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Thanks for your interest in improving CogniXpert. Contributions of code, docs, evaluations, and safety improvements are welcome.
## Ways to Contribute
- Report bugs or issues
- Improve documentation and examples
- Propose new evaluation scripts or prompts
- Contribute training or alignment recipes
- Optimize inference and memory usage
## Development Setup
- Python 3.10+
- `pip install -U transformers unsloth peft bitsandbytes`
- Optional GPU acceleration: CUDA 12.x with a recent NVIDIA driver
## Pull Request Guidelines
- Fork the repo and create a topic branch
- Keep changes focused and incremental
- Update docs and examples when behavior changes
- Add usage notes for new configs or flags
- Ensure code is free of secrets or proprietary data
## Coding and Docs Style
- Prefer clear, simple Python samples
- Use `device_map="auto"` for examples unless reasoned otherwise
- Keep README snippets runnable
- Write concise commit messages in imperative mood
## Safety and Scope
- Do not claim medical capability; include help‑seeking guidance
- Avoid training data that identifies individuals
- Note limitations and potential biases in evaluations
## Issue Triage
- `bug`: malfunction or incorrect behavior
- `docs`: documentation improvements
- `perf`: performance or memory optimization
- `safety`: alignment or guardrails
- `feature`: new recipes or capabilities
## Release and Weights
- LoRA adapters should include `adapter_config.json` and weights files
- Reference base model IDs and versions used during training
- Document data sources and filtering where possible
## License
By contributing, you agree your contributions are licensed under AGPL‑3.0.
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