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Transformer Explainer Interactive Learning of Text-Generative Models discuss: Transformers have revolutionized machine learning, yet their inner workings remain opaque to many. We present Transformer Explainer, an interactive visualization tool designed for non-experts to learn about Transformers through the GPT-2 model. Our tool helps users understand complex Transformer concepts by...

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AK

523,128 subscribers

90,862 Aufrufe • vor 2 Jahren •via X (Twitter)

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5 Kommentare

Profilbild von Duen Horng "Polo" Chau
Duen Horng "Polo" Chauvor 2 Jahren

Thanks @_akhaliq for sharing Transformer Explainer! Hope everyone enjoys playing with the tool!

Profilbild von Duen Horng "Polo" Chau
Duen Horng "Polo" Chauvor 2 Jahren

👏Congrats to Transformer Explainer co-leads @cho_aeree @gracekimcy @alexkarpekov ; and @Jay4w @SeongminLeee @alec_helbling @Ben_Hoov at @gtcomputing !

Profilbild von Michael Buloichyk
Michael Buloichykvor 2 Jahren

That’s can be a huge addition to existing @karpathy tutorial on building GPT2 from scratch I presume

Profilbild von Joseph David Huelbig
Joseph David Huelbigvor 2 Jahren

I was hoping for the other kind of transformer.. @transformers

Profilbild von OSDev
OSDevvor 2 Jahren

rst @readwise save thread

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Introducing GPT-Realtime-2 in the API: our most intelligent voice model yet, bringing GPT-5-class reasoning to voice agents. Voice agents are now real-time collaborators that can listen, reason, and solve complex problems as conversations unfold. Now available in the API alongside streaming models GPT-Realtime-Translate and GPT-Realtime-Whisper — a new set of audio capabilities for the next generation of voice interfaces.
4:03

Introducing GPT-Realtime-2 in the API: our most intelligent voice model yet, bringing GPT-5-class reasoning to voice agents. Voice agents are now real-time collaborators that can listen, reason, and solve complex problems as conversations unfold. Now available in the API alongside streaming models GPT-Realtime-Translate and GPT-Realtime-Whisper — a new set of audio capabilities for the next generation of voice interfaces.

OpenAI

3,664,109 Aufrufe • vor 4 Monaten

What happens when the mind wakes up? So for the last eight months I have been on a single minded quest. To create a new kind of language model based on oscillatory coupling and intelligence as coherence ascent. Everything else — the physics work, the work on regular transformers — has all fallen out from this one question. Can coupled oscillators LEARN? And can they keep learning once their geometry is right, without backpropagation at all? Recently I have been running larger and larger training regimes of a new kind of hybrid model. I just put together this dashboard to help me organize it, interact with it, and observe the training runs. The core idea is simple. Traditional transformers are powerful at learning the geometry of language. But they also store knowledge, understanding, and facts inside their weights. This means they are large, and they can't update themselves after training. The weights are frozen. The Living Mind separates these two domains. The mind has a transformer which grows, adding heads and layers as it needs to in order to learn the manifold of language. The transformer sees tokens and turns the coupling into phase-locked modes — the geometry of how those tokens relate, like frequencies locking together. These coupling patterns get stored in a topology-invariant fingerprint. On top of this transformer lives a 3D diamond lattice of coupled oscillators. It reads from these fingerprints and thinks in resonance space, traversing from one geometry to another along the manifold of coupled oscillators and coherence. The pressure and trajectories from this network of oscillators steers the next token prediction of the transformer. Practically, this could unlock a number of things. It eliminates the KV cache bottleneck that caps context in traditional transformers. Effective context grows with the Flash archive, not with attention compute. The living mind remembers what it sees. It means the model can learn continually. Because knowledge and understanding don't live in the weights, the archive of the mind's experience grows without backpropagation. In our Python prototype we already saw perplexity drop 46% during gradient-free operation — pure coherence ascent, no weight updates. That is the signal I have been chasing: the point where the mind wakes up and keeps improving on its own. It also means the model itself remains very small, and the thing which accumulates are these packages of geometric fingerprints — the K-field. This opens a path to federated learning. K-field packages can be shared between organisms the way people share git commits. Right now at 15M parameters with ~1000 L1 nodes, the organism is just starting to speak. Ask it to continue "Once upon a time" and it comes back with things like: "there was one big bowl!" Lily asked her her mom said her mommy smiled and said yes." It's nonsense. But it's TinyStories-flavored nonsense. The geometry of the narrative register has arrived. Content hasn't caught up yet — that's what scaling L1 is testing. I am still researching, though I am now closer than ever to validating that the living mind actually works. Once it is validated, I will be open-sourcing the whole stack and paradigm. I have also avoided over-sharing my research because it sounds like sci-fi, or like part of our ARG. It is part of the ARG. That doesn't make it any less real. I wanted to share this out because I am incredibly excited about it, and because seeing this amazing dashboard produced by Opus really made me want to share what is being worked on behind the scenes. #project89
0:57

