AWS Machine Learning Blog

MAY 2, 2024

Lower latency – The lower inference latency and faster training times for large datasets of these new recipes can reduce the delay for your end-users. compared to previous versions. Solution overview To use the User-Personalization-v2 and Personalized-Ranking-v2 recipes, you first need to set up Amazon Personalize resources.

Metadata 86

Metadata Software Engineer Large Language Models Machine Learning 86

BAIR

MAY 29, 2024

We then show that fine-tuning the model on this high quality curated dataset, can enable SLMs to even exceed GPT-4-Turbo’s function calling performance. Next, we first discuss how we generated such a dataset, and then discuss the fine tuning approach. function B can only be executed after the execution of function A).

LLM 300

LLM Robotics OpenAI ChatGPT 300

Unite.AI

SEPTEMBER 15, 2023

This article briefly compares Python vs. Spyder to help developers make an informed choice. A Brief Look Into Pycharm & Spyder Before comparing PyCharm vs. Spyder to determine the best IDE for Python development, it’s essential to understand what these tools entail. However, Spyder only supports Git for version control.

Python 147

Python Data Scientist Data Science Data Analysis 147

Marktechpost

MAY 14, 2024

Earlier methods, which rely heavily on human input or sophisticated algorithms for distilling complex datasets into usable training materials, are often constrained by high costs, limited scalability, and potential biases. This method exploits the rich, diverse online content, converting it into a valuable resource for tuning LLMs.

Large Language Models 106

Large Language Models LLM Data Quality Algorithm 106

Marktechpost

MAY 28, 2024

GPT-2: Scaling Up GPT-2, released in February 2019, significantly scaled up the model size and training data, demonstrating the benefits of larger models and datasets. It achieved state-of-the-art performance on numerous benchmarks, including the SuperGLUE and LAMBADA datasets. Model Size: 1.5 Bridging the Gap GPT-3.5,

Neural Network 71

Neural Network Convolutional Neural Networks NLP Data Analysis 71

Marktechpost

MAY 19, 2024

TRANSMI integrates new subwords tailored for transliterated data into the mPLMs’ vocabularies, particularly excelling in the Max-Merge mode for high-resource languages. The datasets used to validate TRANSMI span a variety of scripts, providing a comprehensive assessment of its effectiveness.

Machine Learning 98

Machine Learning NLP Natural Language Processing ML 98

Unite.AI

AUGUST 2, 2023

These limitations are a major issue why an average human mind is able to learn from a single type of data much more effectively when compared to an AI model that relies on separate models & training data to distinguish between an image, text, and speech. They require a high amount of computational power.

Computer Vision 246

Computer Vision Natural Language Processing Algorithm Convolutional Neural Networks 246

Unite.AI

MARCH 26, 2024

Although expressive, the attention mechanism in transformer-derived LLMs requires high computational resources during both inference and training, necessitating substantial memory for the sequence length and quadratic FLOPs. They utilize a routing function to determine which ‘experts' are called into action based on the given context.

Natural Language Processing 147

Natural Language Processing Neural Network Large Language Models Convolutional Neural Networks 147

Unite.AI

APRIL 4, 2024

However, compared to domains like natural language processing and image recognition , the integration of advanced artificial intelligence (AI) techniques into time series forecasting has been relatively slow. This assists in healthcare planning, resource allocation, and policy making.

Data Analysis 226

Data Analysis Natural Language Processing Artificial Intelligence Artificial Intelligence 226

Unite.AI

MARCH 14, 2024

LLaMA, Chinchilla, and PaLM-540B on a wide range of benchmarks commonly used for comparing LLMs, Inflection-1 enables users to interact with Pi, Inflection AI's personal AI, in a simple and natural way, receiving fast, relevant, and helpful information and advice. Outperforming industry giants such as GPT-3.5, With Inflection-2.5,

LLM 278

LLM Large Language Models Data Ingestion AI 278

Marktechpost

MAY 17, 2024

This persistent problem motivates the pursuit of more effective substitutes that sustain performance standards while requiring fewer resources. Although these models perform well on NLP tasks, they could be more practical in contexts with limited resources. The models were trained using the RefinedWeb dataset with 1.2

Neural Network 95

Neural Network NLP Natural Language Processing AI 95

Marktechpost

DECEMBER 15, 2023

However, SAM is not optimized for edge devices, which can lead to retarded performance and high resource consumption. This optimized version of SAM is designed to ensure enhanced performance without sacrificing accuracy on resource-constrained edge devices. A lightweight module is added to address dataset bias issues.

