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Build high-performance AI models with modular building blocks
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Latest capture 2026-07-04 03:09
4 captures since 2026-05-27
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Scanned 2026-07-04 03:09
pyproject.toml
python ecosystem,
27 dependencies
requirements.txt
python ecosystem,
25 dependencies
docs/requirements.txt
python ecosystem,
29 dependencies
examples/cython_tests/setup.py
python ecosystem,
0 dependencies
examples/training/mo/requirements.txt
python ecosystem,
12 dependencies
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Any model. Any hardware. Zero compromise. Built with @ziglang / @openxla / MLIR / @bazelbuild
A deep learning framework built on an autograd engine with high level abstractions and low level control.
A unified library of SOTA model optimization techniques like quantization, distillation, pruning, neural architecture search, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.
Ongoing research training transformer models at scale
Complete API layer for private AI applications on local models: RAG, skills, tools, MCP, text-to-sql, and more. Works with any OpenAI-compatible inference server.
Fast, small, secure, local-first personal AI assistant infrastructure: one Rust binary for tools, memory, channels, providers, and sandboxed autonomy.