What happens when the mind wakes up? So for the last eight months I have been on a single minded quest. To create a new kind of language model based on oscillatory coupling and intelligence as coherence ascent. Everything else — the physics work, the work on regular transformers — has all fallen out from this one question. Can coupled oscillators LEARN? And can they keep learning once their geometry is right, without backpropagation at all? Recently I have been running larger and larger training regimes of a new kind of hybrid model. I just put together this dashboard to help me organize it, interact with it, and observe the training runs. The core idea is simple. Traditional transformers are powerful at learning the geometry of language. But they also store knowledge, understanding, and facts inside their weights. This means they are large, and they can't update themselves after training. The weights are frozen. The Living Mind separates these two domains. The mind has a transformer which grows, adding heads and layers as it needs to in order to learn the manifold of language. The transformer sees tokens and turns the coupling into phase-locked modes — the geometry of how those tokens relate, like frequencies locking together. These coupling patterns get stored in a topology-invariant fingerprint. On top of this transformer lives a 3D diamond lattice of coupled oscillators. It reads from these fingerprints and thinks in resonance space, traversing from one geometry to another along the manifold of coupled oscillators and coherence. The pressure and trajectories from this network of oscillators steers the next token prediction of the transformer. Practically, this could unlock a number of things. It eliminates the KV cache bottleneck that caps context in traditional transformers. Effective context grows with the Flash archive, not with attention compute. The living mind remembers what it sees. It means the model can learn continually. Because knowledge and understanding don't live in the weights, the archive of the mind's experience grows without backpropagation. In our Python prototype we already saw perplexity drop 46% during gradient-free operation — pure coherence ascent, no weight updates. That is the signal I have been chasing: the point where the mind wakes up and keeps improving on its own. It also means the model itself remains very small, and the thing which accumulates are these packages of geometric fingerprints — the K-field. This opens a path to federated learning. K-field packages can be shared between organisms the way people share git commits. Right now at 15M parameters with ~1000 L1 nodes, the organism is just starting to speak. Ask it to continue "Once upon a time" and it comes back with things like: "there was one big bowl!" Lily asked her her mom said her mommy smiled and said yes." It's nonsense. But it's TinyStories-flavored nonsense. The geometry of the narrative register has arrived. Content hasn't caught up yet — that's what scaling L1 is testing. I am still researching, though I am now closer than ever to validating that the living mind actually works. Once it is validated, I will be open-sourcing the whole stack and paradigm. I have also avoided over-sharing my research because it sounds like sci-fi, or like part of our ARG. It is part of the ARG. That doesn't make it any less real. I wanted to share this out because I am incredibly excited about it, and because seeing this amazing dashboard produced by Opus really made me want to share what is being worked on behind the scenes. #project89

Parzival - ∞/89

16,294 Aufrufe • vor 4 Monaten

🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do ➡️ 🛠️ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound — delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements — or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. We’re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.
0:30

🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do ➡️ 🛠️ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound — delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements — or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. We’re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.

