FP16

Following up on a previous question

Okay so answer the question: what does FP16 mean? I’m referencing a tweet relating to Hyperbolic Labs offering Llama 3.1 70B for free with a FP16. Like that’s a big deal. And apparently not just the standard Llama 3.1 70B but the “reflection” model. More about that later.

I asked Claude, thusly:

what does FP16 mean in the context of an LLM?

and here's the answer:

FP16, or "Float Point 16," refers to a 16-bit floating-point numerical format used in the context of Large Language Models (LLMs) and other machine learning applications. Here's a brief explanation of its significance:

1. Precision: FP16 uses 16 bits to represent a number, offering less precision than the more common 32-bit (FP32) or 64-bit (FP64) formats

2. Memory efficiency: Using FP16 allows for storing model parameters and activations in half the memory compared to FP32, which is particularly beneficial for large models like LLMs.

3. Computational speed: Many modern GPUs have specialized hardware for FP16 operations, allowing for faster computations compared to higher precision formats.

4. Training and inference: FP16 is often used in a technique called "mixed precision training," where some operations are performed in FP16 while others use FP32. This balances speed and accuracy.

5. Model size reduction: Using FP16 can effectively reduce the size of an LLM, making it easier to deploy on devices with limited resources.

6. Trade-offs: While FP16 offers benefits in terms of speed and memory usage, it can potentially lead to some loss of accuracy due to its lower precision.

In the context of LLMs, FP16 is often used to optimize model performance, reduce memory requirements, and speed up inference times, especially on hardware designed to handle this format efficiently.

ok thanks Claude.

Is there a native setting for floating point operations? Do models get trained up in FP32 and then “boiled down” or throttled by less precision when it comes to their use in other settings? Interestingly, I asked Claude about that and it said it’d never heard of Llama 3.1 or even a Llama 3 since it was spun up in April 2024 — and apparently 3.1 was released that same month.

Various places on the internet say that models are typically trained up using 32-bit floating point numbers for “model weights and activations.” Then a process called “quantization” takes those weights and represents them with lower-precision numbers, sometimes even four bit numbers it seems. This reduces the memory and computational load so the models run more quickly and efficiently, and hopefully without a loss of quality or at least not a meaningful one. Life is always a trade-off.

Meta has an interesting discussion here of quantization for further reference. Too technical for me at this point.

Things I learned: a four bit integer is called a “nibble,” and represents a number from 0 to 15. Four bit processors were used in microwave ovens, calculators, and cash registers for decimal math. Bit < Nibble < Byte.