⚡Pruna
Pruna is a model optimization framework built for developers, enabling you to deliver faster, more efficient models with minimal overhead.
⭐ Emerging: 1k stars, gaining about 5 a week
View on GitHub ↗repo profile
momentum
durability
bus factor = how many people it takes to cover more than half the commits (6 months). 1 is a solo project; higher means the work is spread across a team. top-author share is the single busiest author's slice of those commits.
since we covered it
why it's a big deal
- Gives Python developers one framework to shrink and speed up inference-heavy models across LLMs, diffusion and flow matching models, vision transformers, and speech recognition, instead of stitching together separate tools.
- Targets the real costs of running models in production; lower memory use, faster inference, and reduced compute and energy consumption.
- Includes an EvaluationAgent so teams can measure the speed and size gains from compression rather than guessing.
under the hood
- Written entirely in Python, requiring Python 3.9 or later, with optional CUDA support and coverage for Linux, MacOS, and Windows subject to per-algorithm restrictions.
- Centers on a smash() function that takes a model plus a SmashConfig object, letting developers combine techniques across ten categories including quantization, pruning, caching, distillation, compilation, and kernel optimization.
- Integrates with Hugging Face transformers and diffusers, with documented examples using Stable Diffusion pipelines.
Radar summary, generated from the project's public sources
star history
- PR#4 200 2025-03-19
- now 1k + 1k since first covered
curve is sampled from GitHub's star history, plus our own daily readings since we covered it; the dashed stretch is before we first covered it, the solid line since. figures at coverage are the numbers we printed then (approx.), current count is live.
understory
Output and attention are roughly in balance.
- output, commits & releases
- clout, star velocity
output = commits & releases; clout = star velocity, both 0 to 100 monthly indices; the gap where output runs above clout is the understory. The understory →
covered in
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Model optimization framework that helps developers deliver faster, more efficient models with minimal overhead
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