Marktechpost

NOVEMBER 7, 2023

Big language models (LLMs) are becoming increasingly skilled in programming in various contexts, such as finishing partly written code, interacting with human programmers, and even figuring out challenging programming riddles at the competition level. Figure 1: The LILO learning loop overview. (Al)

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AWS Machine Learning Blog

APRIL 8, 2024

This version offers support for new models (including Mixture of Experts), performance and usability improvements across inference backends, as well as new generation details for increased control and prediction explainability (such as reason for generation completion and token level log probabilities).

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Unite.AI

AUGUST 1, 2023

One such model that has garnered considerable attention is OpenAI's ChatGPT , a shining exemplar in the realm of Large Language Models. Prompt design and engineering are growing disciplines that aim to optimize the output quality of AI models like ChatGPT.

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AWS Machine Learning Blog

DECEMBER 13, 2023

We use the AWS Neuron software development kit (SDK) to access the AWS Inferentia2 device and benefit from its high performance. We then use a large model inference container powered by Deep Java Library (DJLServing) as our model serving solution. GPU Optimized Kernel using a ml.g5.2xlarge instance type.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

Llama 2 stands at the forefront of AI innovation, embodying an advanced auto-regressive language model developed on a sophisticated transformer foundation. Its model parameters scale from an impressive 7 billion to a remarkable 70 billion. Assistant: Wow, you must be really curious about language models!

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AWS Machine Learning Blog

OCTOBER 11, 2023

What is the optimal framework and configuration for hosting large language models (LLMs) for text-generating generative AI applications? The decode phase includes the following: Completion – After the prefill phase, you have a partially generated text that may be incomplete or cut off at some point. The default is 32.

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AWS Machine Learning Blog

MAY 31, 2023

Complete the following steps: Launch the provided CloudFormation template. When the stack is complete, you can move to the next step. We use Amazon ECR to store a custom Docker image containing our scripts and Neuron packages needed to train a model with ECS jobs running on Trn1 instances. For this post, we use trainium-key.

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AWS Machine Learning Blog

JUNE 13, 2023

Last week, Technology Innovation Institute (TII) launched TII Falcon LLM , an open-source foundational large language model (LLM). Legacy hosting solutions used for smaller models typically don’t offer this type of functionality, adding to the difficulty. Qing Lan is a Software Development Engineer in AWS.

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AWS Machine Learning Blog

MAY 4, 2023

Today, we are excited to announce that Amazon SageMaker supports AWS Inferentia2 (ml.inf2) and AWS Trainium (ml.trn1) based SageMaker instances to host generative AI models for real-time and asynchronous inference. ml.inf2 instances are available for model deployment on SageMaker in US East (Ohio) and ml.trn1 instances in US East (N.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

The Test inference tab enables you to test your model by sending test requests to one of the in-service models directly from the SageMaker Studio interface. You can also edit the auto scaling policy on the Auto-scaling tab on this page. Some familiarity with SageMaker Studio is also assumed.

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The MLOps Blog

JULY 18, 2023

I think another huge component, as I kind of was mentioning earlier, is Conversational AI tends to require large pipelines of machine learning. You usually cannot do a one-shot, “here’s a model,” then it handles everything no matter what you’re reading today. And so we actually need to have a full pipeline of models.

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Google Research AI blog

JANUARY 18, 2023

We have also seen significant success in using large language models (LLMs) trained on source code (instead of natural language text data) that can assist our internal developers, as described in ML-Enhanced Code Completion Improves Developer Productivity. (See paper for details.)

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AWS Machine Learning Blog

APRIL 17, 2023

Quantization and compression can reduce model size and serving cost by reducing the precision of weights or reducing the number of parameters via pruning or distillation. Compilation can optimize the computation graph and fuse operators to reduce memory and compute requirements of a model. For more details, refer to the GitHub repo.

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IBM Journey to AI blog

MARCH 6, 2024

Software development is one arena where we are already seeing significant impacts from generative AI tools. A McKinsey study claims that software developers can complete coding tasks up to twice as fast with generative AI. This can aid in maintaining code quality and performance over time.

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Unite.AI

APRIL 18, 2024

From self-driving cars to language models that can engage in human-like conversations, AI is rapidly transforming various industries, and software development is no exception. However, the advent of AI-powered software engineers like SWE-Agent has the potential to disrupt this age-old paradigm.

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AWS Machine Learning Blog

JUNE 5, 2023

Today, as part of Amazon Web Services’ partnership with Hugging Face, we are excited to announce the release of a new Hugging Face Deep Learning Container (DLC) for inference with Large Language Models (LLMs). The Hugging Face LLM DLC provides these optimizations out of the box and makes it easier to host LLM models at scale.

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Flipboard

APRIL 13, 2023

To give a sense for the change in scale, the largest pre-trained model in 2019 was 330M parameters. Now, the largest models are more than 500B parameters—a 1,600x increase in size in just a few years. Today’s FMs, such as the large language models (LLMs) GPT3.5

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