Language models that can recognize and generate human-like text by studying patterns from vast datasets are extremely effective tools. Nevertheless, the traditional technique for training these models, known as "next-token prediction," has its shortcomings. The method trains models to predict the next word in a sequence, which can lead to suboptimal performance in more complicated…
The landscape for open-source Large Language Models (LLMs) has expanded rapidly, especially after Meta's launches of the Llama3 model and its successor, Llama 2, in 2023. Notable open-source LLMs include Mixtral-8x7B by Mistral, Alibaba Cloud’s Qwen1.5 series, Smaug by Abacus AI, and Yi models from 01.AI, which focus on data quality.
LLMs have transformed the Natural…
PyTorch has introduced TK-GEMM, an enhanced Triton FP8 GEMM (General Matrix-Matrix Multiply) kernel, designed to expedite FP8 inference for large language models (LLMs) such as Llama3. This new development responds to the struggle faced in standard PyTorch execution, where multiple kernels are launched on the GPU for each operation in LLMs, typically leading to inefficient…
Machine learning models are increasingly being used in critical applications, leading to concerns about their vulnerability to manipulation and exploitation. Once trained on a dataset, these models can retain information permanently, making them susceptible to privacy breaches, adversarial attacks, and unintended biases. There is a pressing need for techniques allowing these models to 'unlearn' specific…
The rise of machine learning has led to advancements in numerous fields, including arts, media, and the expansion of text-to-image (T2I) generative networks. These networks have the ability to produce precise images from text descriptions, presenting exciting opportunities for creators, but also triggering concerns over potential misuse such as generating harmful content. Current measures to…
In today's fast-paced world, understanding and mastering ChatGPT, a large language model, has become indispensable due to its potential to enhance productivity, boost creativity, and automate tasks. By gaining skills in ChatGPT, individuals can better navigate the shifting landscape of artificial intelligence and its applications. Here are some top ChatGPT courses to consider in 2024.
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In recent times, large language models (LLMs), such as Med-PaLM 2 and GPT-4, have shown impressive performance on clinical question-answer (QA) tasks. However, these models are restrictive due to their high costs, ecological unsustainability, and paid only accessibility for researchers. A promising approach is on-device AI, which uses local devices to run language models. This…
GitHub Copilot, an AI-powered coding assistant, is among several AI tools designed for developers' efficiency. Harnessing OpenAI's Codex language model, features include completing lines of code and aiding security checks.
Another similar tool is Amazon's CodeWhisperer, a machine-learning-driven code generator providing real-time coding recommendations. CodeWhisperer suggests snippets to entire functions, enhancing code quality, and automating repetitive…
Artificial intelligence (AI) has increasingly become a pivotal tool in the medical industry, assisting clinicians with tasks such as diagnosing patients, planning treatments, and staying up-to-date with the latest research. Despite this, current AI models face challenges in efficiently analyzing the wide array of medical data which includes images, videos and electronic health records (EHRs).…
Iterative preference optimization methods have demonstrated effectiveness in general instruction tuning tasks but haven't shown as significant improvements in reasoning tasks. Recently, offline techniques such as Discriminative Preference Optimization (DPO) have gained popularity due to their simplicity and efficiency. More advanced models advocate the iterative application of offline procedures to create new preference relations, further…
Multi-layer perceptrons (MLPs), also known as fully-connected feedforward neural networks, are foundational models in deep learning. They are used to approximate nonlinear functions and despite their significance, they have a few drawbacks. One of the limitations is that in applications like transformers, MLPs tend to control parameters and they lack interpretability compared to attention layers.…
Large Language Models (LLMs) have become crucial tools for various tasks, such as answering factual questions and generating content. However, their reliability is often questionable because they frequently provide confident but inaccurate responses. Currently, no standardized method exists for assessing the trustworthiness of their responses. To evaluate LLMs' performance and resilience to input changes, researchers…