AWS Machine Learning Blog

DECEMBER 4, 2023

An important aspect of our strategy has been the use of SageMaker and AWS Batch to refine pre-trained BERT models for seven different languages. The project was completed in a month and deployed to production after a week of testing. We used GPU jobs that help us run jobs that use an instance’s GPUs.

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BERT Auto-complete Data Scientist Machine Learning 118

Marktechpost

APRIL 16, 2024

The book covers the inner workings of LLMs and provides sample codes for working with models like GPT-4, BERT, T5, LLaMA, etc. The book covers topics like Auto-SQL, NER, RAG, Autonomous AI agents, and others. LangChain Handbook This book is a complete guide to integrating and implementing LLMs using the LangChain framework.

Large Language Models 104

Large Language Models Auto-complete Prompt Engineering Prompt Engineer 104

AWS Machine Learning Blog

MARCH 2, 2023

Transformer-based language models such as BERT ( Bidirectional Transformers for Language Understanding ) have the ability to capture words or sentences within a bigger context of data, and allow for the classification of the news sentiment given the current state of the world. The code can be found on the GitHub repo. eks-create.sh

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AWS Machine Learning Blog

MAY 8, 2023

With kernel auto-tuning, the engine selects the best algorithm for the target GPU, maximizing hardware utilization. SageMaker MMEs can horizontally scale using an auto scaling policy and provision additional GPU compute instances based on specified metrics. Note that the cell takes around 30 minutes to complete. !docker

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Towards AI

JULY 13, 2024

Their decoder-only model, inspired by NLP giants like BERT, uses a patch-based approach to handle data efficiently. Generating Longer Forecast Output Patches In Large Language Models (LLMs), output is generally produced in an auto-regressive manner, generating one token at a time. However, there is a trade-off.

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Unite.AI

AUGUST 1, 2023

In zero-shot learning, no examples of task completion are provided in the model. Chain-of-thought Prompting Chain-of-thought prompting leverages the inherent auto-regressive properties of large language models (LLMs), which excel at predicting the next word in a given sequence.

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John Snow Labs

JUNE 27, 2023

In this section, we will provide an overview of two widely recognized LLMs, BERT and GPT, and introduce other notable models like T5, Pythia, Dolly, Bloom, Falcon, StarCoder, Orca, LLAMA, and Vicuna. BERT excels in understanding context and generating contextually relevant representations for a given text.

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Towards AI

JULY 21, 2023

This… github.com Kite Auto Complete For all the Jupyter notebook fans, Kite code autocomplete is now supported! For complete coverage, follow our Twitter: @Quantum_Stat www.quantumstat.com Join thousands of data leaders on the AI newsletter. The new architecture helps reduce parameter size in addition to making models deeper.

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NLP Auto-complete Natural Language Processing BERT 77

Unite.AI

SEPTEMBER 13, 2024

Kernel Auto-tuning : TensorRT automatically selects the best kernel for each operation, optimizing inference for a given GPU. The API constructs a graph representation of the model, making it easier to manage the complex layers involved in LLM architectures like GPT or BERT. build/tensorrt_llm*.whl

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deepsense.ai

OCTOBER 8, 2023

Usually agents will have: Some kind of memory (state) Multiple specialized roles: Planner – to “think” and generate a plan (if steps are not predefined) Executor – to “act” by executing the plan using specific tools Feedback provider – to assess the quality of the execution by means of auto-reflection.

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Mlearning.ai

JUNE 22, 2023

In this article, we will delve into the three broad categories of transformer models based on their training methodologies: GPT-like (auto-regressive), BERT-like (auto-encoding), and BART/T5-like (sequence-to-sequence). In such cases, we might not always have a complete sequence we are mapping to/from.

Auto-classification 52

Auto-classification Auto-complete BERT Deep Learning 52

AWS Machine Learning Blog

DECEMBER 11, 2024

Two open-source libraries, Ragas (a library for RAG evaluation) and Auto-Instruct, used Amazon Bedrock to power a framework that evaluates and improves upon RAG. Generating improved instructions for each question-and-answer pair using an automatic prompt engineering technique based on the Auto-Instruct Repository.

Generative AI 71

Generative AI Chatbots Prompt Engineering Prompt Engineer 71

DataRobot Blog

FEBRUARY 2, 2023

As an example, getting started with a BERT model for question answering (bert-large-uncased-whole-word-masking-finetuned-squad) is as easy as executing these lines: !pip pip install transformers==4.25.1 pip install transformers==4.25.1 datarobot==3.0.2 the custom.py print("Creating model deployment.") print("Creating model deployment.")

BERT 52

BERT Large Language Models Natural Language Processing Machine Learning 52

Marktechpost

JULY 11, 2023

The best example is OpenAI’s ChatGPT, the well-known chatbot that does everything from content generation and code completion to question answering, just like a human. Even OpenAI’s DALL-E and Google’s BERT have contributed to making significant advances in recent times. What is AutoGPT? What is BabyAGI?

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Auto-complete Generative AI Large Language Models OpenAI 40

Explosion

JULY 6, 2021

For example, you’ll be able to use the information that certain spans of text are definitely not PERSON entities, without having to provide the complete gold-standard annotations for the given example. spacy-dbpedia-spotlight Use DBpedia Spotlight to link entities ✍️ contextualSpellCheck Contextual spell correction using BERT ?

