ashhart / TensorFold

# TensorFold
TensorFold serves language models from a Zig binary on Apple Silicon and NVIDIA GPUs.
The engine reads checkpoints, tokenizes requests and runs Metal or CUDA kernels directly.
Serving needs no Python or MLX installation.
Building with a coding agent? Have it load the contributor skill before it writes any code.
## Quick start
Start with Qwen3.5-2B, the small 4-bit checkpoint checked on Metal and a GB10. The commands below download its
pinned revision, then serve it as `local-model`. Model weights have their own licenses.
### Mac with Apple Silicon
Install with Homebrew, then start the server:
 [code] 
### Linux with an NVIDIA GPU
Use the release archive for your CPU: `linux-aarch64` on a GB10, `linux-x86_64` on an x86_64 host.
It needs glibc 2.28 or newer and an NVIDIA driver providing `libcuda.so.1`. Keep the archive's directories together.
See the CUDA runbook for runtime libraries and supported GPUs.
 [code] 
### Send a request
Leave the server running. In another terminal, on either platform:
 [code] 
The API also serves completions, Responses and Anthropic Messages. `/v1/models` lists the served name.
On Windows with an NVIDIA GPU, follow the native Windows build guide.
Use `tensorfold --help`, `tensorfold capabilities --json` and `tensorfold serve REPO --help` to inspect your binary.
You can also serve a complete local checkpoint directory or an existing Hugging Face cache.
## Mac app
The TensorFold desktop workspace bundles the local engine, model downloads, hardware recommendations, live generation metrics, coding workers and document editing.
Download the Apple-silicon beta 8 DMG, or build from `app/`.
Mobile clients in `app/mobile/` remain preview software.
## Living Weights (experimental)
Living Weights teaches a running model new facts in its own weights. Start a Nemotron 3.5 Lightning server with
`--slide` and tell it something: each fact that passes the lesson's checks is written into the checkpoint folder's
files. It is experimental: related facts can disturb each other, recall drops past a few dozen facts and with thinking
on, and learned facts cost some decode speed on a Mac. Known issues
has the details.
 [code] 
Leave that server running and send a lesson from another terminal:
 [code] 
Send it a document instead and it finds the facts in it and learns them one by one. Open
`http://127.0.0.1:8080/slide` to watch in 3D: the facts, the neurons each one took in the model's layers, the shard
files that hold them, and the lesson running now.
Learning runs on Apple-silicon Macs and on an NVIDIA GB10 (DGX Spark), and a folder that learned on one serves on the
other. On an APFS volume, `cp -cRL` makes a clone that shares the original's data until lessons change it. It rewrites the folder's files:
before the first lesson the server keeps the originals in a hidden snapshot, and
`tensorfold slide rollback ~/models/nemotron-living` puts them back, undoing every lesson. The
Living Weights guide covers the key, teaching files an