FlashAttention and Persimmon-8B: Adept AI revolution

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The world of artificial intelligence, machine learning, and other emerging tech has surprised us lately. By now, you must have known about Adept AI and its groundbreaking capabilities of general intelligence through ACT-1, a transformer for actions. If not, check this article here. Today, we will take a step forward in their advancement and understand the new algorithm in the works – FlashAttention.

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FlashAttention Adept AI
Photo by Amr Taha™ on Unsplash

What is FlashAttention?

The team at Adept AI claims that FlashAttention Algorithm will speedup attention while reducing memory usage. Where, you ask? During the transformer training. To date, the grey area in the language model intelligence was training on long sequences. Even after the deepening and widening of transformers.

FlashAttention is pretty new, and a few companies and research teams are testing it to improve training speeds. Adept is cruising to make the algorithm better to help testers and onboard new organizations.

One of the major works in the backdrop is making FlashAttention fast enough for long sequences to train the language models with longer context. Let’s see how they progress.

Imagine if we could scale up the context length of transformers to train models. We could use this to understand books, high-resolution images, web pages, multi-turn user interactions, and long-form videos. Wouldn’t it be great? For now, this is a challenging area.

Though the FlashAttention algorithm reorders the attention computation and uses classical techniques such as tiling and recomputation to speed it up significantly, reducing memory usage from quadratic to linear in sequence length, it is not optimized for very long sequences. 

Here is the key advantage of FlashAttention. It can handle large data sets with ease, making it a valuable tool for data science and big data analytics. 

The algorithm uses a combination of standard attention and memory-efficient exact attention to delivering precise results. Even when training data is limited. 

FlashAttention is also highly adaptable. It makes it a valuable asset for automation, robotics, and other areas of computer science where real-time processing is critical. 

Research (GitHub) points out that FlashAttention yields the fastest BERT training on MLPerf cloud instances.

How FlashAttention works?

When large transformers train on long sequences with modern parallelism techniques such as data parallel, pipeline parallel, and tensor parallel, the batch size can get very small. So, the number of heads is approximately 8-12.

FlashAttention has been parallelized over the batch size and some heads. Furthermore, to make use of the multiprocessors on the GPU, it has now been parallelized over the sequence length dimension. 

Read more on Attention Parallelism and Forward Pass Computation here. Part-time research fellow Tri Dao explains this concept really well.

Join the Adept AI waitlist here to access the alpha release.

So, how does FlashAttention change Adept AI and language models?

FlashAttention will help machine learning (ML) models with long context to capture the history of user interactions. It makes them more personalized and effective. This advancement will enhance the idea of having a personal assistant who is intelligent. Also, has a shocking memory to help work out the tasks easily.

With ML models increasingly deployed and interacting with billions of users daily, the ability to remember past actions and user feedback is becoming crucial. It will change how we look at ML today. 

As ML models evolve to incorporate multiple modalities such as text, vision, and speech, long-context modeling will become even more important. It will enable models to comprehend complex media such as books, high-resolution images, and videos.

Furthermore, the team at FlashAttention is enthusiastic about this vision and welcomes input from individuals or organizations who believe their applications could benefit from these ideas. Connect with them on Twitter.

Adept AI open source Persimmon-8B

Adept has open-sourced Persimmon-8B, a highly adept language model with an Apache license.

This model has unique features, including a large context size, superior performance compared to other 8B models, and efficient inference code.

They evaluate model quality by having it generate text responses rather than using traditional probability-based metrics.

Persimmon-8B outperforms similar models in various tasks and has specific architecture modifications.

The release includes details about the model and a fast inference code, achieving high inference speed without a separate C++ codebase.

This release is the beginning of more to come from Adept. Read More about this open source on Adept’s official website.

AI tools to learn and master


Adept AI’s FlashAttention algorithm is poised to revolutionize how machine learning can help across many industries. 

With training data optimization for deep learning neural networks, FlashAttention aims to provide real-time results with remarkable speed and accuracy. 

Moreover, this breakthrough innovation is already drawing the attention of industry leaders like Google and OpenAI. Who knows, they may be eager to get hands with this technology. Let’s see how this turns out.

By 2025, it is clear that the internet will mostly rely on machine learning to manage and interpret big data. With its speed, accuracy, and adaptability, Adept AI’s FlashAttention algorithm will play a vital role in unlocking the full potential of this technology. 

With real-time results and optimized training data, FlashAttention will transform industries, data science, and computer science. 

Robotics, big data analytics, or any other application that requires fast and accurate data processing, FlashAttention may be the answer. It is a game-changer in world of deep learning.

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