mlx-vlm-skills

Plugin mlx-vlm-skills from marketplace mlx-vlm in Neywa-Labs/mlx-vlm. Commands read from the repo's manifest — today.

Skills (8)

• add-new-model

  Use this skill when the user wants to add or port a new model architecture to MLX-VLM — mapping a Hugging Face model_type to a new file under mlx_vlm/models, writing the ModelConfig, matching layer/weight names, reusing a similar existing model, adding a test class, and validating the port. Covers v

• benchmarking

  Use this skill when the user wants to benchmark an MLX-VLM change and present the numbers in a PR — fork-vs-main A/B comparisons, isolated-module micro-benchmarks, median-of-N timing with warmup, peak-memory reporting, correctness checks, parameter sweeps, and self-contained reproducible bench scrip

• cli-inference

  Use this skill when the user wants to run or debug MLX-VLM inference from the command line, including uv run mlx_vlm.generate, image/audio/video inputs, local model paths, Hugging Face model IDs, deterministic repro commands, and CLI errors around processors, prompts, model loading, or missing weigh

• contributing

  Use this skill when the user wants to contribute to MLX-VLM — opening a PR, where model code/config/tests go, backward-compatible config args, running the test suite, code formatting and the pre-commit hooks (black, clang-format), and PR expectations (tests, review, perf evidence). Use it to set up

• convert-quantize

  Use this skill when the user wants to convert a Hugging Face model to MLX or quantize/dequantize one with mlx_vlm.convert, including bits and group size, quant modes (affine, mxfp4, nvfp4, mxfp8), RTN vs AWQ, mixed-bit recipes, dtype casts, calibration (text or multimodal), local vs Hub paths, revis

• hf-cache-models

  Use this skill when the user wants to list, inspect, or report cached, custom, or loaded MLX-VLM models through the server's model discovery endpoint.

• reproducible-github-issues

  Use this skill when the user wants to create, improve, or triage a reproducible GitHub issue for MLX-VLM, including bug reports from CLI inference, server inference, model loading, processors, media inputs, dependency setup, crashes, wrong outputs, or performance regressions.

• server-inference

  Use this skill when the user wants to run or debug MLX-VLM server inference, including uv run mlx_vlm.server, /v1/models, /v1/chat/completions, /v1/responses, streaming, OpenAI-compatible clients, health checks, metrics, model unload/reload, adapters, trust-remote-code, and server request/response f

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