AWS Machine Learning Blog

MARCH 6, 2023

Language models are statistical methods predicting the succession of tokens in sequences, using natural text. Large language models (LLMs) are neural network-based language models with hundreds of millions ( BERT ) to over a trillion parameters ( MiCS ), and whose size makes single-GPU training impractical.

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Marktechpost

JUNE 4, 2023

Large Language Models (LLMs) have successfully catered their way into the challenging areas of Artificial Intelligence. Large Language Models are often augmented with reasoning skills and the ability to use different tools.

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Marktechpost

NOVEMBER 24, 2024

Transformer architectures have revolutionized Natural Language Processing (NLP), enabling significant language understanding and generation progress. A critical issue with LLMs lies in their inference speed, which is constrained by the high memory bandwidth requirements and sequential nature of auto-regressive generation (ARG).

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John Snow Labs

JUNE 27, 2023

Are you curious about the groundbreaking advancements in Natural Language Processing (NLP)? Prepare to be amazed as we delve into the world of Large Language Models (LLMs) – the driving force behind NLP’s remarkable progress. What are Large Language Models (LLMs)?

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AWS Machine Learning Blog

APRIL 25, 2024

The added benefit of asynchronous inference is the cost savings by auto scaling the instance count to zero when there are no requests to process. Hugging Face is a popular open source hub for machine learning (ML) models. Prerequisites Complete the following prerequisites: Create a SageMaker domain.

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AWS Machine Learning Blog

DECEMBER 4, 2024

SageMaker AI makes sure that sensitive data stays completely within each customer’s SageMaker environment and will never be shared with a third party. Their validation capabilities include automatic scoring, version comparison, and auto-calculated metrics for properties such as relevance, coverage, and grounded-in-context.

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ODSC - Open Data Science

AUGUST 24, 2023

Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI Practices Editor’s note: Jayachandran Ramachandran and Rohit Sroch are speakers for ODSC APAC this August 22–23. Auto Eval Common Metric Eval Human Eval Custom Model Eval 3. are harnessed to channel LLMs output.

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Towards AI

JULY 13, 2024

Now, imagine a large, pretrained model tailored for time-series data — one that delivers accurate predictions without extensive retraining. Their decoder-only model, inspired by NLP giants like BERT, uses a patch-based approach to handle data efficiently. However, there is a trade-off.

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Flipboard

NOVEMBER 20, 2023

Retrieval Augmented Generation (RAG) allows you to provide a large language model (LLM) with access to data from external knowledge sources such as repositories, databases, and APIs without the need to fine-tune it. The same approach can be used with different models and vector databases.

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AWS Machine Learning Blog

SEPTEMBER 25, 2023

Transformers are slow and memory-hungry on generating long text sequences due to the sheer size of the models. Large language models (LLMs) used to generate text sequences need immense amounts of computing power and have difficulty accessing the available high bandwidth memory (HBM) and compute capacity.

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AWS Machine Learning Blog

SEPTEMBER 5, 2023

Foundation models are a class of generative AI models that are capable of understanding and generating human-like content, thanks to the vast amounts of unstructured data they have been trained on. You need to first register your endpoint variant with Application Auto Scaling, define a scaling policy, and then apply the scaling policy.

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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 7, 2024

The Hugging Face containers host a large language model (LLM) from the Hugging Face Hub. They are designed for real-time, interactive, and low-latency workloads and provide auto scaling to manage load fluctuations. You can find other Hugging Face models that are better suited for other languages.

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AWS Machine Learning Blog

MAY 22, 2024

Using machine learning (ML) and natural language processing (NLP) to automate product description generation has the potential to save manual effort and transform the way ecommerce platforms operate. BLIP-2 consists of three models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model (LLM).

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AWS Machine Learning Blog

AUGUST 5, 2024

In recent years, we have seen a big increase in the size of large language models (LLMs) used to solve natural language processing (NLP) tasks such as question answering and text summarization. Introduction Modern language models are based on the transformer architecture. compared to 76.4).

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AWS Machine Learning Blog

JULY 24, 2024

Einstein has a list of over 60 features, unlocked at different price points and segmented into four main categories: machine learning (ML), natural language processing (NLP), computer vision, and automatic speech recognition. These models are designed to provide advanced NLP capabilities for various business applications.

