Defeating Nondeterminism in LLM Inference
Reproducibility is a bedrock of scientific progress. However, itβs remarkably difficult to get reproducible results out of large language models.
For example,...
Similar Articles (10 found)
π 63.8% similar
Why DeepSeek is cheap at scale but expensive to run locally
Why is DeepSeek-V3 supposedly fast and cheap to serve at scale, but too slow and expensive...
π 60.9% similar
Techniques for training large neural networks
Large neural networks are at the core of many recent advances in AI, but training them is a difficult en...
π 60.8% similar
First, thanks to the publisher and authors for making this freely available!
I retired recently after using neural networks since the 1980s. I still s...
π 58.8% similar
I'm curious why we seem convinced that this is a task that is possible or something worthy of investigation.
I've worked on language models since 2018...
π 58.7% similar
Writing an LLM from scratch, part 22 -- finally training our LLM!
This post wraps up my notes on chapter 5 of Sebastian Raschka's book "Build a Large ...
π 58.6% similar
> the generation of 281,128 augmented examples, from which 1,000 were
held out as a benchmark test set.
This model is trained on a custom dataset of 2...
π 57.2% similar
This article doesn't talk much about testing or getting training data. It seems like that part is key.
For code that you think you understand, it's be...
π 55.7% similar
The Bitter Lesson is Misunderstood
Together, the Bitter Lesson and Scaling Laws reveal that the god of Compute we worship is yoked to an even greater ...
π 55.4% similar
For some reason they focus on the inference, which is the computationally cheap part. If you're working on ML (as opposed to deploying someone else's ...
π 55.0% similar
Building with Humility
John Goddard | July 31st, 2025
How a product can get it right when machine learning gets it wrong
Introduction
Silicon Valley i...