Text Generation
Transformers
Safetensors
multilingual
darkit-v2.5
open-source
programming
reasoning
fine-tuning
customizable
conversational
Instructions to use darkps/darkit-v2.5-transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| language: multilingual | |
| license: apache-2.0 | |
| author: Dark | |
| library_name: transformers | |
| tags: | |
| - darkit-v2.5 | |
| - open-source | |
| - text-generation | |
| - programming | |
| - reasoning | |
| - fine-tuning | |
| - customizable | |
| base_model: darkps/darkit-v2.5-transformers | |
| model_type: custom | |
| pipeline_tag: text-generation | |
| # 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 | |