OneCompression vs llm-pruning-collection

Common questions

What is the difference between OneCompression and llm-pruning-collection?

OneCompression: Python package for LLM compression. llm-pruning-collection: Collection of LLM pruning methods and training code for GPUs & TPUs.. See the comparison table for live GitHub stats and shared categories.

When should I choose OneCompression over llm-pruning-collection?

Choose OneCompression over llm-pruning-collection when License: OneCompression is MIT, llm-pruning-collection is Apache-2.0; Tags unique to OneCompression: compression, cuda, llm, mps; Also covers Inference & Serving, LLM Frameworks; Use OneCompression when you need to compress large language models and have access to a Linux system with an NVIDIA GPU, as it supports CUDA for quantization and vLLM serving.

When should I choose llm-pruning-collection over OneCompression?

Choose llm-pruning-collection over OneCompression when License: llm-pruning-collection is Apache-2.0, OneCompression is MIT; Pricing: The software is free and open-source, licensed under Apache-2.0, but users must provide their own hardware or use cloud services like Google TPU Research Cloud for computational resources.; Requirements: The repository includes pretraining and fine-tuning scripts for both GPU and TPU platforms.; A JAX-based environment is required to run the code in this repository.; Tags unique to llm-pruning-collection: jax, llm-evaluation, llm-training, pruning; Also covers Evaluation & Observability; When you are working on reducing the size or improving inference speed of large language models using various pruning techniques available in this collection.

When should I avoid OneCompression?

Avoid OneCompression if you are working on a macOS system and require vLLM serving, as it is only supported on Linux with an NVIDIA GPU. Do not use OneCompression if you are working with Python 3.13 and CUDA, as the first run with --extra vllm may take a long time due to the lack of pre-built xformers wheels for this combination. Do not use OneCompression if you need to work with older CUDA versions like cu118 or cu121 and require vLLM, as --extra vllm is only compatible with cu130.

When should I avoid llm-pruning-collection?

Avoid if your project requires a pruning method that is not included in the collection or if the current platform capabilities do not align with your hardware requirements. Not suitable for those who need tools to train models from scratch rather than focusing on model pruning and optimization techniques.

Is OneCompression or llm-pruning-collection more popular on GitHub?

OneCompression has more GitHub stars (426 vs 72). Stars measure visibility, not whether either tool fits your constraints.

Are OneCompression and llm-pruning-collection open source?

Yes - both are open-source projects on GitHub (OneCompression: MIT, llm-pruning-collection: Apache-2.0).

Where can I find alternatives to OneCompression or llm-pruning-collection?

GraphCanon lists graph-backed alternatives at OneCompression alternatives and llm-pruning-collection alternatives (OneCompression markdown twin, llm-pruning-collection markdown twin), ranked by typed relationship edges rather than popularity votes.

Is there a machine-readable version of this comparison?

Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, OneCompression or llm-pruning-collection?

OneCompression: Very active. llm-pruning-collection: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

Where are the full trust reports for OneCompression and llm-pruning-collection?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: OneCompression trust report; llm-pruning-collection trust report.