Machine Learning 115

Machine Learning Computer Vision Artificial Intelligence Artificial Intelligence 115

Marktechpost

DECEMBER 5, 2023

Models like OpenAI’s ChatGPT and Google Bard require enormous volumes of resources, including a lot of training data, substantial amounts of storage, intricate, deep learning frameworks, and enormous amounts of electricity. These versions offer flexibility in terms of applications, ranging from Mini with 4.4

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BERT Large Language Models Neural Network Natural Language Processing 127

IBM Journey to AI blog

MAY 20, 2024

Rather, quantum computers will serve as a highly specialized and complementary computing resource for running specific tasks. Quantum mechanics offers us access to a tweaked and counterintuitive version of probability that allows us to run computations inaccessible to classical computers. Search and optimization.

Algorithm 241

Algorithm Artificial Intelligence Artificial Intelligence Explainability 241

The MLOps Blog

JUNE 27, 2023

This includes features for data labeling, data versioning, data augmentation, and integration with popular data storage systems. Collaboration and version control : Support collaboration among data and ML teams, allowing them to share code, models, and experiments.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Snorkel AI

MAY 28, 2024

Bonito converts unannotated text from specialized domains into synthetic instruction-tuning datasets. You can watch a lightly-edited version below—and I recommend it; the Snorkelers asked some great questions! We identified templates in NLP datasets that reason over passages. Here’s an example of how this works. on a test set.

Natural Language Processing 52

Natural Language Processing Large Language Models Machine Learning Data Scientist 52

Marktechpost

MAY 18, 2024

This equilibrium guarantees that the model can carry out intricate tasks without necessitating the enormous computational resources that are generally linked with large-scale models. When compared against benchmarks, the Yi-1.5-34B 34B model has shown remarkable performance. 34B model is a great development in Artificial Intelligence.

Artificial Intelligence 123

Artificial Intelligence Artificial Intelligence AI AI 123

Marktechpost

APRIL 14, 2024

Let’s explore the key features, advantages, and disadvantages, culminating in a comparative table summarizing their differences and similarities. It excels in handling large datasets and provides extensive customization options, making it a favorite for data analysts seeking to delve deep into analytics.

Business Intelligence 79

Business Intelligence AI AI Text Analytics 79

Unite.AI

MARCH 21, 2024

However, as these models grow in size, the computational requirements for training and inference become increasingly demanding, pushing against the limits of available hardware resources. Finetuning and Overfitting : MoE models tend to overfit more easily during finetuning, especially when the downstream task has a relatively small dataset.

Large Language Models 147

Large Language Models Neural Network Natural Language Processing LLM 147

AWS Machine Learning Blog

FEBRUARY 29, 2024

You can add data to Amazon Personalize in bulk by importing large historical datasets all at once from an Amazon Simple Storage Service (Amazon S3) CSV file, using a format required by Amazon Personalize. Create a dataset group. Create datasets and schemas. Create a solution and a solution version.

Auto-complete 85

Auto-complete AI AI ML 85

Unite.AI

MARCH 13, 2024

These models, trained on massive datasets, have demonstrated an impressive ability to understand and generate human-like text, unlocking new possibilities across various domains. Training from scratch : Alternatively, DSLMs can be trained entirely from scratch using domain-specific datasets.

Large Language Models 204

Large Language Models Natural Language Processing LLM Data Scarcity 204

Unite.AI

SEPTEMBER 19, 2023

For instance, accessing the fine-tuning capabilities of the GPT-4 comes at a premium, requiring a paid subscription that is relatively more expensive compared to other options available in the market. However, it is essential to note that not all fine-tuning avenues are created equal.

Large Language Models 298

Large Language Models LLM Neural Network Prompt Engineer 298

Marktechpost

MAY 7, 2024

However, making accurate AD requires a lot of resources, such as special expertise, equipment, and significant time investment. The proposed method is tested using the MAD dataset, which includes a rich collection of over 264,000 audio descriptions from 488 movies. vs 13.4), respectively.

Automation 94

Automation Large Language Models LLM Artificial Intelligence 94

Marktechpost

FEBRUARY 7, 2024

This includes the code used for training and evaluating the model, the datasets used for training, and comprehensive documentation of the architecture and development process. OLMo has been made available in several versions, the current models out of which are 1B and 7B parameter models, with a bigger 65B version in the works.

