NVIDIA

MARCH 18, 2024

Talking the Talk LLMs , a form of generative AI, largely represent a class of deep-learning architectures known as transformer models , which are neural networks adept at learning context and meaning. Li Auto unveiled its multimodal cognitive model, Mind GPT, in June.

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Bugra Akyildiz

MARCH 16, 2025

ThunderMLA builds upon and substantially improves DeepSeek's FlashMLA through the implementation of a completely fused "megakernel" architecture, achieving performance gains of 20-35% across various workloads. Libraries TNN : A high-performance, lightweight neural network inference framework open sourced by Tencent Youtu Lab.

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Marktechpost

OCTOBER 1, 2024

Here are ten proven strategies to reduce LLM inference costs while maintaining performance and accuracy: Quantization Quantization is a technique that decreases the precision of model weights and activations, resulting in a more compact representation of the neural network.

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Marktechpost

JUNE 12, 2024

Word completion, next-word predictions, active auto-correction (AC), and active key correction (KC) all work together to make it easier for the user to type by correcting errors and offering multiple word candidates in the suggestion bar or inline, as well as smart compose.

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AssemblyAI

SEPTEMBER 18, 2023

Below, we'll give you the basic know-how you need to understand LLMs, how they work, and the best models in 2023. A large language model (often abbreviated as LLM) is a machine-learning model designed to understand, generate, and interact with human language. LLMs are built upon deep learning, a subset of machine learning.

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AWS Machine Learning Blog

MARCH 28, 2024

This architecture allows different parts of a neural network to specialize in different tasks, effectively dividing the workload among multiple experts. This technique provides targeted yet broad-ranging search capabilities, furnishing the LLM with a wider perspective. Mixtral-8x7B uses an MoE architecture.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Similar to the rest of the industry, the advancements of accelerated hardware have allowed Amazon teams to pursue model architectures using neural networks and deep learning (DL). From the earliest days, Amazon has used ML for various use cases such as book recommendations, search, and fraud detection.

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AWS Machine Learning Blog

OCTOBER 11, 2023

The LMI container has a powerful serving stack called DJL serving that is agnostic to the underlying LLM. It provides system-level configuration parameters that can be tuned for extracting the best performance of the hosting infrastructure for a given LLM. These cached key and value tensors are often referred to as the KV cache.

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ODSC - Open Data Science

MARCH 1, 2024

Trending AI Open Source Projects minbpe is an open-source project from famed researcher Andrej Karpathy and provides minimal, clean code for the ( Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization. Neural Flow is a Python script for plotting the intermediate layer outputs of Mistral 7B.

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Flipboard

MARCH 18, 2025

Anyspheres Cursor tool, for example, helped advance the genre from simply completing lines or sections of code to building whole software functions based on the plain language input of a human developer. Coding assistants grew considerablyboth in capability and usageduring 2024.

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The MLOps Blog

JANUARY 23, 2025

How do multimodal LLMs work? A typical multimodal LLM has three primary modules: The input module comprises specialized neural networks for each specific data type that output intermediate embeddings. Basic structure of a multimodal LLM. Different modalities are processed by separate input modules.

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AWS Machine Learning Blog

MAY 1, 2024

Llama2 by Meta is an example of an LLM offered by AWS. Llama 2 is an auto-regressive language model that uses an optimized transformer architecture and is intended for commercial and research use in English. This results in faster restarts and workload completion.

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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. LLMs’ generative abilities make them popular for text synthesis, summarization, machine translation, and more.

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The MLOps Blog

JANUARY 19, 2024

Imagine you’re facing the following challenge: you want to develop a Large Language Model (LLM) that can proficiently respond to inquiries in Portuguese. We will fine-tune different foundation LLM models on a dataset, evaluate them, and select the best model. You have a valuable dataset and can choose from various base models.

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ODSC - Open Data Science

OCTOBER 11, 2023

Even with the most advanced neural network architectures, if the training data is flawed, the model will suffer. For more complex issues like label errors, you can again simply filter out all the auto-detected bad data. Be sure to check out his talk, “ How to Practice Data-Centric AI and Have AI Improve its Own Dataset ,” there!

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The MLOps Blog

JUNE 27, 2023

Can you see the complete model lineage with data/models/experiments used downstream? Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on. LLM training configurations. Is it fast and reliable enough for your workflow?

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Mlearning.ai

JULY 9, 2023

One of the biggest challenges of using LLMs is the cost of accessing them. Many LLMs, such as OpenAI’s GPT-3, are only available through paid APIs. Learn how to deploy any open-source LLM as a free API endpoint using HuggingFace and Gradio. Many LLMs, such as OpenAI’s GPT-3, are only available through paid APIs.

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Mlearning.ai

OCTOBER 2, 2023

It completely depends on your data and the goal of the project itself. If there are too many missing pieces, then it might be hard to complete the puzzle and understand the whole picture. Autoencoder An autoencoder is a type of artificial neural network that learns how to copy things. Here’s the overview.

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Viso.ai

MAY 29, 2024

Some original Tesla features are embedded into the robot, such as a self-running computer, autopilot cameras, a set of AI tools, neural network planning , auto-labeling for objects, etc. The data from multiple sensors are combined and processed to create a complete understanding of the environment.

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John Snow Labs

JUNE 27, 2023

At their core, LLMs are built upon deep neural networks, enabling them to process vast amounts of text and learn complex patterns. Instead of navigating complex menus or waiting on hold, they can engage in a conversation with a chatbot powered by an LLM. Pythia: Pythia is a vision and language LLM developed by EleutherAI.

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Snorkel AI

MARCH 2, 2023

Ali Arsanjani, director of cloud partner engineering at Google Cloud , presented a talk entitled “Challenges and Ethics of DLM and LLM Adoption in the Enterprise” at Snorkel AI’s recent Foundation Model Virtual Summit. Others, toward language completion and further downstream tasks. Hope you can all hear me well.

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Snorkel AI

MARCH 2, 2023

Ali Arsanjani, director of cloud partner engineering at Google Cloud , presented a talk entitled “Challenges and Ethics of DLM and LLM Adoption in the Enterprise” at Snorkel AI’s recent Foundation Model Virtual Summit. Others, toward language completion and further downstream tasks. Hope you can all hear me well.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

AssemblyAI

MARCH 11, 2024

Below, we'll walk you through all the top LLM use cases and applications in 2024. What is an LLM (and what do you use it for)? LLMs operate on neural networks designed to mimic the human brain's way of learning. This LLM use case makes education more accessible and practical.

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TheSequence

JUNE 2, 2023

Large language model (LLM) hallucinations pose a big threat to the successful adoption of the new wave of LLM apps. In this post, the Galileo team dives into how one can prevent hallucinations from creeping in, as well as some metrics developed by the researchers at Galileo to quantify potential LLM hallucinations.

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Flipboard

APRIL 13, 2023

Recent advancements in ML (specifically the invention of the transformer-based neural network architecture) have led to the rise of models that contain billions of parameters or variables. To give a sense for the change in scale, the largest pre-trained model in 2019 was 330M parameters. We’ll initially have two Titan models.

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AWS Machine Learning Blog

JANUARY 9, 2025

Using deep neural networks (DNNs), Deep Instinct analyzes threats with unmatched accuracy, adapting to identify new and unknown risks that traditional methods might miss. This process is like assembling a jigsaw puzzle to form a complete picture of the malwares capabilities and intentions, with pieces constantly changing shape.

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