AI21 Labs has introduced Jamba, a hybrid language model combining Transformer and Mamba architectures with a mixture-of-experts component. Jamba aims to optimize general AI by outperforming similar models on a single GPU, offering improved performance and efficiency on long context tasks.
π€ From this week's issue: AI21 Labs announced Jamba, the worldβs first production-grade Mamba model with a 256k context window. https://t.co/VcncgKnFs7
Summarizing important arXiv papers. π πKey Insight: The ability of large language models to handle long-context tasks with a large number of labels varies widely and can be influenced by factors such as input length and instance positioning. Paper ID: 2404.02060 π§΅π https://t.co/XSG5gog8pF https://t.co/j2pU1CThdw
Releasing Jambert, my first official fine-tune of Jamba by @AI21Labs Still experimental, but on a specialized task where Mamba has the potential to shine: RAG synthesis of document (not so long for now, but this 256k context length window has potentialβ¦). https://t.co/S0xfJ6kjUR https://t.co/n5q6SPqvVv
Summarizing important arXiv papers. π πKey Insight: Training AI to recognize style in images is like teaching it to understand the unique signature of an artist, and those artists with a wider range of subjects leave a stronger signature for the AI to capture. π§΅π https://t.co/T9X7nO6S96
Summarizing important arXiv papers. π πKey Insight: Even complex machines can learn to tell what might persuade us, almost as well as a friend who knows us well. Paper ID: 2404.00750 π§΅π https://t.co/CTFoylrP92
Summarizing important arXiv papers. π πKey Insight: Teaching language models to remember where they learned something can make them more trustworthy and easier to understand. Paper ID: 2404.01019 π§΅π https://t.co/orhnNyWBx9 https://t.co/51t3ssdIXg
[CL] Jamba: A Hybrid Transformer-Mamba Language Model O Lieber, B Lenz, H Bata, G Cohen... [A21 Labs] (2024) https://t.co/6ru4lhWK2W - Presents Jamba, a new hybrid Transformer-Mamba language model with MoE, combining strengths of both architectures while addressing limitations.β¦ https://t.co/FH8bQzjDgS
Jamba: Hybrid Transformer-Mamba! π Paper: https://t.co/sJcbUnVb9Q Model: https://t.co/L5ErgNmkWb Posting it because the thumbnail turned out well π https://t.co/7WYzCcQIfQ
Jamba, a new language model, merges Transformer & Mamba architectures with MoE, boosting performance & efficiency on a single 80GB GPU: https://t.co/tlywNeI7EK https://t.co/KkwKV09vbD
The most important result in the Jamba paper is that: 1. It outperforms vanilla transformer of the same size. 2. No difference between 1:7 and 1:3 ratios of mamba layers to attention layers. Meaning: Given an architecture, you can replace ~84% of it's layers to mamba. Huge. https://t.co/bx2xeYhrMp https://t.co/jXASlnum3V
"The core of the Mamba model comes from the concept of State Space Models. State Space Models, like Transformers and RNN, process sequences of information, like text, audio signals, video frames, DNA sequences, etc." Read more from @vedantjumle1's post: https://t.co/2KhnBIHc5G
Last Week @AI21Labs released the production-scale Mamba implementation, and today, they released their paper. π§ Jamba introduces a new hybrid Transformer-Mamba mixture-of-experts architecture offering state-of-the-art performance but with significant improvements on longβ¦ https://t.co/9i5ORHIZmQ
Some light reading for the extended Easter weekend: official Jamba paper with lots of insights to scale hybrid Mamba/transformer models and highly promising results for long context memorization for a model that holds on one GPU. https://t.co/xb8t0NjVsq https://t.co/fnS0WIwPt2
Summarizing important arXiv papers. π πKey Insight: Combining the brains of high and low-res images makes computers better understand and create pictures and words together, without getting bogged down. Paper ID: 2403.18814 π§΅π https://t.co/a6ruzFYro7
Summarizing important arXiv papers. π πKey Insight: Using smaller language models, fine-tuned for specific tasks like reference resolution, can perform nearly as well as larger language models while being more efficient and adaptable to new entity types and use cases. π§΅π https://t.co/aeaEE6whCM
