{"repo":"mindtro/semafold","free":true,"listed":false,"github":"https://github.com/mindtro/semafold","clone":"git clone https://github.com/mindtro/semafold.git","description":"Vector compression with TurboQuant codecs for embeddings, retrieval, and KV-cache. 10x compression, pure NumPy core — optional GPU acceleration via PyTorch (CUDA/MPS) or MLX (Metal).","language":"Python","stars":19,"topics":["embedding-compression","kv-cache","llm-inference","qjl","quantization","retrieval","turboquant","vector-compression","vector-database","semafold"],"license":"Apache-2.0","category":"databases-storage","readme_excerpt":"Semafold Vector compression with TurboQuant codecs for embeddings, retrieval, and KV-cache. 10x compression, pure NumPy core — no GPU required by default, but professionally accelerated on NVIDIA (CUDA) and Apple Silicon (Metal) when available. Semafold is a vector-first compression toolkit for AI workloads that compresses embeddings, retrieval representations, and cache-shaped KV tensors with explicit byte accounting, typed encode/decode contracts, and validation evidence. It is designed for teams building AI infrastructure that need measurable storage reduction without losing visibility into distortion, artifact size, or integration boundaries. Today it is strongest at two jobs: - compressing embedding / vector workloads - compressing cache-shaped K/V tensors with TurboQuant-based codecs It gives you: - typed encode/decode contracts - measured byte accounting - explicit guarantees and validation evidence - deterministic synthetic validation and benchmarks - pure NumPy core — no GPU required, runs anywhere - enterprise GPU acceleration — zero-config, automatic offloading to PyTorch (CUDA/MPS) or MLX (Apple Metal) when installed Compression Results Workload Baseline Setting Artifact Size Smaller Ratio --- ---: --- ---: ---: ---: Embedding 128 x 1536 float32 786,432 B TurboQuantMSE 3-bit 74,738 B 90.50% 10.52x Embedding 128 x 1536 fp16/bf16 393,216 B TurboQuantMSE 3-bit 74,738 B 80.99% 5.26x KV tensor (4,8,256,128) float32 8,388,608 B K=Prod 3b, V=MSE 3b 885,734 B 89.44% 9.47x","default_branch":null,"files":null,"tree":[],"storefront":"/r/mindtro","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/mindtro/semafold/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}