Author Archives: Isaac Ellmen

Generating Haikus with Llama 3.2

At the recent OPIG retreat, I was tasked with writing the pub quiz. The quiz included five rounds, and it’s always fun to do a couple “how well do you know your group?” style rounds. Since I work with Transformers, I thought it would be fun to get AI to create Haiku summaries of OPIGlet research descriptions from the website.

AI isn’t as funny as it used to be, but it’s a lot easier to get it to write something coherent. There are also lots of knobs you can turn like temperature, top_p, and the details of the prompt. I decided to use Meta’s new Llama 3.2-3B-Instruct model which is publicly available on Hugging Face. I ran it locally using vllm, and instructed it to write a haiku for each member’s description using a short script which parses the html from the website.

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Architectural highlights of AlphaFold3

DeepMind and Isomophic Labs recently published the methods behind AlphaFold3, the sequel to the famous AlphaFold2. The involvement of Isomorphic Labs signifies a shift that Alphabet is getting serious about drug design. To this end, AlphaFold3 provides a substantial improvement in the field of complex prediction, a major piece in the computational drug design pipeline.

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3 approaches to linear-memory Transformers

Transformers are a very popular architecture for processing sequential data, notably text and (our interest) proteins. Transformers learn more complex patterns with larger models on more data, as demonstrated by models like GPT-4 and ESM-2. Transformers work by updating tokens according to an attention value computed as a weighted sum of all other tokens. In standard implentations this requires computing the product of a query and key matrix which requires O(N2d) computations and, problematically, O(N2) memory for a sequence of length N and an embedding size of d. To speed up Transformers, and to analyze longer sequences, several variants have been proposed which require only O(N) memory. Broadly, these can be divided into sparse methods, softmax-approximators, and memory-efficient Transformers.

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Understanding positional encoding in Transformers

Transformers are a very popular architecture in machine learning. While they were first introduced in natural language processing, they have been applied to many fields such as protein folding and design.
Transformers were first introduced in the excellent paper Attention is all you need by Vaswani et al. The paper describes the key elements, including multiheaded attention, and how they come together to create a sequence to sequence model for language translation. The key advance in Attention is all you need is the replacement of all recurrent layers with pure attention + fully connected blocks. Attention is very efficeint to compute and allows for fast comparisons over long distances within a sequence.
One issue, however, is that attention does not natively include a notion of position within a sequence. This means that all tokens could be scrambled and would produce the same result. To overcome this, one can explicitely add a positional encoding to each token. Ideally, such a positional encoding should reflect the relative distance between tokens when computing the query/key comparison such that closer tokens are attended to more than futher tokens. In Attention is all you need, Vaswani et al. propose the slightly mysterious sinusoidal positional encodings which are simply added to the token embeddings:

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