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AWS Machine Learning Blog

OCTOBER 23, 2024

It is a family of embedding models with a BERT-like architecture, designed to produce high-quality embeddings from text data. Optionally, set up auto scaling for the endpoint to automatically adjust the number of instances based on the incoming request traffic. The BGE models come in three sizes: bge-large-en-v1.5:

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Auto-complete Auto-classification Generative AI Artificial Intelligence 79

TheSequence

MAY 24, 2023

This is also where I met Lewis Tunstall and as language models with BERT and GPT-2 started taking off we decided to start working on a textbook about transformer models and the Hugging Face ecosystem. data or auto-generated files). cell outputs) for code completion in Jupyter notebooks (see this Jupyter plugin ).

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Auto-complete Python ML Machine Learning 59

The MLOps Blog

SEPTEMBER 26, 2024

Self-attention is the mechanism where tokens interact with each other (auto-regressive) and with the knowledge acquired during pre-training. In extreme cases, certain tokens can completely break an LLM. Others, like Gary Marcus, argue strongly that transformer-based LLMs are completely unable to eliminate hallucinations.

LLM 72

LLM Auto-complete Prompt Engineer Prompt Engineering 72

Explosion

JANUARY 31, 2021

de_dep_news_trf German bert-base-german-cased 99.0 95.8 - es_dep_news_trf Spanish bert-base-spanish-wwm-cased 98.2 94.4 - zh_core_web_trf Chinese bert-base-chinese 92.5 When you load a config, spaCy checks if the settings are complete and if all values have the correct types. Reproducibility with no hidden defaults.

NLP 52

NLP Python BERT Auto-classification 52

AWS Machine Learning Blog

APRIL 17, 2023

SageMaker LMI containers includes model download optimization by using the s5cmd library to speed up the model download time and container startup times, and eventually speed up auto scaling on SageMaker. A complete example that illustrates the no-code option can be found in the following notebook.

Prompt Engineer 94

Prompt Engineer Prompt Engineering Deep Learning Machine Learning 94

Google Research AI blog

FEBRUARY 28, 2023

Dataset Description Auto-Arborist A multiview urban tree classification dataset that consists of ~2.6M MultiBERTs Predictions on Winogender Predictions of BERT on Winogender before and after several different interventions. UGIF A multi-lingual, multi-modal UI grounded dataset for step-by-step task completion on the smartphone.

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Robotics Deep Learning Auto-classification Auto-complete 72

Jay Alammar

DECEMBER 16, 2020

This article focuses on auto-regressive models, but these methods are applicable to other architectures and tasks as well. In generating the second token to complete the date, the name still is the most important with 60% importance, followed by the first portion of the date -- a model output, but an input to the second time step.

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Viso.ai

DECEMBER 22, 2023

This leap forward is due to the influence of foundation models in NLP, such as GPT and BERT. Full-Auto: SAM independently predicts segmentation masks in the final stage, showcasing its ability to handle complex and ambiguous scenarios with minimal human intervention.

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Convolutional Neural Networks Computer Vision Neural Network Auto-classification 52

AWS Machine Learning Blog

MARCH 6, 2023

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. This results in faster restarts and workload completion. Cluster update is currently enabled for P and G GPU-based instance types.

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Large Language Models LLM Machine Learning ML 96

Snorkel AI

MARCH 2, 2023

It came to its own with the creation of the transformer architecture: Google’s BERT, OpenAI, GPT2 and then 3, LaMDA for conversation, Mina and Sparrow from Google DeepMind. Others, toward language completion and further downstream tasks. So there’s obviously an evolution. Really quickly, LLMs can do many things.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Snorkel AI

MARCH 2, 2023

It came to its own with the creation of the transformer architecture: Google’s BERT, OpenAI, GPT2 and then 3, LaMDA for conversation, Mina and Sparrow from Google DeepMind. Others, toward language completion and further downstream tasks. So there’s obviously an evolution. Really quickly, LLMs can do many things.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Robot Writers AI

AUGUST 27, 2023

Patel’s secret: Auto-GPT, a tool that can auto-generate its own prompts that ChatGPT can use to complete a task — without human supervision. Observes Patel: “Instead of spending hours fine-tuning models for different tasks, Auto-GPT uses smart, automated techniques.

Auto-complete 52

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AWS Machine Learning Blog

JANUARY 9, 2023

The models can be completely heterogenous, with their own independent serving stack. After these business logic steps are complete, the inputs are passed through to ML models. And during idle time, it should be able to turn off compute capacity completely so that you’re not charged. ML inference options.

ML 82

ML Auto-complete Auto-classification Deep Learning 82

AWS Machine Learning Blog

FEBRUARY 24, 2023

In addition, load testing can help guide the auto scaling strategies using the right metrics rather than iterative trial and error methods. We tested two NLP models: bert-base-uncased (109M) and roberta-large (335M). NLP bert-base-uncased 109M PyTorch 26 62% 70 -39% 105 142% 140 29% TensorRT 42. Diff (%) CV CNN Resnet50 ml.g4dn.2xlarge

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AWS Machine Learning Blog

NOVEMBER 1, 2023

Additionally, you benefit from advanced features like auto scaling of inference endpoints, enhanced security, and built-in model monitoring. The pre-training of IDEFICS-9b took 350 hours to complete on 128 Nvidia A100 GPUs, whereas fine-tuning of IDEFICS-9b-instruct took 70 hours on 128 Nvidia A100 GPUs, both on AWS p4.24xlarge instances.

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Topbots

JUNE 29, 2023

On a more advanced stance, everyone who has done SQL query optimisation will know that many roads lead to the same result, and semantically equivalent queries might have completely different syntax. 3] provides a more complete survey of Text2SQL data augmentation techniques. different variants of semantic parsing.

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