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What is macos-laguna-s2.1?

tanishq-dubey/macos-laguna-s2.1 — explained in plain English

Analysis updated 2026-05-18

146PythonAudience · developerComplexity · 3/5Setup · moderate

In one sentence

A community benchmark collection that tests compressed versions of Poolside's Laguna S 2.1 AI language model on Apple Silicon Macs, measuring speed, memory, and accuracy to help you pick the best version for your hardware.

Mindmap

mindmap
  root((repo))
    What it does
      Benchmarks model variants
      Tests on Apple Silicon
      Measures speed and memory
      Scores answer quality
    Tech stack
      Python
      Apple MLX framework
    Use cases
      Pick best model version
      Compare memory tradeoffs
      Verify model accuracy
    Audience
      Mac developers
      AI researchers
      Hobbyists

Code map

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What do people build with it?

USE CASE 1

Compare compressed versions of the Laguna S 2.1 model to find the best one for your Mac's memory and speed.

USE CASE 2

Verify that a model variant passes agentic coding tests before using it for development work.

USE CASE 3

Reproduce benchmark results on your own Apple Silicon Mac to see how the model performs locally.

USE CASE 4

Review the full results table to trade off memory usage against generation speed for your needs.

What is it built with?

PythonMLX

How does it compare?

tanishq-dubey/macos-laguna-s2.1awslabs/loom2dogsandanerd/clawrag
Stars146146147
LanguagePythonPythonPython
Setup difficultymoderatehardmoderate
Complexity3/55/54/5
Audiencedeveloperops devopsdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires an Apple Silicon Mac with significant unified memory and pinned versions of loader software to avoid runtime bugs.

No license information is provided in the explanation.

So what is it?

This repository is a community collection of benchmark scripts for running Poolside's Laguna S 2.1 language model on Apple Silicon Macs. Specifically, it tests different compressed versions, called quantizations, of the model to see how they perform on a M5 Max computer. The work was sponsored by DWS LLC. Quantization shrinks a model so it can run on limited hardware. There are many possible quantizations, and each one trades a bit of accuracy or speed for lower memory usage. The repository's harness measures a range of these versions and records statistics like generation speed, memory usage, and how well the model answers questions or writes code. The goal is to help people pick the best version for their own Mac. The results show that a 2-bit conversion from pipenetwork is the current leader. It passed all 38 assertions in the test suite and reached high speeds on a 128 GB Apple M5 Max. Other conversions from the mlx-community and unsloth groups also passed the agentic coding tests but scored slightly lower overall. The repository keeps a full table of every measured variant, so you can compare options based on your available memory and speed needs. The README is detailed and includes notes on specific runtime issues. For example, some earlier versions had bugs that caused the model to produce broken output. The harness uses specific pinned versions of the loader software to work around these issues. The included chart compares local Apple Silicon measurements against Poolside's published Terminal-Bench results. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1
Help me understand which Laguna S 2.1 quantization I should use on my M-series Mac with 64 GB of unified memory based on the benchmark results in this repo.
Prompt 2
Write a script similar to the harness in this repo that runs a model through a set of agentic coding assertions and records generation speed and memory usage.
Prompt 3
Explain why the 2-bit pipenetwork quantization outperforms the mlx-community and unsloth variants in this benchmark, and what tradeoffs each conversion makes.
Prompt 4
Set up this benchmark repository on my Apple Silicon Mac, pin the correct loader versions, and run the test suite against a local model file.

Frequently asked questions

What is macos-laguna-s2.1?

A community benchmark collection that tests compressed versions of Poolside's Laguna S 2.1 AI language model on Apple Silicon Macs, measuring speed, memory, and accuracy to help you pick the best version for your hardware.

What language is macos-laguna-s2.1 written in?

Mainly Python. The stack also includes Python, MLX.

What license does macos-laguna-s2.1 use?

No license information is provided in the explanation.

How hard is macos-laguna-s2.1 to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is macos-laguna-s2.1 for?

Mainly developer.

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