Sreenath Vadlamudi

Artificial Intelligence Engineering student, Amrita Vishwa Vidyapeetham.

[Sreenath Vadlamudi @imVsreenath]

I just published How Large Concept Models Are Transforming Healthcare https://t.co/SpFDTfmtRz

[Sreenath Vadlamudi @imVsreenath]

There was no proper response from your end.

[Sreenath Vadlamudi @imVsreenath]

@ETSInsights
My GRE at-home test (ETS ID: 2SG76B66), scheduled on Feb 13, 2025, was terminated before the exam began.
The proctor asked me to disconnect and contact CSR. I’ve sent many emails and made several calls but got no proper response.
This is delaying my academic plans.

[Sreenath Vadlamudi @imVsreenath]

@ETSInsights I have been doing the same since Feb 14, 2025.

[Sreenath Vadlamudi @imVsreenath]

@ETSInsights Please resolve this.

[Sreenath Vadlamudi @imVsreenath]

This Entrepreneur Made $10M in 4 Years — With 10 Income Streams by Joseph Mavericks in @entrehandbook https://t.co/28W3UPchec

[Sreenath Vadlamudi @imVsreenath]

Hackathon Mode: "We developed a full-fledged application in just three days!"
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[Sreenath Vadlamudi @imVsreenath]

I just published Harnessing AI and Machine Learning for Precision Medicine in Tuberculosis

https://t.co/TRgxeRzxVr

[Sreenath Vadlamudi @imVsreenath]

“the idea that rather than feeling guilty for not being productive, I should give myself full permission to ‘write off’ the day.“

[Sreenath Vadlamudi @imVsreenath]

ChatGPT Prompt Hacks

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[Sreenath Vadlamudi @imVsreenath]

I just published Unlocking Authentic Connections Online: Introducing Eden — Build Circles Anywhere

https://t.co/4uxO9acItT

[Sreenath Vadlamudi @imVsreenath]

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via @CodePen

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[elvis @omarsar0]

xLSTM: Extended Long Short-Term Memory

Attempts to scale LSTMs to billions of parameters using the latest techniques from modern LLMs and mitigating common limitations of LSTMs.

To enable LSTMs the ability to revise storage decisions, they introduce exponential gating and a new memory mixing mechanism (termed sLSTM).

To enhance the storage capacities of LSTMs, they add a matrix memory and a covariance update rule (termed mLSTM).

Both the sLSTM and xLSTM cells stabilize their exponential gates using the same technique.

These extensions lead to xLSTM blocks that are residually stacked into the final xLSTM architecture.

Compared to Transformers, xLSTMs have a linear computation and constant memory complexity concerning the sequence length.

The xLSTM architecture is shown to be efficient at handling different aspects of long context problems.

Achieves better validation perplexities when compared to different model classes like Transformers, SSMs, and RNNs.

Then the xLSTM was used to train an LLM with 300B tokens from SlimPajama, with model sizes ranging from 125M to 1.3B parameters.

xLSTMs maintain low perplexities for longer contexts when compared to other methods. xLSTMs also broadly outperform all other models when evaluated on a set of downstream tasks.

Check the paper for more interesting insights and results such as assessing power-law scaling behavior.

Overall, my impression is that this enhanced xLSTM architecture has a lot of potential when they are scaled to billions of parameters. It shows training and performance stability when scaled which was a huge barrier. Looking forward to seeing more variants of this new LLM architecture.

[Sebastian Raschka @rasbt]

If you are looking for something to code & read this weekend, I uploaded a notebook to finetune a small GPT model to classify SPAM messages with ~96% accuracy: https://t.co/9SGciqJfqJ
(Fun fact: it's small enough to train it on your laptop; ~5 min on my M3 MacBook Air!)

[Sreenath Vadlamudi @imVsreenath]

https://t.co/mGEZQmjbvZ