L @CodeTitanium
Joined July 2023-
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LoRA makes fine-tuning more accessible, but it's unclear how it compares to full fine-tuning. We find that the performance often matches closely---more often than you might expect. In our latest Connectionism post, we share our experimental results and recommendations for LoRA.…
Initial thoughts looking at these scores: the model is effectively GPT-5 level (while being 40% more expensive) - Big Terminal Bench gains - Similar to GPT5, it has huge gains on TauBench - Telecom specifically. - SWEBench is starting to plateau
Initial thoughts looking at these scores: the model is effectively GPT-5 level (while being 40% more expensive) - Big Terminal Bench gains - Similar to GPT5, it has huge gains on TauBench - Telecom specifically. - SWEBench is starting to plateau https://t.co/SYEni2w3nf
Introducing Claude Sonnet 4.5—the best coding model in the world. It's the strongest model for building complex agents. It's the best model at using computers. And it shows substantial gains on tests of reasoning and math.
I was never fully convinced that there's any hard ceiling for self-attention in transformers if one carefully applies classical remedies like multi-stage retrieval. Great job, whale!
I was never fully convinced that there's any hard ceiling for self-attention in transformers if one carefully applies classical remedies like multi-stage retrieval. Great job, whale!
🚀 Introducing DeepSeek-V3.2-Exp — our latest experimental model! ✨ Built on V3.1-Terminus, it debuts DeepSeek Sparse Attention(DSA) for faster, more efficient training & inference on long context. 👉 Now live on App, Web, and API. 💰 API prices cut by 50%+! 1/n
Only incels yap about how ai is unproductive, while sigma males only benefit from it.
Only incels yap about how ai is unproductive, while sigma males only benefit from it. https://t.co/hoPLtUZAUY
Today, Canada recognises the State of Palestine.
I did not see that coming: Chain of Thought in AI Video generation! LumaAI introduced Ray 3 - HDR Generation - Fidelity "state of the art realism, physics, character consistency, and far superior instruction following" - SOTA Physics & Controls And much more. Some examples:
I did not see that coming: Chain of Thought in AI Video generation! LumaAI introduced Ray 3 - HDR Generation - Fidelity "state of the art realism, physics, character consistency, and far superior instruction following" - SOTA Physics & Controls And much more. Some examples: https://t.co/x4WDxhtSu1
🚀 Introducing Qwen3-Next-80B-A3B — the FUTURE of efficient LLMs is here! 🔹 80B params, but only 3B activated per token → 10x cheaper training, 10x faster inference than Qwen3-32B.(esp. @ 32K+ context!) 🔹Hybrid Architecture: Gated DeltaNet + Gated Attention → best of speed &…
I hope to receive pushback on today's claim.
Great to see this effort towards rigorous hyperparameter tuning. Two areas for improvement: 1. IIUC, the scaled up run here isn't actually tuned at all - its hparams are set via extrapolation 2. Sensitive hparams need a more granular sweep than power-of-2 x.com/percyliang/sta…
Great to see this effort towards rigorous hyperparameter tuning. Two areas for improvement: 1. IIUC, the scaled up run here isn't actually tuned at all - its hparams are set via extrapolation 2. Sensitive hparams need a more granular sweep than power-of-2 x.com/percyliang/sta…
Following up on my Newton–Schulz speedup post, here’s the code: github.com/thib-s/flash-n… (I'll do a PR soon in Dion/Muon) And here’s how I squeezed out the extra gain ⬇️
Following up on my Newton–Schulz speedup post, here’s the code: github.com/thib-s/flash-n… (I'll do a PR soon in Dion/Muon) And here’s how I squeezed out the extra gain ⬇️
μtransfer for Mamba2 & Muon
🔍 How do we teach an LLM to 𝘮𝘢𝘴𝘵𝘦𝘳 a body of knowledge? In new work with @AIatMeta, we propose Active Reading 📙: a way for models to teach themselves new things by self-studying their training data. Results: * 𝟔𝟔% on SimpleQA w/ an 8B model by studying the wikipedia…
Good news: I managed to get an extra 1.6x speedup of the Newton Schulz algorithm (which is at the core of Dion/Muon). It reaches nearly a 3x speedup over the plain torch implementation !
Google made a huge breakthrough in image editing with their new Gemini 2.5 Flash model 171 elo points ahead of the 2nd best model
Motif 2.6B tech report is pretty insane, first time i see a model with differential attention and polynorm trained at scale! > It's trained on 2.5T of token, with a "data mixture schedule" to continuously adjust the mixture over training. > They use WSD with a "Simple moving…
So we went from "LLM is memorizing dataset" to "LLM is not reasoning" to "LLM cannot do long / complex math proving" to "Math that LLM is doing is not REAL math. LLM can't do REAL math" Where do we go from now?
So we went from "LLM is memorizing dataset" to "LLM is not reasoning" to "LLM cannot do long / complex math proving" to "Math that LLM is doing is not REAL math. LLM can't do REAL math" Where do we go from now?
I've finally solved steepest descent on Finsler-structured (matrix) manifolds more generally. This generalizes work by me, @jxbz, and @Jianlin_S on Muon, Orthogonal Muon, & Stiefel Muon. --- The general solution turned out to be much simpler than I thought. And it should…
I've finally solved steepest descent on Finsler-structured (matrix) manifolds more generally. This generalizes work by me, @jxbz, and @Jianlin_S on Muon, Orthogonal Muon, & Stiefel Muon. --- The general solution turned out to be much simpler than I thought. And it should… https://t.co/NWwzMzmcHH
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