AWS Machine Learning Blog

MAY 17, 2024

LLMs are specifically focused on language-based tasks such as summarization, text generation, classification, open-ended conversation, and information extraction. FMs and LLMs, even though they’re pre-trained, can continue to learn from data inputs or prompts during inference. send the LLM generated response to a human reviewer.

Generative AI 129

Generative AI LLM Prompt Engineer Prompt Engineering 129

AWS Machine Learning Blog

FEBRUARY 12, 2025

The brand might be willing to absorb the higher costs of using a more powerful and expensive FMs to achieve the highest-quality classifications, because misclassifications could lead to customer dissatisfaction and damage the brands reputation. Consider another use case of generating personalized product descriptions for an ecommerce site.

Auto-classification 79

Auto-classification Metadata Generative AI Machine Learning 79

AWS Machine Learning Blog

MARCH 28, 2024

You can deploy this solution with just a few clicks using Amazon SageMaker JumpStart , a fully managed platform that offers state-of-the-art foundation models for various use cases such as content writing, code generation, question answering, copywriting, summarization, classification, and information retrieval. Create a question embedding.

LLM 128

LLM Auto-complete Auto-classification Generative AI 128

Marktechpost

AUGUST 10, 2023

The model consists of two key components: a location-aware image tokenizer and an LLM-based decoder. An LLM-based decoder is utilized to extract vision and text features for discriminative tasks and auto-regressively generate response tokens in generative tasks. million common and rare concepts in the real world and has 132.2

Auto-classification 98

Auto-classification Natural Language Processing Large Language Models LLM 98

AWS Machine Learning Blog

SEPTEMBER 12, 2024

Hallucinations – LLMs have a remarkable ability to respond to natural language, and clearly encode significant amounts of knowledge. An LLM doesn’t model facts so much as it models language. Scaling language model training with Amazon SageMaker HyperPod Thomson Reuters knew that training LLMs would require significant computing power.

Auto-classification 88

Auto-classification Auto-complete LLM ML 88

John Snow Labs

FEBRUARY 1, 2025

This talk explores how RAG LLMs power specialized AI agents for Auto Bodily Injury, Workers Compensation, and General Liability claims, handling identity verification, intent classification, document processing, fraud detection, and claim negotiation with high accuracy.

Responsible AI 52

Responsible AI Auto-classification Automation LLM 52

AWS Machine Learning Blog

JUNE 20, 2024

It allows LLMs to reference authoritative knowledge bases or internal repositories before generating responses, producing output tailored to specific domains or contexts while providing relevance, accuracy, and efficiency. Generation is the process of generating the final response from the LLM.

Auto-classification 126

Auto-classification LLM Prompt Engineer Prompt Engineering 126

The MLOps Blog

MARCH 23, 2023

These models have achieved various groundbreaking results in many NLP tasks like question-answering, summarization, language translation, classification, paraphrasing, et cetera. Storage capacity With parameters ranging from millions to billions, LLM can pose storage capacity challenges. For instance, a 1.5B

NLP 115

NLP LLM Large Language Models Natural Language Processing 115

AWS Machine Learning Blog

APRIL 8, 2024

Context window extension techniques Rotary Positional Embedding (RoPE)-based context scaling is now available on the LMI-Dist, vLLM, and TensorRT-LLM backends. Be mindful that LLM token probabilities are generally overconfident without calibration. TensorRT-LLM requires models to be compiled into efficient engines before deployment.

Auto-complete 100

Auto-complete LLM Deep Learning Machine Learning 100

AWS Machine Learning Blog

MAY 2, 2024

This post showcases a reward modeling technique to efficiently customize LLMs for an organization by programmatically defining rewards functions that capture preferences for model behavior. We demonstrate an approach to deliver LLM results tailored to an organization without intensive, continual human judgement.

LLM 104

LLM Auto-complete Auto-classification Artificial Intelligence 104

Mlearning.ai

DECEMBER 19, 2023

Modifying Microsoft Phi 2 LLM for Sequence Classification Task. Training using LoRA and QLoRA approaches using Huggingface trainer Microsoft’s Phi-2 LLM is a 2.7 Phi-2, currently doesn’t have sequence classification support on HuggingFace AutoModel APIs.

