Microsoft has introduced SAMBA, a hybrid model combining state space models with attention mechanisms for efficient long sequence modeling. SAMBA outperforms Phi-3-mini across benchmarks, offering extensive context length language modeling capabilities. SAMBA utilizes Mamba, sliding window attention, and MLP layers to achieve high performance on small scales.
Are the current generation of text diffusion models competitive with the autoregressive models? @jdeschena and I investigated this question empirically. The answer is it is getting there, but still, a lot of work needs to be done 🤖🦾 https://t.co/2p4epjqFPf
Discover Samba, Microsoft’s innovative model designed for efficient unlimited Context Language Modeling. #AI #Microsoft https://t.co/V13eihVmok
Dive into the world of #Samba, Microsoft’s innovative model revolutionizing Context Language Modeling with its unique hybrid architecture. Discover how it handles unlimited context! #AI #Microsoft #LanguageModeling https://t.co/HuBJnGh8Jj https://t.co/eGaNdd0WG7
On Friday let's Samba 💃🏾 with an Arxiv Dive into Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling. Come nerd out with us and review the learnings! @liliangren @nlpyang @WeizhuChen CC @UofIllinois @microsoft @MSFTResearch https://t.co/CnO0H2Jloq
Mamba + Sliding Window Attention = SAMBA with Efficient Unlimited Context 🔥 📌 Despite being pre-trained on 4K length sequences, SAMBA can be extrapolated to 1M length in zero-shot with improved perplexity on Proof-Pile while maintaining linear decoding time complexity and… https://t.co/y1u5z2rh1q
SAMBA - State space + Sliding Window Attention - Beats transformers on many tasks. From Microsoft China. https://t.co/NimtoKYq3w
Microsoft Researchers Introduce Samba 3.8B: A Simple Mamba+Sliding Window Attention Architecture that Outperforms Phi3-mini on Major Benchmarks https://t.co/DnCWDDnMR6 #SAMBA38B #LanguageModeling #MicrosoftResearchers #AI #NaturalLanguageProcessing #ai #news #llm #ml #researc… https://t.co/hqYBcsedNA
Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling Mamba, a selective State Space Model (SSM), with Sliding Window Attention (SWA) https://t.co/FB5ftV1zSh
Modeling long sequences has been tricky due to quadratic computation complexity and limited extrapolation ability of existing models. WELL, SAMBA is here to change the game, offering yet another mamba based solution for extensive context length language modeling! A… https://t.co/EcmEuKKqOS
The recent "Samba: Simple Hybrid State Space Models" by Microsoft look great! Basically a Mamba/transformer hybrid with MLP layers and sliding window attention. And it performs really well on a small <2B scale. Proud to see that that our LitGPT open-source library powered this! https://t.co/K7E1wygjeM
[CL] Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling L Ren, Y Liu, Y Lu, Y Shen… [Microsoft] (2024) https://t.co/rF595l5f5K - SAMBA combines selective SSM layers (Mamba) with sliding window attention (SWA) to efficiently model sequences… https://t.co/zBJMfNSnnK
🎉 Good folks at @Microsoft bring SAMBA! 🎉 What's Samba? 🤔 SAMBA is a hybrid model that integrates state space models (SSMs) with attention mechanisms to handle long sequence modeling efficiently. It combines these models through a layer-wise interleaving of components named… https://t.co/tPcefzVaJ5
CompSci Paper of the Day, Issue 39: Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling 1/4 🧵 https://t.co/AvJeJIxIEx
Is Samba a production ready non-transformer 3.8BN Large Language Model? Much of the takeoff in AI the past few years has been built on transformer and diffusion models. But a question is - how much of this progress is due to the capabilities of these architectures, relative to… https://t.co/T1OjeYT17n
Is that what we call Bingo? 🎯 "Samba = Mamba + MLP + Sliding Window Attention + MLP stacking at the layer level." => infinite context length with linear complexity Samba-3.8B-instruct outperforms Phi-3-mini across all benchmarks using the same dataset (trained on 3.2… https://t.co/at64EDAB5Y