From 9c25e23773a2c62a62cab1107fd330d7c941ba0e Mon Sep 17 00:00:00 2001 From: Triex Date: Thu, 5 Jun 2025 04:08:59 +1000 Subject: [PATCH] docs: Improved README, add additional references --- README.md | 23 ++++++++++++++++++++++- 1 file changed, 22 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 97641f4..56544b8 100644 --- a/README.md +++ b/README.md @@ -30,7 +30,7 @@ Current LLM inference is dominated by Python/PyTorch, which introduces: - **Complex deployment** with heavy runtimes - **Platform lock-in** due to dependency complexity -## The Zig Advantage +## Why Zig? **Performance**: Zero-cost abstractions, compile-time optimization, direct hardware access **Simplicity**: Single static binary, no runtime dependencies, cross-compilation built-in @@ -180,6 +180,8 @@ This is an ambitious project that would benefit from expertise in: - **ML model implementation** - **Web server development** - **Performance optimization** +- **Hardware-software co-design** +- **Novel inference techniques** (Speculative decoding, quantization) ## Project Timeline @@ -188,12 +190,31 @@ This is an ambitious project that would benefit from expertise in: - Backend optimization and web API - Testing, benchmarking, deployment tools +## Key Questions + +**Q: Why not just optimize PyTorch?** +A: PyTorch's Python overhead and GC pauses are fundamental limitations. Zig offers zero-cost abstractions, superior error handling, and deterministic performance. + +**Q: How will this compare to llama.cpp?** +A: Similar performance goals, but with built-in web API, better memory management, and focus on DeepSeek V3's specific MoE architecture. + +**Q: What about ONNX/TensorRT/ZML etc?** +A: Those are inference runtimes, not development frameworks / LLM frameworks. This project enables rapid iteration and custom optimization for research. + +--- + +**Status**: 🎯 Seeking feedback & idea expansion +**Vision**: Foundation for advanced AI reasoning research + ## References - [DeepSeek V3 Paper](https://arxiv.org/abs/2412.19437) - Original model architecture - [Zig Language](https://ziglang.org/) - Language documentation - [Awesome Zig](https://github.com/C-BJ/awesome-zig) - Community resources - [Zig Patterns](https://github.com/SuperAuguste/zig-patterns) - Common idioms +- [ZML](https://github.com/zml/zml) - Zig Inference Stack +- [LLaMA.cpp](https://github.com/ggerganov/llama.cpp) - C++ Inference Engine +- [DeepZig Consciousness](https://github.com/Triex/DeepZig-Consciousness) - Research goal/end game ---