit-v2.5:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf darkps/darkit-v2.5:Q4_K_M

Use Docker

docker model run hf.co/darkps/darkit-v2.5:Q4_K_M

LM Studio
Jan

vLLM

How to use darkps/darkit-v2.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "darkps/darkit-v2.5"
# 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/darkit-v2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/darkps/darkit-v2.5:Q4_K_M

SGLang

How to use darkps/darkit-v2.5 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "darkps/darkit-v2.5" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "darkps/darkit-v2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "darkps/darkit-v2.5" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "darkps/darkit-v2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use darkps/darkit-v2.5 with Ollama:
```
ollama run hf.co/darkps/darkit-v2.5:Q4_K_M
```

Unsloth Studio

How to use darkps/darkit-v2.5 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/darkit-v2.5 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/darkit-v2.5 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for darkps/darkit-v2.5 to start chatting

How to use darkps/darkit-v2.5 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf darkps/darkit-v2.5: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": "darkps/darkit-v2.5:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use darkps/darkit-v2.5 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/darkit-v2.5: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/darkit-v2.5:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use darkps/darkit-v2.5 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf darkps/darkit-v2.5: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/darkit-v2.5: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"

Docker Model Runner
How to use darkps/darkit-v2.5 with Docker Model Runner:
```
docker model run hf.co/darkps/darkit-v2.5:Q4_K_M
```

Lemonade

How to use darkps/darkit-v2.5 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull darkps/darkit-v2.5:Q4_K_M

Run and chat with the model

lemonade run user.darkit-v2.5-Q4_K_M

List all available models

lemonade list

DarkIT v2.5

DarkIT is a next-generation high-performance large language model engineered for advanced programming, deep reasoning, and natural human conversation.

DarkIT v2.5 introduces major improvements in:

Advanced code generation
Complex debugging & error analysis
Long-context reasoning
Multi-language programming support
Instruction following for difficult technical tasks
Architecture understanding & code refactoring
Stable conversational behavior
Fast and efficient local inference
Unrestricted responses with strong adaptability

What's New in v2.5

DarkIT v2.5 has been significantly upgraded with a massive programming-focused training phase.

Major Improvements

Trained on over 18 million high-quality programming conversations
Strongly improved coding intelligence and reasoning
Better understanding of software architecture and system design
More accurate debugging and bug fixing
Improved instruction consistency
Better long-response stability
Reduced hallucinations in programming tasks
Faster response generation quality under long prompts

Programming Capabilities

DarkIT v2.5 performs strongly across:

Python
C++
JavaScript / TypeScript
Java
Rust
Go
PHP
SQL
Bash / Shell scripting
HTML / CSS
AI & Machine Learning workflows

Key Specifications

Model Family: DarkIT Coder
Version: v2.5
Model Size: 15B Parameters
Context Length: 256k Tokens
Format: GGUF (optimized for efficient local deployment)
Inference Support: CPU / GPU
Primary Focus: Programming & Technical Reasoning

Performance Notes

Optimized for strong local inference performance
Excellent balance between speed and output quality
Stable long-context generation
Enhanced code completion consistency
Improved logical reasoning across technical tasks
Designed for developer workflows and advanced prompting

Recommended Usage

DarkIT v2.5 performs best when used for:

Software development
AI engineering tasks
Code generation
Debugging large projects
Technical explanations
Automation scripting
Long-context programming conversations
Local offline AI deployment

⚠️ Notes

Designed primarily for inference deployment
Output quality may vary depending on quantization level and hardware
Best performance is achieved using structured prompts
Large context usage may require substantial RAM/VRAM

About Dark

Dark is an independent developer focused on building efficient, powerful, and scalable language models for real-world applications, with a strong focus on programming intelligence and local AI deployment.

Website: https://dark.ps
Telegram: https://t.me/sii_3

Downloads last month: 687

GGUF

Model size

15B params

Architecture

qwen3

Hardware compatibility

3-bit

4-bit

8-bit

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