BitNet (1-bit Transformer) Explained in 3 Minutes!

Описание: Transformers have hit a wall.

As we scale to trillions of parameters, the bottleneck is no longer just "intelligence", it’s the massive energy, memory, and compute cost of traditional 16-bit floating-point math.

Enter BitNet.

In this video, we explore why 1-bit training (specifically BitNet 1.58b) is a fundamental shift in how we build AI. Instead of compressing models after they are trained, BitNet introduces "Quantization-Aware Training," allowing models to reach state-of-the-art performance using only ternary weights (-1, 0, 1).

🔍 What We Cover:
The Scaling Problem: Why FP16/FP32 is becoming a hardware nightmare.
Post-Training vs. Training-Aware: Why most compression tricks fail at low bit-widths.
The BitLinear Layer: How BitNet replaces expensive matrix multiplication with simple addition.
Stability Secrets: The role of Latent Weights and Straight-Through Estimators (STE).
Hardware Efficiency: Why this leads to massive energy savings and faster inference.

📄 Referenced Papers:
BitNet: Scaling 1-bit Transformers for Large Language Models
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits (BitNet 1.58b)

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