0xSero

0xSero / moe-compress

Public

Model-agnostic MoE compression automation: build calibration bundles, run REAP/quantization/benchmark/publish stages, and render auditable reports.

38
3
100% credibility
Found Mar 22, 2026 at 38 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A collection of automation tools that streamline shrinking large AI models by trimming unused sections, compressing data, measuring performance, and producing clear summary reports.

How It Works

1
🔍 Discover MoE Compress

You find a handy tool that helps shrink massive AI models to run faster while keeping their smarts.

2
📦 Gather your model and samples

You collect your big AI model files and some example conversations or texts to test with.

3
📋 Describe your plan

You jot down simple instructions on how much to trim, squeeze, test, and where to save the results.

4
🚀 Launch the compression magic

With one go, it builds a test set, trims extra parts, squeezes the data, checks speeds, and creates a full summary.

5
📊 Review your results

You get easy-to-read reports with charts showing sizes, speeds, and quality checks for each version.

🎉 Enjoy your speedy AI

Your smaller, faster model is ready to use or share, saving time and resources every day.

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AI-Generated Review

What is moe-compress?

This Python tool automates full MoE compression pipelines for LLMs, handling calibration bundle creation from local JSONL and Hugging Face datasets, REAP pruning, quantization, benchmarking, publishing to HF repos, and generating auditable reports in JSON, Markdown, and HTML. It solves the drudgery of stitching together vendor-specific commands for moe compression, letting you define everything in one JSON config and run via simple CLI like `uv run run_moe_pipeline.py --config pipeline.json`. Model-agnostic design means you plug in your exact observe/prune/quantize commands, while it orchestrates stages, captures logs, and expands variables like `{model_path}`.

Why is it gaining traction?

Its config-first minimalism stands out—no bloat, just pipeline orchestration that stops on failure and outputs normalized manifests for reports, making moe model compression reproducible without lock-in to one framework. Developers hook on the calibration builder's practical defaults (code/agentic mixes from evol-codealpaca, SWE-bench, etc.) and one-file drives for build/run/render. Auditable outputs track how compression error in experts impacts inference accuracy, bridging the gap in moe compressor tools.

Who should use this?

ML engineers pruning MoE models like Mixtral for deployment, teams running REAP/quantization on custom stacks needing benchmarks and HF uploads, or researchers automating moe compression experiments with calibration bundles. Ideal for those tired of manual scripting observations, variants like w4a16 quant, and piecing together reports.

Verdict

Grab it if you're deep in model-agnostic moe compression—solid docs and examples make it usable now, despite 38 stars and 1.0% credibility signaling early maturity. No tests visible, but its small scope lowers risk; fork and extend for production.

(198 words)

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