| # Contributing to CogniXpert v1.0 |
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| Thanks for your interest in improving CogniXpert. Contributions of code, docs, evaluations, and safety improvements are welcome. |
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| ## Ways to Contribute |
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| - Report bugs or issues |
| - Improve documentation and examples |
| - Propose new evaluation scripts or prompts |
| - Contribute training or alignment recipes |
| - Optimize inference and memory usage |
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| ## Development Setup |
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| - Python 3.10+ |
| - `pip install -U transformers unsloth peft bitsandbytes` |
| - Optional GPU acceleration: CUDA 12.x with a recent NVIDIA driver |
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| ## Pull Request Guidelines |
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| - 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 |
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| ## Coding and Docs Style |
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| - 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 |
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| ## Safety and Scope |
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| - Do not claim medical capability; include help‑seeking guidance |
| - Avoid training data that identifies individuals |
| - Note limitations and potential biases in evaluations |
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| ## Issue Triage |
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| - `bug`: malfunction or incorrect behavior |
| - `docs`: documentation improvements |
| - `perf`: performance or memory optimization |
| - `safety`: alignment or guardrails |
| - `feature`: new recipes or capabilities |
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| ## Release and Weights |
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| - 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 |
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| ## License |
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| By contributing, you agree your contributions are licensed under AGPL‑3.0. |
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