Goekdeniz-Guelmez

Goekdeniz-Guelmez / MLX-Benchmark

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The best benchmark for LLMs on Apple's MLX framework knowledge and coding tasks.

33
1
100% credibility
Found Apr 19, 2026 at 33 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A tool for evaluating AI language models on Apple's MLX framework through knowledge questions, code completion, and debugging tasks, with support for local and cloud models.

How It Works

1
🔍 Discover MLX Tester

You find a simple tool to check how well AI chatbots handle Apple's MLX framework questions and coding challenges.

2
📥 Set It Up Quickly

You add the testing tool to your Mac with one easy step, ready to go in seconds.

3
🧠 Pick Your AI

Choose a smart AI buddy – either one running locally on your Mac or a powerful one from online.

4
▶️ Start the Quiz

Launch the test, and it asks your AI a bunch of MLX knowledge, multiple-choice, coding, and bug-fixing questions.

5
📊 Watch Progress

Follow along live as it scores each answer right or wrong, building up overall accuracy.

6
📈 Get Breakdown Scores

See detailed results like percent correct for easy vs hard questions, coding vs quizzes, feeling the AI's strengths.

🎉 Create Charts & Tables

Turn results into pretty bar charts and neat tables to compare AIs and share your insights proudly.

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Star Growth

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AI-Generated Review

What is MLX-Benchmark?

MLX-Benchmark is a Python CLI tool that tests LLMs on Apple MLX framework knowledge and coding tasks using a bundled dataset of 441 questions across QA, multiple-choice, code completion, coding, and debug challenges. Developers run `mlx-bench --model llama3.2` to benchmark local Ollama models or cloud ones via OpenAI, Anthropic, Groq, or OpenRouter APIs, with results saved as JSON including per-question scores and aggregate stats by type, difficulty, and category. It supports filtering like `--types coding debug --difficulties hard` and exports LaTeX tables or PNG charts for reports.

Why is it gaining traction?

It fills a niche as an mlx benchmark llm tool tailored for Apple Silicon devs, unlike general best benchmark software that ignores MLX-specific APIs—letting you quickly compare models on CPU/GPU tasks without custom scripts. Features like multi-model configs via YAML, concurrent workers, and LLM-as-judge evaluation make runs fast and reliable, with polished outputs that beat basic benchmark tests on Reddit or best benchmark sites. Python API integration hooks those scripting custom evals.

Who should use this?

MLX developers validating LLM assistants for Apple Silicon apps, like building local inference tools or fine-tuning coders for mlx_nn tasks. Researchers ranking open models on MLX debug/coding, or teams iterating on best github copilot model alternatives for Mac workflows. Skip if you're not in the Apple MLX ecosystem.

Verdict

Solid early tool with excellent docs and MIT license, but 33 stars and 1.0% credibility score signal it's pre-mainstream—test on non-critical projects first. Worth adopting for MLX benchmark needs if local Ollama setups match your stack.

(198 words)

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