Pull requests (for contributors)

Test your changes:
- Using the commands in the tests folder. For instance, running the ./tests/test-backend-ops command tests different backend implementations of the GGML library
- Execute the full CI locally on your machine before publishing
Please rate the complexity of your PR (i.e. Review Complexity : Low, Review Complexity : Medium, Review Complexity : High). This makes it easier for maintainers to triage the PRs.
- The PR template has a series of review complexity checkboxes [ ] that you can mark as [X] for your convenience
Consider allowing write access to your branch for faster review
If your PR becomes stale, don't hesitate to ping the maintainers in the comments

AI usage

AI usage is not encouraged but it is tolerated. However, if you use AI to prepare PRs, please

Disclose it
PRs having Claude or similar as author will be rejected.
Minimize as much as possible the AI slop. Both, in the PR description and in the endless comments left behind in the code by the LLM. If in doubt, just remove all AI generated comments before submitting the PR. Comments can be added during the review process if needed and where appropriate.
Please do not submit PRs that you don't understand how they work and/or have never tested (or have not tested sufficiently)

Squash-merge PRs
Use the following format for the squashed commit title: <module> : <commit title> (#<issue_number>). For example: utils : fix typo in utils.py (#1234)

Avoid adding third-party dependencies, extra files, extra headers, etc.
Always consider cross-compatibility with other operating systems and architectures
Avoid fancy looking modern STL constructs, use basic for loops, avoid templates, keep it simple
There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit
Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, void * ptr, int & a
Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices
Matrix multiplication is unconventional: C = ggml_mul_mat(ctx, A, B) means C^T = A B^T \Leftrightarrow C = B A^T.