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

Libraries

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

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

pipe = pipeline("text-generation", model="darkps/darkit-v2.5-transformers")
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-transformers", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use darkps/darkit-v2.5-transformers 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-transformers"
# 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-transformers",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use darkps/darkit-v2.5-transformers 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-transformers" \
    --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-transformers",
		"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-transformers" \
        --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-transformers",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

DarkIT v2.5

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

DarkIT v2.5 is built as an open-source and extensible project, allowing developers to adapt, modify, fine-tune, and integrate it into a wide range of workflows and applications.

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
Adaptable open-source deployment

What's New in v2.5

DarkIT v2.5 has been significantly upgraded with a major 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
More suitable for modification, extension, and community development

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: Transformers / Open-source project
Inference Support: CPU / GPU
Primary Focus: Programming & Technical Reasoning

Open-Source Project Features

Built for open development and experimentation
Easy to adapt for custom use cases
Supports fine-tuning and project-based modification
Suitable for local deployment and integration
Designed with extensibility in mind
Works well as a base for developer-driven improvements
Encourages community contribution and iterative upgrades

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
Flexible enough to support open-source enhancement

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
Custom open-source experimentation
Fine-tuning and iterative model improvement

⚠️ Notes

Designed primarily for open deployment and development
Output quality may vary depending on hardware and configuration
Best performance is achieved using structured prompts
Large context usage may require substantial RAM/VRAM
Open-source setups may require additional integration depending on the target environment

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

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Safetensors

Model size

15B params

Tensor type

BF16

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