clip.cpp

Why we included this project

Running CLIP embeddings usually means pulling in PyTorch and a few hundred megabytes of dependencies, which is a non-starter on small devices. clip.cpp takes a different route: it's a plain C/C++ implementation on top of ggml that loads OpenAI and LAION CLIP models converted to GGUF, and its 4-bit quantization brings a model down to about 85 MB. That makes it a realistic choice for edge hardware, embedded systems, and serverless functions where cold starts and memory limits rule out heavyweight frameworks. The repo includes working examples for semantic image search and zero-shot labeling, plus a Python binding that only needs the standard library, so you can test the same pipeline in a lighter environment. If you're building image retrieval or multimodal encoders on constrained infrastructure, this gives you a direct path from model conversion to a running binary.