Future-proof Dockerization

[cmlpolymath]

Hey everyone! I’m excited to share bitnet:slim, my new multi-stage Docker build for Microsoft’s BitNet inference. By carefully splitting the build and runtime stages, I’ve slashed the image from 15.7 GB down to 9.44 GB—hence the “slim” name! Inside you get everything you need to start testing immediately:

• A ready-to-use conda environment (bitnet-cpp) with Python 3.9
• All Python and C++ dependencies pre-installed (Torch, HuggingFace libraries, llama.cpp, etc.)
• The BitNet model weights already downloaded and configured at container launch
• Precompiled inference binaries and CMake-built code via setup_env.py

Plus, the CUDA 12.1 runtime is baked into the image so GPU acceleration is ready whenever Microsoft releases it. Just pull the image, run it with --gpus all, and you’ll be up and running in seconds. Head to my repo (https://github.com/cmlpolymath/container-store/blob/master/bit_net/Dockerfile) to see how I did it.

If you like what you see: pull my Dockerfile > docker build . > docker run -it bitnet:slim > and that's it!