Too many great people worked on this to name them all, but here’s a start: Peter Robinson Brian Spears Jay Thiagarajan Rushil Sam Ade Jacobs Frank Di Natale @benjbay with much help and support from LLNL Computing Cyrus Harrison Ian Lee and of course Lawrence Livermore National Laboratory!

AI for AI for AI…..really cool!

Yann LeCun Pedro Domingos Hmm here's some seemingly less opinionated holistic view on the topic. #ChatGPT seems to be one of the better collators of public knowledge but of course not replacing human experts who *created* that training data. Got any views on this?

Ask ⁦OpenAI⁩ #ChatGPT simple(st) question about Nigeria, you get 45% accuracy….progress FWIW!!!!!

Exhausted by an amazing day 1. Fusion ignition announced 2. Watch party with the team 3. Friends and colleagues sharing the excitement with the world 3. Daughters sending me screenshots with breaking news about mom and dad’s work! 4. Being a part of something amazing for humanity

We trained an AI using process supervision — rewarding the thought process rather than the outcome — to achieve new state-of-art in mathematical reasoning. Encouraging sign for alignment of advanced AIs: …openai.com/research/impro…

Want to train 1 million token context lengths (all 7 of the Harry Potter books!📚) on a GPT-like model w. 64 GPUs? Announcing DeepSpeed-Ulysses🚀 This release enables highly efficient and scalable LLM training with extremely long sequence lengths🤯 github.com/microsoft/Deep…

🚀Exciting new updates on #DeepSpeed ZeRO-Inference with 20X faster generation! - 4x lesser memory usage through 4-bit weight quantization with no code change needed. - 4x larger batch sizes through KV cache offloading. Available in DeepSpeed v0.10.3: aka.ms/z3-inference

We have much to learn about LLMs. Compact 1.3 billion parameter phi-1.5 model exhibits surprising capabilities. Microsoft Research

🚀Introducing #DeepSpeed-VisualChat! 🖼📜 - Multi-image, multi-round #dialogues - Novel #MultiModal causal attention - Enriched training data via improved blending techniques - Unmatched #scalability (>70B params) Blog: github.com/microsoft/Deep… Paper: arxiv.org/abs/2309.14327

Introducing DeepSpeed-FastGen 🚀 Serve LLMs and generative AI models with - 2.3x higher throughput - 2x lower average latency - 4x lower tail latency w. Dynamic SplitFuse batching Auto TP, load balancing w. perfect linear scaling, plus easy-to-use API github.com/microsoft/Deep…

We're rolling out new features and improvements that developers have been asking for: 1. Our new model GPT-4 Turbo supports 128K context and has fresher knowledge than GPT-4. Its input and output tokens are respectively 3× and 2× less expensive than GPT-4. It’s available now to

🚀 Excited to announce our paper "ZeRO++: Extremely Efficient Collective Communication for Large Model Training" has been accepted at #ICLR2024! 🔍 ZeRO++ significantly reduces communication volume by 4x, achieving up to 3.3x speedup. microsoft.com/en-us/research… #DeepSpeed #AI

Introducing Mixtral, Phi2, Falcon, and Qwen support in #DeepSpeed-FastGen! - Up to 2.5x faster LLM inference - Optimized SplitFuse and token sampling - Exciting new features like RESTful API and more! For more details: github.com/microsoft/Deep… #DeepSpeeed #AI

If you were holding off to try @MSFTDeepSpeed ZeRO++ it looks like deepspeed@master should work well now: github.com/microsoft/Deep… ZeRO++'s main feature is allowing you to use a hybrid approach if you can fit a model on a single node of 8 gpus. So it takes benefit of the super

A nice example of the kind of capabilities unlocked by the long context feature in the Gemini 1.5 Pro model.

Introducing Universal Checkpointing for boosting training efficiency. - Change parallelism (PP, SP, TP, ZeRO-DP) or GPU count mid-stream - Improve resilience by scaling down to healthy nodes💪 - Increase throughput by scaling up to elastic nodes🚀 Blog: rb.gy/aup3pn

Announcing that DeepSpeed now runs natively on Windows. This exciting combination unlocks DeepSpeed optimizations to Windows users and empowers more people and organizations with AI innovations. - HF Inference & Finetuning - LoRA - CPU Offload Blog: shorturl.at/a7TF8

Great to see the amazing DeepSpeed optimizations from Guanhua Wang, Heyang Qin, Masahiro Tanaka, Quentin Anthony, and Sam Ade Jacobs presented by Ammar Ahmad Awan at MUG '24.

🚀Introducing Ulysses-Offload🚀 - Unlock the power of long context LLM training and finetuning with our latest system optimizations - Train LLaMA3-8B on 2M tokens context using 4xA100-80GB - Achieve over 55% MFU Blog: shorturl.at/Spx6Y Tutorial: shorturl.at/bAWu5