Usama Alameldin Salama’s Post

Co authored a blog with some awesome people on the availability of Llama 2 inference and fine-tuning support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker JumpStart! Check it out - it contains an end to end walkthrough and reach out with any questions! Link: https://lnkd.in/evypm6Y6 Huang Xin Nitin Eusebius #aws #llama #sagemaker #jumpstart

tl;dr In this blog we walk through the steps required to build a chatbot powered by Temporal and Amazon Web Services (AWS) Bedrock

AWS Serverless Product Recommendation: powered by k-NN with OpenSearch #serverless #opensearch #aws

Metas Code Llama foundation models for code generation are now available on Amazon SageMaker JumpStart for easy deployment. Code Llama is a large language model that can generate code and text from code and text prompts. #aws #awscloud #cloud #amazonsagemaker #amazonsagemakerjumpstart #announcements #artificialintelligence

We’re excited to share our open-source framework that lets you score different generative language models across various evaluation tasks and benchmarks - used by leaderboards such as Hugging Face. While working with the Amazon Web Services (AWS) team to train our models on AWS Tranium, we realized we were restricted to both VRAM and the availability of GPU instances when it came to the mainstream tool for LLM evaluation. Our open source solution overcomes these challenges—integrating AWS Neuron, the library behind AWS Inferentia and Trainium, into lm-evaluation-harness. Take a look at: ✅ How We Broke Down Our Tests ✅ The Challenges We Encountered ✅ An Example of Using the Testing Harness on AWS Inferentia A huge thank you Michael Feil and Jim Burtoft for the partnership and collaboration, giving back to the developer community. #Gradient #GradientAI #AWS #LLM #OpenSource #LLMEvaluation

With fine-tuning, you can improve our Command R model’s performance by over 20%. Enterprises need highly customized models for optimal performance. Fine-tuning on Command R allows organizations to achieve this efficiently, using company-specific data to deliver superior results at a fraction of the cost of larger models. Check out this step-by-step guide on how to fine-tune your Command R model on Amazon SageMaker (Amazon Web Services (AWS)). https://lnkd.in/ga3Q-F2m 

Nope. We’re not over it yet. It’s just over a week since our team got back from #AWS re:Invent. And they’re still talking about it. Who can blame them? And if you haven’t had a chance to catch up on what was big at this year’s Amazon Web Services (AWS) showpiece, don’t worry. Maarten Bruntink, Antti H. and Mariusz Preiss have given their top picks from Vegas. Get the lowdown on: 🧠 #GenAI (obviously), and what really matters here 🛒 How AWS are leaning into marketplace 🤑 How to be a “frugal architect” 🎥 What the top sessions were (and where to rewatch them) Give it a read: https://lnkd.in/ddbYEZrC #reinvent2023 #reinvent #awsreinvent #nordcloudbytes

Really excited to share some of our learnings integrating Amazon Web Services (AWS) Inferentia instances into our evals stack. I know a lot of our customers have trouble obtaining GPUs for quick eval iteration cycles in their development process so we've worked to remove that friction. #AWS #GenerativeAI #LLMs #Evals

Want to take your LLM game to the next level without breaking the bank? Gradient has cracked the code with AWS Inferentia! I just stumbled upon this gem of an article that breaks down how Gradient is making LLM benchmarking a breeze. And let me tell you, it's a game-changer! Here are the key takeaways: • Gradient's AI Development Lab is helping enterprises build custom LLMs and AI co-pilots. Pretty cool, right? • They faced some challenges with the mainstream benchmarking tool, but AWS Neuron came to the rescue! ️ • By integrating AWS Neuron into lm-evaluation-harness, Gradient can now benchmark their models against public ones during training and after. Talk about efficiency! • Moving to Amazon EC2 Inf2 instances powered by AWS Inferentia2 gave them access to a whopping 384 GB of shared accelerator memory. Plus, they saved up to 90% with AWS Spot Instances! • The article even provides a step-by-step guide on deploying Gradient's model using AWS Inferentia2 instances. It's like having a cheat sheet! If you're into LLMs and want to up your evaluation game, this is a must-read! Check out the full article here: "Gradient makes LLM benchmarking cost-effective and effortless with AWS Inferentia" https://lnkd.in/gW753SFp Happy Wednesday! Let me know your thoughts. Casey Jones #LLM #Benchmarking #AWSInferentia #Gradient #AI