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
File size: 4,059 Bytes
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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
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