Jaisidh Singh @jaisidhsingh
machine teacher • i like to learn things. jaisidhsingh.bearblog.dev Joined August 2020-
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Thinky: we developed theory NVIDIA and everyone else: we just did it
"A problem just means something needs your attention. That's a good thing. All we have is time and attention. Give your attention, you'll be fine." ~ Jimmy Carr
DeepSpeed ZeRO - crisp and clear. jaisidhsingh.bearblog.dev/the-ultra-scal…
[paper release!] Did you know that you can - speed up any LLM by 4x - and reduce its memory footprint by 2x - and improve its results - without modifying the model at all How??? Here is how we do it 🧵
[paper release!] Did you know that you can - speed up any LLM by 4x - and reduce its memory footprint by 2x - and improve its results - without modifying the model at all How??? Here is how we do it 🧵 https://t.co/wFppbRcoe2
It’s hard to find conviction, but you deserve nothing less.
A very insightful paper, made me see attention differently. If you pre-mix tokens and treat (V·Wo) as an FFN expert, attention-MoE and FFN-MoE collapse into one design with shared experts. Sparse where it counts, lower PPL, similar compute. Elegant unification.
We beat Nvidia’s cuBLAS kernels on B200s in 170 LOC. Using zero CUDA. Just pure Mojo. Here’s exactly how we went from 1% to 106% of Nvidia benchmark perf from scratch (with code) 👇🧵
Took me 4 days to read this blog, but totally worth it. A great detailed guide to post training. Why aren't many people talking about it. Kudos to @Han_Fang_ @karthikabinav for sharing their thoughts. tokens-for-thoughts.notion.site/post-training-…
Procrastination is SO dangerous: one basically trades alpha in the future for comfort in the present. This is worse if you desire to be high output. Procrastinated once today and the coming 2 days will now have significantly more todos. Ugh.
1/ Introducing Isaac 0.1 — our first perceptive-language model. 2B params, open weights. Matches or beats models significantly larger on core perception. We are pushing the efficient frontier for physical AI. perceptron.inc/blog/introduci…
Just got the greenlight to share some work we did at Google DeepMind from over a year ago: We fine-tuned Gemini on thousands of the most toxic discussions on 4chan...and it just talked to us like a completely normal and nice language model. How? Our method, Generative Data…
Wild to see, I derived it on paper from scratch, and later realized it’s exactly Theorem 3.3 (p.5) in @elon_lit’s paper (arxiv.org/abs/2508.08369). Always amazing when independent thoughts converge :)
Wild to see, I derived it on paper from scratch, and later realized it’s exactly Theorem 3.3 (p.5) in @elon_lit’s paper (arxiv.org/abs/2508.08369). Always amazing when independent thoughts converge :) https://t.co/P3vgvbgbdZ
One of my most exciting results lately! We identify experts in MoE models for properties like safety and faithfulness, and steer them to improve/hurt model faithfulness and safety. Most shockingly, with stearMoE, we can jailbreak 100% safety guardrails for open models. Details 👇
One of my most exciting results lately! We identify experts in MoE models for properties like safety and faithfulness, and steer them to improve/hurt model faithfulness and safety. Most shockingly, with stearMoE, we can jailbreak 100% safety guardrails for open models. Details 👇
Sovereign model update 1: PT work [███████░░░] SFT work [█████░░░░░░] RL work [█░░░░░░░░░░░░] Ablations [███████░░░] Infra/code [█████████░]

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