August Papers: Hallucinations, Quantisations and Test-Time Computations
If there’s one thing you can count on from Graphcore Research, it’s tireless enthusiasm for effective compute utilsation! Our favourite papers from August i...
Posts by Category
papers-of-the-month
July Papers: All About Scaling
Scaling continues to be a super hot topic of research and our selection of papers for this month all tackle different angles of how to scale models efficient...
June Papers: Mamba-2 & Matmul-free Models
Improving transformers is now not “just one area” of machine learning research. This is illustrated by the breadth of papers we got excited about this month,...
May Papers: xLSTM, Schedule-Free Optimizers, and Multi-token prediction
May is always an eventful time of year for ML researchers, with final ICML paper decisions and ICLR taking place in early May, and NeurIPS submission deadlin...
April Papers: TriForce, QuaRot & Mixture-of-Depths
For our April selection of AI research papers, there is a clear common thread: efficient LLM inference. But as it happens, ML researchers are showing there a...
March Papers: Low-Rank Galore & 1.58-Bit Weights
March was a fruitful month for AI research, with plenty of papers for us to choose from. A trend in the work we’ve selected is the pushing of previously publ...
February Papers: Longer RoPEs & Better Quantisation
Improving LLM inference is a key research topic at the moment, and something we’re particularly interested in at Graphcore because of its hardware implicatio...
January Papers: Great Teachers & Beyond Chinchilla
For the research community, 2023 was dominated by large transformers and the associated challenges with training, tuning and deploying them. This trend has c...
December Papers: FP8 Training & Simpler Transformers
The last month saw impressive developments in the space of efficient transformers and applied ML, from materials discovery to chip design.
posts
Speeding up LLM inference using SparQ Attention & llama.cpp
With the rapid advances in the capabilities of large language models (LLMs), there is an increasing need for efficient inference platforms that would enable ...
Scale-preserving nonlinearities for u-μP
My colleagues and I always get excited when, every once in a while, deep learning research throws up a fun little maths problem. Our recent work on u-μP does...
A transformer walk-through, with Gemma
Transformer-based LLMs seem mysterious, but they don’t need to. In this post, we’ll walk through a modern transformer LLM, Google’s Gemma, providing bare-bon...
Almost-scaled dot-product attention
TL;DR: Scaled dot product attention isn’t properly scaled, and that’s a good thing!