optimization

How paying “higher” attention can drive ML cost savingsOnce more, Flex Attention offers a substantial performance boost, amounting to 2.19x in eager mode and a pair of.59x in compiled mode.Flex Attention LimitationsAlthough we've got...

A groundbreaking recent technique, developed by a team of researchers from Meta, UC Berkeley, and NYU, guarantees to reinforce how AI systems approach general tasks. Referred to as “Thought Preference Optimization” (TPO), this method...

Here’s a full guide on genetic algorithms, what they're, and how you can use themProceed reading on Towards Data Science »

Part 3: The algorithm under the hoodUp until now, this series has covered the fundamentals of linear programming. In this text, we're going to move from basic concepts into the main points under the...

There may be a joke that cracks me up:“Did that, before the clock was invented, people needed to actively roam around and ask people the time?”There may be obviously no need to clarify...

import torch import torch.nn.functional as F class DPOTrainer: def __init__(self, model, ref_model, beta=0.1, lr=1e-5): self.model = model self.ref_model =...

The search for efficiency and speed stays vital in software development. Every saved byte and optimized millisecond can significantly enhance user experience and operational efficiency. As artificial intelligence continues to advance, its ability to...

A less expensive alignment method performing in addition to DPOThere are actually many methods to align large language models (LLMs) with human preferences. Reinforcement learning with human feedback (RLHF) was one in all the...