Instructions to use K1shan/Chitti with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use K1shan/Chitti with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="K1shan/Chitti")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("K1shan/Chitti", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use K1shan/Chitti with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "K1shan/Chitti" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "K1shan/Chitti", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/K1shan/Chitti
- SGLang
How to use K1shan/Chitti 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 "K1shan/Chitti" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "K1shan/Chitti", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "K1shan/Chitti" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "K1shan/Chitti", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use K1shan/Chitti with Docker Model Runner:
docker model run hf.co/K1shan/Chitti
π 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
- π Fine-tuned from Qwen2.5-Coder-7B-Instruct
- π‘ Built for secure software development
- π§ Optimized for professional coding workflows
- π Multilingual programming support
- π Benchmarked against leading open-source coding models
Benchmark Results
| Benchmark | Chitti | Reference | Result |
|---|---|---|---|
| HumanEval+ | 76.83% | DeepSeek-Coder-7B (73%) | β |
| MultiPL-E (Average) | 67.49% | Qwen2.5-Coder-7B (65%) | β |
| LiveCodeBench | 23.5% | Competitive Baseline | β |
Training Pipeline
Qwen2.5-Coder-7B-Instruct
β
βΌ
Abliteration
β
βΌ
LoRA Fine-tuning
β
βΌ
Model Merge
β
βΌ
Benchmark Evaluation
β
βΌ
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
π§ Email: kkishann4@gmail.com
πΌ LinkedIn: https://linkedin.com/in/kkishann
π 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.