Auto-classification 52

Auto-classification LLM Large Language Models Data Scientist 52

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. Similarly, you can deploy the Falcon 2 11B LLM using its own model ID.

Python 112

Python Machine Learning Auto-classification ML 112

Snorkel AI

OCTOBER 31, 2023

Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification Guha et al. A case for reframing automated medical image classification as segmentation Hooper et al. The authors in this study develop a task-agnostic approach for improving LLM reasoning abilities without task-specific training (e.g. fine-tuning).

Auto-classification 59

Auto-classification AI Researcher AI Research Large Language Models 59

Snorkel AI

OCTOBER 31, 2023

Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification Guha et al. A case for reframing automated medical image classification as segmentation Hooper et al. The authors in this study develop a task-agnostic approach for improving LLM reasoning abilities without task-specific training (e.g. fine-tuning).

AI Researcher 52

AI Researcher AI Research Auto-classification Large Language Models 52

Unite.AI

JUNE 24, 2024

You can surely train a simple classifier on a system with 8 GB RAM and no GPU device, but it would not be prudent to train an LLM model on the same infrastructure. It checks data and model quality, data drift, target drift, and regression and classification performance. We also save the trained model as an artifact using wandb.save().

Data Drift 120

Data Drift Machine Learning Data Scientist ML 120

ODSC - Open Data Science

OCTOBER 11, 2023

For more complex issues like label errors, you can again simply filter out all the auto-detected bad data. For instance, when fine-tuning various LLM models on a text classification task (politeness prediction), this auto-filtering improves LLM performance without any change in the modeling code!

Auto-classification 52

Auto-classification Auto-complete Data Drift Machine Learning 52

Mlearning.ai

FEBRUARY 19, 2023

Along with text generation it can also be used to text classification and text summarization. The auto-complete feature on your smartphone is based on this principle. When you type “how”, the auto-complete will suggest words like “to” or “are”.

Auto-classification 52

Auto-classification Natural Language Processing Auto-complete NLP 52

Snorkel AI

JULY 14, 2023

classification, information extraction) using programmatic labeling, fine-tuning, and distillation. This is especially helpful for classification across many classes, where users tend to write more LFs. Intelligent Auto-Suggest Strategies for Labeling Functions You can now target specific error hotspots using slice-based suggestions.

Auto-classification 52

Auto-classification Data Scientist Machine Learning LLM 52

Snorkel AI

JULY 14, 2023

classification, information extraction) using programmatic labeling, fine-tuning, and distillation. Alongside Snorkel GenFlow, we also announced Snorkel Foundry for programmatically sampling, filtering, cleaning, and augmenting proprietary data for domain-specific pre-training of Large Language Models (LLM).

Auto-classification 52

Auto-classification Machine Learning Data Science Data Platform 52

Snorkel AI

JULY 14, 2023

classification, information extraction) using programmatic labeling, fine-tuning, and distillation. Alongside Snorkel GenFlow, we also announced Snorkel Foundry for programmatically sampling, filtering, cleaning, and augmenting proprietary data for domain-specific pre-training of Large Language Models (LLM).

Auto-classification 52

Auto-classification Machine Learning Data Science Data Platform 52

The MLOps Blog

JUNE 27, 2023

Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on. The platform provides a comprehensive set of annotation tools, including object detection, segmentation, and classification.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Bugra Akyildiz

OCTOBER 21, 2023

AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. They can operate in various modes that employ combinations of LLMs, human inputs, and tools. It simplifies the orchestration, automation, and optimization of a complex LLM workflow.

Auto-classification 52

Auto-classification Large Language Models LLM Auto-complete 52

Bugra Akyildiz

MAY 14, 2023

Simulation of consumption of queue up to drivers estimated position becomes an easy simple algorithm and results in wait time classification. Libraries Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. They refer to this as our “demand” model.

Auto-classification 52

Auto-classification LLM ML Python 52

The MLOps Blog

MARCH 12, 2024

TL;DR LLMOps involves managing the entire lifecycle of Large Language Models (LLMs), including data and prompt management, model fine-tuning and evaluation, pipeline orchestration, and LLM deployment. Prompt-response management: Refining LLM-backed applications through continuous prompt-response optimization and quality control.

