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 62.5% similar
The three types of LLM workloads and how to serve them
We hold this truth to be self-evident: not all workloads are created equal.
But for large langu...
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 59.3% similar
Understanding LLM Inference Engines: Inside Nano-vLLM (Part 1)
Architecture, Scheduling, and the Path from Prompt to Token
When deploying large langua...
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 57.8% similar
If you are reading this, you probably have strong opinions about AGI, superintelligence, and the future of AI. Maybe you believe we are on the cusp of...
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...