The PR you would have opened yourself

Making transformers models available in mlx-lm using a Skill and test harness

We provide a Skill and a test harness to help port language models from transformers to mlx-lm, so they become (almost) instantly available the moment they are added to transformers. The Skill is designed to support contributors and reviewers as an aide, not an automation. We explain why we did it, how, and comment about how to meaningfully contribute to open source in the age of agents.

In 2026, code agents started to actually work. What used to be auto-completion at the side of your editor turned into a system that one-shots reasonable solutions from brief specifications. The generated code usually works out of the box, covers what you asked for, and makes reasonable assumptions about details you didn't specify. This is great. As Jensen Huang puts it, we've instantly gone from 30 million to one billion coders in the world. Creative minds are unleashed.

We built a Skill that mlx-lm contributors can use to port a model from transformers to MLX. Given a prompt like "convert the olmo_hybrid architecture to MLX", the Skill sets up a virtual environment to work on, discovers and downloads the relevant models from the Hub, reads the transformers modeling code, writes the MLX implementation, and runs a battery of tests. If results don't look right, it debugs and iterates, and does not declare success until it's satisfied.

For the contributor , the Skill of course handles all the scaffolding: finding model variants on the Hub, diffing their configs to spot parameters that vary across model variants, downloading checkpoints, setting up editable installs of both mlx-lm and transformers. But it also handles the more difficult modeling tasks. It pays attention to salient architecture details and verifies sensitive areas, like RoPE configurations, that may result in hard-to-find bugs. It detects when the config doesn't declare a dtype and infers it from the safetensors metadata header. It runs per-layer comparisons between transformers and MLX to pinpoint exactly where divergence occurs. These are the kinds of checks that only someone with porting experience would think to run.

The same logic applies to MLX: their maintainers care deeply about the code and read every PR carefully. We wanted to see whether agents could help contributors land high-quality model ports fast, and at the same time support reviewers in their work. Not only do we aspire to produce PRs that could have come from a careful human submission, but we also provide additional artifacts to increase the signal: generation examples, numerical comparisons, and a separate non-agentic test harness for reproducibility.

Take the transformers library as an example. It has hundreds of contributors, is used in thousands of projects, has been downloaded over a billion times. Suddenly, anyone with an agent can instruct it to find some open issue, fix it, and submit a PR. And that's exactly what's happening. Those people feel happy because they are contributing to a great library, but the sad reality is that, most of the time, they don't realize they are not.

Codebases like transformers care deeply about the code . It's cool to build projects where it doesn't matter what the code looks like, but transformers is not one of them. Being used by thousands of people, transformers is primarily built as a human-to-human communication method, through code. Model files read top to bottom, because we want practitioners to understand them without jumping through complex abstractions. This permeates throughout the library design and is the reason why, for example, we favor flat hierarchies.

Agents don't have that context . Because design decisions are not explicit, agents suggest refactors to "improve" the codebase by following "best practices", without realizing they are breaking implicit contracts between the library and its users. They are verbose, generalize too early, don't notice when a change affects other areas, introduce subtle bugs, break performance. They are also sycophantic, and accept any idea as good and follow it through diligently, including ones a maintainer would have pushed back early on with a terse comment.