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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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AWS Machine Learning Blog

DECEMBER 5, 2023

Customers can create the custom metadata using Amazon Comprehend , a natural-language processing (NLP) service managed by AWS to extract insights about the content of documents, and ingest it into Amazon Kendra along with their data into the index. For example, metadata can be used for filtering and searching. append(e["Text"].upper())

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AWS Machine Learning Blog

MAY 12, 2023

To get started, complete the following steps: On the File menu, choose New and Terminal. Use CodeWhisperer in Studio After we complete the installation steps, we can use CodeWhisperer by opening a new notebook or Python file. To get started, complete the following steps: On the File menu, choose New and Terminal.

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AWS Machine Learning Blog

MAY 1, 2024

Large language models (LLMs) are making a significant impact in the realm of artificial intelligence (AI). Llama 2 is an auto-regressive language model that uses an optimized transformer architecture and is intended for commercial and research use in English.

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AWS Machine Learning Blog

JUNE 13, 2023

SupportGPT leverages state-of-the-art Information Retrieval (IR) systems and large language models (LLMs) to power over 30 million customer interactions annually. Forethought uses per-customer fine-tuned models to detect customer intents in order to solve customer interactions. 2xlarge instances.

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Mlearning.ai

FEBRUARY 19, 2023

Natural Language Processing (NLP) NLP is subset of Artificial Intelligence that is concerned with helping machines to understand the human language. NLP is a branch of AI that focuses on the use of natural human language for various computing applications.

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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. Its model parameters scale from an impressive 7 billion to a remarkable 70 billion.

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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. code_falcon40b_deepspeed/model.py add_as_json(result) That’s it!

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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 22, 2024

Companies across various scales and industries are using large language models (LLMs) to develop generative AI applications that provide innovative experiences for customers and employees. To take complete advantage of this multi-GPU cluster, we use the recent support of QLoRA and PyTorch FSDP. 24xlarge compute instance.

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Mlearning.ai

JULY 4, 2023

Once your CUDA installation completes, reboot your computer. Here are some I found useful: --auto-devices: Automatically uses both the GPU and the CPU as needed. --gpu-memory: gpu-memory: If you don’t want the loaded model to use all of your available GPU memory, you can limit how much GPU memory can be used (I set this to 8Gb).

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AWS Machine Learning Blog

AUGUST 14, 2023

With the advent of large language models (LLMs), we can implement conversational experiences in providing the results to users. However, we need to ensure that the LLMs limit the responses to company data, thereby mitigating model hallucinations.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

An intelligent document processing (IDP) project usually combines optical character recognition (OCR) and natural language processing (NLP) to read and understand a document and extract specific terms or words. She focuses on NLP-specific workloads, and shares her experience as a conference speaker and a book author.

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AWS Machine Learning Blog

MAY 31, 2024

It’s a next generation model in the Falcon family—a more efficient and accessible large language model (LLM) that is trained on a 5.5 It’s built on causal decoder-only architecture, making it powerful for auto-regressive tasks. Deploy the model in SageMaker JumpStart Deployment starts when you choose Deploy.

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AWS Machine Learning Blog

SEPTEMBER 6, 2023

Today, we are excited to announce the capability to fine-tune Llama 2 models by Meta using Amazon SageMaker JumpStart. The Llama 2 family of large language models (LLMs) is a collection of pre-trained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters.

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AWS Machine Learning Blog

DECEMBER 14, 2023

Complete the following steps to edit an existing space: On the space details page, choose Stop space. To start using Amazon CodeWhisperer, make sure that the Resume Auto-Suggestions feature is activated. Choose Create JupyterLab space. For Name , enter a name for your Space. Choose Create space. Choose Run space to relaunch the space.

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Marktechpost

AUGUST 18, 2024

In the field of Natural Language Processing (NLP), Retrieval Augmented Generation, or RAG, has attracted much attention lately. To make sure the knowledge base is as precise and complete as feasible, duplicates should also be removed.

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AWS Machine Learning Blog

MAY 30, 2023

Furthermore, the CPUUtilization metric shows a classic pattern of periodic high and low CPU demand, which makes this endpoint a good candidate for auto scaling. For information, see Automatically Scale Amazon SageMaker Models. If all are successful, then the batch transform job is marked as complete.

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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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Topbots

AUGUST 22, 2023

Large Language Models (LLMs) present a unique challenge when it comes to performance evaluation. Also, while your base model may excel in broad metrics, general performance doesn’t guarantee optimal performance for your specific use cases. auto-evaluation) and using human-LLM hybrid approaches.

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AWS Machine Learning Blog

APRIL 16, 2024

Then we show how you can enhance the in-notebook SQL experience using Text-to-SQL capabilities provided by advanced large language models (LLMs) to write complex SQL queries using natural language text as input. Complete the following steps: On the Secrets Manager console, choose Store a new secret.

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