πŸš€ Chitti

Enterprise coding assistant for secure software development and cybersecurity research.


Overview

Chitti is a fine-tuned 7B coding assistant designed for professional developers, security researchers, and software engineering teams.

Built upon Qwen2.5-Coder-7B-Instruct, Chitti combines abliteration and LoRA fine-tuning to improve coding performance while remaining practical for legitimate cybersecurity and software engineering workflows.

The complete training, evaluation, and deployment pipeline was developed independently using MoLab (Marimo).


Highlights

  • πŸš€ Fine-tuned from Qwen2.5-Coder-7B-Instruct
  • πŸ›‘ Built for secure software development
  • 🧠 Optimized for professional coding workflows
  • 🌍 Multilingual programming support
  • πŸ“ˆ Benchmarked against leading open-source coding models

Benchmark Results

Benchmark Chitti Reference Result
HumanEval+ 76.83% DeepSeek-Coder-7B (73%) βœ…
MultiPL-E (Average) 67.49% Qwen2.5-Coder-7B (65%) βœ…
LiveCodeBench 23.5% Competitive Baseline βœ…

Training Pipeline

Qwen2.5-Coder-7B-Instruct
            β”‚
            β–Ό
      Abliteration
            β”‚
            β–Ό
     LoRA Fine-tuning
            β”‚
            β–Ό
      Model Merge
            β”‚
            β–Ό
 Benchmark Evaluation
            β”‚
            β–Ό
         Chitti

Base Model

  • Qwen2.5-Coder-7B-Instruct

Fine-tuning

  • LoRA
  • Unsloth
  • TRL
  • PEFT

Parameters

7.62 Billion


Capabilities

  • Algorithmic problem solving
  • Code generation
  • Code explanation
  • Code refactoring
  • Debugging assistance
  • Software engineering support
  • Security-oriented programming workflows
  • Multilingual coding (Python, Java, C++, Go, Rust, C# and more)

Intended Use

Chitti is designed for:

  • Professional software development
  • Cybersecurity research
  • Secure coding assistance
  • Learning and education
  • Code review
  • Algorithm practice
  • Development productivity

Tech Stack

  • Python
  • PyTorch
  • Hugging Face Transformers
  • Unsloth
  • PEFT
  • TRL
  • LoRA

Hardware

Training and evaluation were performed on MoLab (Marimo) using NVIDIA RTX PRO 6000 Blackwell GPUs.


Availability

Chitti is not currently available for public distribution.

Enterprise deployments, licensing, and collaboration opportunities are available through direct engagement.


Contact

πŸ“§ Email: kkishann4@gmail.com

πŸ’Ό LinkedIn: https://linkedin.com/in/kkishann

πŸ™ GitHub: https://github.com/Ki1shan


License

Apache License 2.0


Author

Kishan N

Cybersecurity Engineer + AI Systems Builder


Building AI systems for secure software engineering and cybersecurity research.

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