Prompt Engineer 52

Prompt Engineer Prompt Engineering Large Language Models LLM 52

AWS Machine Learning Blog

NOVEMBER 16, 2023

In your application, take time to imagine the diverse set of questions available in your images to help your classification or regression task. In social media platforms, photos could be auto-tagged for subsequent use. The enhanced data contains new data features relative to this example use case.

ML 102

ML Machine Learning Computer Vision Auto-classification 102

John Snow Labs

JUNE 27, 2023

Instead of navigating complex menus or waiting on hold, they can engage in a conversation with a chatbot powered by an LLM. The LLM analyzes the customer’s query, processes the natural language input, and generates a contextual response in real-time. Pythia: Pythia is a vision and language LLM developed by EleutherAI.

Large Language Models 52

Large Language Models BERT Natural Language Processing NLP 52

Towards AI

NOVEMBER 10, 2024

Recently, I discovered a Python package called Outlines, which provides a versatile way to leverage Large Language Models (LLMs) for tasks like: Classification Named Entity Extraction Generate synthetic data Summarize a document … And… Play Chess (there are also 5 other uses). First, what is sampling in an LLM?

LLM 119

LLM Auto-classification Large Language Models Python 119

AWS Machine Learning Blog

JULY 16, 2024

The NVIDIA NeMo Framework provides a comprehensive set of tools, scripts, and recipes to support each stage of the LLM journey, from data preparation to training and deployment. His work spans multilingual text-to-speech, time series classification, ed-tech, and practical applications of deep learning.

Generative AI 120

Generative AI Auto-complete Auto-classification Deep Learning 120

AWS Machine Learning Blog

NOVEMBER 1, 2023

Combined with large language models (LLM) and Contrastive Language-Image Pre-Training (CLIP) trained with a large quantity of multimodality data, visual language models (VLMs) are particularly adept at tasks like image captioning, object detection and segmentation, and visual question answering.

Auto-classification 109

Auto-classification LLM Auto-complete Generative AI 109

Unite.AI

MARCH 28, 2024

In this technical deep dive, we'll explore cutting-edge techniques for accelerating LLM inference, enabling faster response times, higher throughput, and more efficient utilization of hardware resources. Let's start by understanding why LLM inference is so challenging compared to traditional NLP models.

Large Language Models 130

Large Language Models LLM Natural Language Processing Prompt Engineer 130

AWS Machine Learning Blog

AUGUST 8, 2023

For example, input images for an object detection use case might need to be resized or cropped before being served to a computer vision model, or tokenization of text inputs before being used in an LLM. However, If the instance’s storage volume reaches its capacity, SageMaker will delete the unused models from the storage volume.

BERT 88

BERT Deep Learning Auto-classification Python 88

Marktechpost

JULY 15, 2023

Here are some other open-source large language models (LLMs) that are revolutionizing conversational AI. LLaMA Release date : February 24, 2023 LLaMa is a foundational LLM developed by Meta AI. Dolly Release date: March 8, 2023 Dolly is an instruction-following LLM developed by Databricks.

Conversational AI 98

Conversational AI AI Modeling Large Language Models Auto-classification 98

Flipboard

APRIL 13, 2023

For instance, a financial firm that needs to auto-generate a daily activity report for internal circulation using all the relevant transactions can customize the model with proprietary data, which will include past reports, so that the FM learns how these reports should read and what data was used to generate them.

Generative AI 182

Generative AI ML AI AI 182

Topbots

JUNE 29, 2023

It not only requires SQL mastery on the part of the annotator, but also more time per example than more general linguistic tasks such as sentiment analysis and text classification. 4] In the open-source camp, initial attempts at solving the Text2SQL puzzle were focussed on auto-encoding models such as BERT, which excel at NLU tasks.[5,

Algorithm 52

Algorithm Auto-complete Data Scientist Auto-classification 52

AWS Machine Learning Blog

OCTOBER 23, 2024

Set up the large language model (LLM) Meta Llama3-70B-Instruct and define a prompt template for generating questions based on the context provided by the document chunks. Use the LLM to generate synthetic question answer pairs for each document chunk. Auto scaling helps make sure the endpoint can handle varying workloads efficiently.

Auto-complete 75

Auto-complete Auto-classification Generative AI Artificial Intelligence 75