Natural Language Processing 104

Natural Language Processing Artificial Intelligence Artificial Intelligence NLP 104

AWS Machine Learning Blog

MAY 13, 2024

These metrics will assess how well a machine-generated summary compares to one or more reference summaries. This post then seeks to assess whether prompt engineering is more performant for clinical NLP tasks compared to the RAG pattern and fine-tuning. Dataset The MIMIC Chest X-ray (MIMIC-CXR) Database v2.0.0

Generative AI 99

Generative AI Prompt Engineer Prompt Engineering LLM 99

Marktechpost

FEBRUARY 27, 2024

Visual instruction datasets focus on simple questions and improving fundamental abilities rather than complex reasoning. The answers in these datasets often need to be longer, uninformative, and require polishing or regeneration. This method aims to provide a more resource-efficient solution without compromising on performance.

Big Data 74

Big Data Automation ML Large Language Models 74

Marktechpost

MAY 3, 2024

There are 1273 test cases, 10178 training instances, and 1272 development examples in the MedQA dataset. The researchers used the same format, training data, and training code for all the models to ensure they could compare them fairly. A comparable hyperparameter sweep was used to find the optimal values.

Large Language Models 85

Large Language Models NLP ML AI 85

Marktechpost

JANUARY 9, 2024

In the context of language inclusivity, they leverage a model trained specifically on low-resource languages. They combine this model with the LLM, granting them access to its advanced generation and reasoning abilities, resulting in notably enhanced performance for translation and arithmetic reasoning tasks in low-resource languages.

Large Language Models 97

Large Language Models Machine Learning LLM AI 97

Towards AI

SEPTEMBER 28, 2023

▢ [Automation] How are the new versions rolled out and the process to compare them against the running version? (A/B Collaboration] How can multiple data scientists understand the impact of their version before releasing it? (A/B Suitable for use cases with large datasets that are available upfront.

Automation 78

Automation Data Scientist ML Machine Learning 78

Marktechpost

MARCH 4, 2024

While LLMs excel in generating diverse datasets and refining prompts, their accuracy hinges on correctly interpreting user intentions. Despite advancements, prompt sensitivity remains a hurdle, especially in proprietary models where version changes can alter behavior significantly. Check out the Paper and Github.

Prompt Engineer 82

Prompt Engineer Prompt Engineering LLM Automation 82

Marktechpost

JULY 17, 2023

Current LLMs and their development focus on English and resource-rich languages. POLYLM has been built using a massive dataset of 640B tokens from publically accessible sources, including Wikipedia, mC4, and CC-100. The team has also developed MULTIALPACA, a multilingual instruction dataset, for the supervised fine-tuning (SFT) phase.

Large Language Models 97

Large Language Models LLM Natural Language Processing Artificial Intelligence 97

Unite.AI

DECEMBER 6, 2023

This updated version is trained on more data, gives fewer wrong answers, and understands complex instructions better. Comparative Performance In terms of general benchmarks, open-source LLMs have shown remarkable progress. Similarly, Yi-34B, developed from scratch, stood out with scores comparable to GPT-3.5-turbo

LLM 276

LLM OpenAI ChatGPT AI 276

Marktechpost

NOVEMBER 5, 2023

Gathering the correct dataset and data mixture is a tedious task that requires a lot of time, resources, and money. released RedPajama-1T in March this year, a 5TB dataset—more than 190,000 times and have been using them in imaginative ways. Researchers from Together.ai CommonCrawl is the main emphasis of RedPajama-V2.

Large Language Models 121

Large Language Models LLM Categorization AI 121

AWS Machine Learning Blog

NOVEMBER 30, 2023

A leaderboard allows you to compare key performance metrics (for example, accuracy, precision, recall, and F1 score) for different models’ configurations to identify the best model for your data, thereby improving transparency into model building and helping you make informed decisions on model choices.

Machine Learning 83

Machine Learning Neural Network Algorithm Auto-classification 83

Heartbeat

FEBRUARY 13, 2024

The best resource to monitor the current status of open source LLMS is Hugging Face Open LLM Leaderboard, where the open source LLMs are ranked based on various evaluations. Fine-tuning LLMs: LLM fine-tuning is one of the exciting areas where we curate the dataset specific to our needs and tune the LLM models built by the providers.

Data Scientist 52

Data Scientist LLM Deep Learning Prompt Engineer 52

AWS Machine Learning Blog

MARCH 15, 2024

However, the datasets needed to build the ML models and give reliable results are sitting in silos across different healthcare systems and organizations. In the training phase, a global FL model is disseminated and synchronized between unit organizations for training on individual datasets, and a local trained model is returned.

ML 92

ML Data Scientist Natural Language Processing Machine Learning 92

Viso.ai

MARCH 29, 2024

Deployment can take various forms, such as integrating the model into existing applications, using it in a batch process for large datasets, or making it available as a service via an API. Providing features such as collaboration and model versioning. This helps organize your experiments and compare runs within the same context.

Machine Learning 52

Machine Learning ML Automation Data Scientist 52