ShiroOnigami23: TITAN-10 Intelligence Engine
This is a from-scratch implementation of a High-Performance Deep Learning Engine. It bypasses standard frameworks (PyTorch/TensorFlow) to use a custom Virtual Memory Manager (VMM) and a Tape-based Autograd system.
Key Features:
- Zero-Copy Autograd: Minimal memory overhead via pre-allocated VMM buffers.
- Flash-Attention-2: Tiled attention implementation for $O(N)$ memory efficiency.
- RoPE: Rotary Positional Embeddings for infinite sequence context.
- LoRA Adaptation: Intelligence contained in a ~120KB Low-Rank weight matrix.
Architecture
Designed to surpass the CMU 11-785 (Deep Learning) curriculum within a 10-day execution window.
Final Audit
- Effective Rank: 32 (Verified via SVD)
- Spectral Energy (First 4 Dims): 16.79%
- Precision: FP16 Safetensors
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