Summarizing important arXiv papers.π πKey Insight: Combining Transformer and Mamba layers in a hybrid architecture, supplemented with a MoE component, can result in a more efficient language model that performs well on long context lengths and standard benchmarks. π§΅π https://t.co/VkHY1y05oX
Summarizing important arXiv papers. π πKey Insight: A novel open-source LLM that improves across 6 dimensions and 30 benchmarks, long-context modeling, & open-ended subjective evaluations through better pre-training and optimization. π§΅π https://t.co/MX70ePUVIN
Daily AI News in 60 Seconds 1/8 AI21 Labs launches Jamba: AI21 Labs has launched Jamba, a hybrid Mamba-Transformer model that handles 256K tokens on a single GPU, outperforming similarly-sized models. https://t.co/bS5IFMwar2
Jamba A Hybrid Transformer-Mamba Language Model present Jamba, a new base large language model based on a novel hybrid Transformer-Mamba mixture-of-experts (MoE) architecture. Specifically, Jamba interleaves blocks of Transformer and Mamba layers, enjoying the benefits of https://t.co/cSi7tZezSG
Research Summary: βJamba: A Hybrid Transformer-Mamba Language Modelβ Source: https://t.co/z7zZAIZNlZ https://t.co/UR6Y4NiLeM
Summary of the paper βJamba: A Hybrid Transformer-Mamba Language Modelβ https://t.co/tlXRcgM3wh
Summarizing important arXiv papers. π πKey Insight: Transformers excel because they can "think" about which neural network to use for each piece of information they process. Ref: https://t.co/2zrWqm2aoo Paper ID: 2403.18415 π§΅π https://t.co/MdnynxysGO
Summarizing important arXiv papers. π πKey Insight: "Sparse expert networks can do more with less, by smartly choosing which parts of the network to use for each task." Ref: https://t.co/JqFUY6miE8 Paper ID: 2202.08906 π§΅π https://t.co/3g4PNTM6Ss
Summarizing important arXiv papers. π πKey Insight: Making language models smarter, not just bigger, by teaching them to be specialists, not just generalists. Ref: https://t.co/JqFUY6miE8 π§΅π https://t.co/OZC8MOJPcC
Summarizing important arXiv papers. π πKey Insight: We can better understand and control neural network behaviors by breaking them down into interpretable, fine-grained components, and adjusting these components to remove unintended signals. π§΅π https://t.co/cfgryLVK5e
Mamba Explained As always easy to read an understand. All about state-space models, contenders of transformers. https://t.co/7V3YZAKogr https://t.co/NpTuweaChr
Excellent explanation of Mamba and State Space Models https://t.co/MnNXSBPDqu
Is Attention all you need? Mamba π, a novel AI model based on State Space Models, emerges as a alternative to the widely used Transformer models π€ Read more in our latest article -> https://t.co/6STGsk2BT6
Summarizing important arXiv papers. π πKey Insight: Memory is a universal phenomenon that transcends biology, manifesting in the physical world through the dynamic interactions and structures of matter. Paper ID: 1810.08587 π§΅π https://t.co/TjCk7Z1Hi3
Summarizing important arXiv papers. π πKey Insight: A new Transformer architecture that combines the strengths of both Post-LN and Pre-LN models to prevent gradient vanishing and representation collapse, achieving improved performance in machine translation tasks. π§΅π https://t.co/d4glUIuoPB
Summarizing important arXiv papers. π Key Insight: Trimming LLMs by cutting out their least changing parts keeps them smart but makes them lighter and faster. Paper ID: 2403.17887 π§΅π https://t.co/Y5QJ7PlQ5B
Summarizing important arXiv papers. π π Key Insight : Robots can learn to do complex tasks like humans by reading a "story" of actions, thanks to a trick of translating robot vision into words that a book-reading AI already knows. π§΅π https://t.co/FbixOjijL3
Jamba has been introduced by @AI21Labs, a new approach in #genAI. Combining the Mamba model with transformers, Jamba aims to optimize gen AI. Learn more about this hybrid model in @Techcrunch: https://t.co/00XJx1v6hC