Transformers without Normalization
Paper
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2503.10622
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Published
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83
Jaward Sesay
Jaward
AI & ML interests
I like to train large deep neural nets too 🧠🤖💥 | First Paper (AutoAgents: A Framework for Automatic Agent Generation) Accepted @ IJCAI 2024 | Role Model Karpathy
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about 12 hours ago
Transformers without Normalization
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2 days ago
sesame/csm-1b
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3 days ago
This is the most exciting of this week’s release for me: Gemini Robotics - A SOTA generalist Vision-Language-Action model that brings intelligence to the physical world. It comes with a verifiable real-world knowledge Embodied Reasoning QA benchmark. Cool part is that the model can be specialized with fast adaptation to new tasks and have such adaptations transferred to new robot embodiment like humanoids. Looking forward to the model and data on hf, it’s about time I go full physical:)
Technical Report: https://storage.googleapis.com/deepmind-media/gemini-robotics/gemini_robotics_report.pdf
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Jaward's activity
upvoted
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about 12 hours ago
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This is the most exciting of this week’s release for me: Gemini Robotics - A SOTA generalist Vision-Language-Action model that brings intelligence to the physical world. It comes with a verifiable real-world knowledge Embodied Reasoning QA benchmark. Cool part is that the model can be specialized with fast adaptation to new tasks and have such adaptations transferred to new robot embodiment like humanoids. Looking forward to the model and data on hf, it’s about time I go full physical:)
Technical Report: https://storage.googleapis.com/deepmind-media/gemini-robotics/gemini_robotics_report.pdf
Technical Report: https://storage.googleapis.com/deepmind-media/gemini-robotics/gemini_robotics_report.pdf
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3 days ago
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5 days ago
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1958
Super Interesting Paper!
Proposes neural networks (CRNNs) that can learn to produce traveling waves in their hidden state in response to visual stimuli, thus enabling the transfer and integration of spatial information across neural connections. In other words they showed that neural networks have wave-like properties that blends and processes visual information over time, cool seeing a union of AI and physics in this way.
Paper: https://arxiv.org/pdf/2502.06034
Code: https://github.com/KempnerInstitute/traveling-waves-integrate
Proposes neural networks (CRNNs) that can learn to produce traveling waves in their hidden state in response to visual stimuli, thus enabling the transfer and integration of spatial information across neural connections. In other words they showed that neural networks have wave-like properties that blends and processes visual information over time, cool seeing a union of AI and physics in this way.
Paper: https://arxiv.org/pdf/2502.06034
Code: https://github.com/KempnerInstitute/traveling-waves-integrate
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update
7 days ago
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2544
Lightweight (nanoGPT) implementation of hybrid norm - an intuitive normalization method that combines the strength of both pre-norm (i.e QKV-norm in MHA) and post-norm in the feed-forward network.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/hybrid_normalization.ipynb
Code: https://github.com/Jaykef/ai-algorithms/blob/main/hybrid_normalization.ipynb
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15 days ago
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16 days ago
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made a few improvements on custom grpo trainer:
- added sequence similarity reward (seems to work)
- improved vllm support (5x inference speed)
- adjusted reward scores (this helped with format/accuracy)
- can now push to hf hub (already pushed mine lol: Jaward/smollm2_360m_grpo_gsm8k_reasoner)
Code: https://github.com/Jaykef/ai-algorithms/blob/main/smollm2_360M_135M_grpo_gsm8k.ipynb
- added sequence similarity reward (seems to work)
- improved vllm support (5x inference speed)
- adjusted reward scores (this helped with format/accuracy)
- can now push to hf hub (already pushed mine lol: Jaward/smollm2_360m_grpo_gsm8k_reasoner)
Code: https://github.com/Jaykef/ai-algorithms/blob/main/smollm2_360M_135M_grpo_gsm8k.ipynb
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25 days ago
bro if you had read the repo you would see that this implementation is for educational purpose, it's not done because it's easy. Not to mention unsloth is using trl's GRPO trainer which is super slow on cpu and does not scale for models under 500M params, I tried it both on cpu and gpu. This custom implementation cuts most of the heavy lifting allowing you to train and scale faster even on cpu, plus a bunch of custom configs with a simplified GRPO trainer in under 500 lines of code. There's a lot one can learn from it.
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update
27 days ago
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3871
Finally here it is: a faster, custom, scalable GRPO trainer for smaller models with < 500M params, can train on 8gb ram cpu, also supports gpu for sanity sake (includes support for vllm + flash attention). Using smolLM2-135M/360M-instructs as ref & base models. Experience your own “aha” moment 🐳 on 8gb ram.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/smollm2_360M_135M_grpo_gsm8k.ipynb
Code: https://github.com/Jaykef/ai-algorithms/blob/main/smollm2_360M_135M_grpo_gsm8k.ipynb
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about 1 month ago
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about 1 month ago
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ByteDance drops OmniHuman🔥
This is peak SOTA performance - flawless natural gestures with perfect lip sync and facial expressions. This is the second time they've released SOTA level talking-heads only this time with hands and body motion.
Project: https://omnihuman-lab.github.io/
This is peak SOTA performance - flawless natural gestures with perfect lip sync and facial expressions. This is the second time they've released SOTA level talking-heads only this time with hands and body motion.
Project: https://omnihuman-lab.github.io/
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