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Prakhar Maurya

@mphysicus.bsky.social

Physics student @ IISER Pune. Master Thesis student @ NCRA-TIFR. Astronomy • Astrophysics • Machine Learning Personal Website: mphysicus.github.io

65 Posts128 Follower324 Folgt
Sifa ID 🪿@sifa.id · last wk.

Show off your Github activity on Sifa 👩‍💻 ⬛🟩🟩🟩⬛🟩⬛🟩🟩🟩⬛🟩⬛⬛🟩⬛ ⬛🟩⬛⬛⬛🟩⬛🟩⬛⬛🟩⬛🟩⬛🟩⬛ ⬛⬛🟩⬛⬛🟩⬛🟩🟩⬛🟩🟩🟩⬛🟩⬛ ⬛⬛⬛🟩⬛🟩⬛🟩⬛⬛🟩⬛🟩⬛⬛⬛ ⬛🟩🟩🟩⬛🟩⬛🟩⬛⬛🟩⬛🟩⬛🟩⬛ We already showed your @tangled.org activity in your Sifa timeline, but now we also ingest and show Github activities for users that have verified their Github account.

Activity timelime from @gui.do showing an uncollapsed block or 3 merged PRs and a second block on the bottom with 17 merged PRs
Berkeley Lab@berkeleylab.lbl.gov · 4mo ago

“We’re going to celebrate completion of the original survey and then get started on the work of churning through the data, because we’re all curious about what new surprises are waiting for us.” - Michael Levi, DESI Survey director and Lab scientist ion-galaxies-48987

DESI Exceeds Expectations With Largest 3D Map of the Universe, Surveying More Than 47 Million Galaxies

Learn about the results of DESI, an instrument that has created the largest high-resolution 3D map of the universe in an effort to study dark energy.

discovermagazine.com

European Space Agency@esa.int · 4mo ago

🎉#Artemis II update: Integrity is back on Earth! At 01:07 BST/02:07 CEST, Orion and its crew splashed down in the Pacific Ocean. 🚀Our European Service Module propelled Orion over 1 million km through deep space, before burning up in Earth's atmosphere, its job complete 🔥 www.esa.int/Science_Expl...

NASA's Orion spacecraft and its crew splashed down safely in a deep blue Pacific Ocean, marking the successful end of the Artemis II mission.
Katie Mack@astrokatie.com · 4mo ago

More context on this #Artemis II image: * This is the night side, lit by moonlight. You can see city lights in Spain & Portugal, & a sliver of day at lower right * The Sun is entirely behind Earth, which makes it a kind of solar eclipse, but w/ Earth doing the eclipsing instead of the Moon: ☀️🌍🚀🌕

A full disc image of Earth, as seen from the Orion Crew Module. The planet is a pale blue, swirling with white clouds and glowing slightly lighter blue in place from reflected light. At lower left, a large brown landmass is Africa, with Spain and Portugal with twinkling lights where the planet curves. At top right, auroras glow in a thin green glow, just barely separated from the planet's surface. Earth is set against the black of space (pic: NASA/R.Wiseman)
ESA Exploration@exploration.esa.int · 4mo ago

What a lambtastic launch! WOOL DONE!🚀🐑 Wishing the #Artemis II astronauts safe travels around the Moon 🌙 Now Shaun’s switching from cheering… to mission monitoring 👀
Next stop: Mission Control at NASA’s Johnson Space Center in Houston to follow the flight up close!

Prakhar Maurya@mphysicus.bsky.social · 5mo ago

Been using @hf.co’s Accelerate library for a few months now, and I can’t emphasize enough how great it is. It makes launching distributed training effortless. If you’re into training deep learning models, definitely give it a try. GitHub repo: github.com/huggingface/... #ML #DistributedTraining

GitHub - huggingface/accelerate: 🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-conf...

🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed suppo...

github.com

Prakhar Maurya@mphysicus.bsky.social · 5mo ago

"Learning to Reason in 13 parameters" An interesting paper by Meta: arxiv.org/abs/2602.04118 Authors propose "TinyLoRA". They showed that Qwen 8B parameter model was able to achieve about 92% accuracy on certain maths datasets with just "13 trainable parameters (26 bytes)" for finetuning. #ML

Learning to Reason in 13 Parameters

Recent research has shown that language models can learn to \textit{reason}, often via reinforcement learning. Some work even trains low-rank parameterizations for reasoning, but conventional LoRA can...

arxiv.org

Prakhar Maurya@mphysicus.bsky.social · 7mo ago

Interesting piece by @quantamagazine.bsky.social on "Platonic Representation Hypothesis" (Neural networks, trained with different objectives on different data and modalities, are converging to as shared statistical model of reality in their representation spaces). #machinelearning

Quanta Magazine@quantamagazine.org · 7mo ago

Recently, a team of AI researchers found that different models can develop similar internal representations, even if they’re trained on entirely different data types. www.quantamagazine.org/distinct-ai-...