R-KV: Optimize Reasoning Founders get efficient cache compression with R-KV, a tool for reasoning models, backed by 1.2k+ GitHub stars.
100% free ยท open source
R-KV provides efficient cache compression for reasoning models by reducing redundancy in key-value stores, optimizing performance and memory usage.
git clone https://github.com/Zefan-Cai/R-KV.git
โข When deploying large-scale reasoning models that require significant cache storageโข When optimizing model performance is crucial, and cache compression can provide a significant speed boostโข When memory usage needs to be minimized, such as in edge devices or low-resource environments1 Navigate to the R-KV directory: `cd R-KV` 2 Run the provided example script: `python examples/example.py` 3 Modify the `config.json` file to suit your specific use case, adjusting parameters such as cache size and compression ratio 4 Implement the R-KV cache compression algorithm in your own reasoning model using the `rkvcache` module 5 Evaluate the performance impact of R-KV on your model using the provided benchmarking tools Ready-to-paste prompt python examples/example.py --cache_size 1000 --compression_ratio 0.5 Heads up: Ensure you have the required Python version (>=3.8) and necessary dependencies installed, as specified in the README, to avoid compatibility issues
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Topics kvcache
llm
reasoning-models
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