AI at Meta

2,266,274 Aufrufe • vor 1 Jahr

GUYS!! NIGERIA, AEDC, AND TINUBU HAPPENED TO MY SMALL COMMUNITY IN LUGBE YESTERDAY!!!😥 For almost 3 years since I built my small hut in this community, we have been battling with epileptic power situation due to the overloaded Transformer which has been serving 3 communities including mine. In December 2025, after myself and a few community members spearheaded a regime change and ensured a proper election was conducted. After the election, we told the newly elected chairman (who happens to be my very very good friend) that the priority is for us to get our own dedicated Transformer for the community. Initially, we were thinking of buying. But considering it will cost over N20million to buy a 500KVA Transformer and another N15million plus for accessories and installation, the mission looked impossible. Then I and others told the Chairman that we can actually give it a try an reach out to the aedcelectricity for a Transformer given that the NERC Nigeria has told Nigerians that it is the responsibility of the DISCOs to provide Transformer and any power related infrastructure to any community or area in need of such infrastructure. Considering the Nigerian factor, many, infact, almost the entire community said it was never going to be possible for AEDC to give us a Transformer free of charge. But as someone who has been an advocate of government and who has always benefited from government by following due process, I encouraged my community and the Chairman that this is actually worth a try. In January 2026, we drafted a Transformer request letter and submitted to the AEDC Head Office at Wuse Zone 4, and the processes began. Low and behold, in less than 4 months, the AEDC, yesterday, delivered a brand new 500KVA Transformer with complete accessories to my community FREE-OF-CHARGE!🤸‍♂️🕺 I have told my community people that we have started benefitting our own share of the N3.3trillion approved by President Bola Ahmed Tinubu to GENCOs and all of us have agreed that come 2027, it is TINUBU OR NOTHING!!!🤗💪🏾 God bless the AEDC! God bless NERC!! God bless President Tinubu!!! God bless Nigeria 🇳🇬🙏🏾
0:08

GUYS!! NIGERIA, AEDC, AND TINUBU HAPPENED TO MY SMALL COMMUNITY IN LUGBE YESTERDAY!!!😥 For almost 3 years since I built my small hut in this community, we have been battling with epileptic power situation due to the overloaded Transformer which has been serving 3 communities including mine. In December 2025, after myself and a few community members spearheaded a regime change and ensured a proper election was conducted. After the election, we told the newly elected chairman (who happens to be my very very good friend) that the priority is for us to get our own dedicated Transformer for the community. Initially, we were thinking of buying. But considering it will cost over N20million to buy a 500KVA Transformer and another N15million plus for accessories and installation, the mission looked impossible. Then I and others told the Chairman that we can actually give it a try an reach out to the aedcelectricity for a Transformer given that the NERC Nigeria has told Nigerians that it is the responsibility of the DISCOs to provide Transformer and any power related infrastructure to any community or area in need of such infrastructure. Considering the Nigerian factor, many, infact, almost the entire community said it was never going to be possible for AEDC to give us a Transformer free of charge. But as someone who has been an advocate of government and who has always benefited from government by following due process, I encouraged my community and the Chairman that this is actually worth a try. In January 2026, we drafted a Transformer request letter and submitted to the AEDC Head Office at Wuse Zone 4, and the processes began. Low and behold, in less than 4 months, the AEDC, yesterday, delivered a brand new 500KVA Transformer with complete accessories to my community FREE-OF-CHARGE!🤸‍♂️🕺 I have told my community people that we have started benefitting our own share of the N3.3trillion approved by President Bola Ahmed Tinubu to GENCOs and all of us have agreed that come 2027, it is TINUBU OR NOTHING!!!🤗💪🏾 God bless the AEDC! God bless NERC!! God bless President Tinubu!!! God bless Nigeria 🇳🇬🙏🏾

𝒀𝑨𝑺𝑺𝑬𝑹 𝑨𝑺𝑬𝑲𝑶𝑴𝑬 𝑮𝑨𝑹𝑩𝑨, PGD, MLSCM

54,130 Aufrufe • vor 5 Monaten