Unite.AI

SEPTEMBER 13, 2024

As the demand for large language models (LLMs) continues to rise, ensuring fast, efficient, and scalable inference has become more crucial than ever. NVIDIA's TensorRT-LLM steps in to address this challenge by providing a set of powerful tools and optimizations specifically designed for LLM inference.

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Marktechpost

JANUARY 20, 2024

Stable AI has recently released a new state-of-the-art model, Stable-Code-3B , designed for code completion in various programming languages with multiple additional capabilities. Stable-Code-3B is an auto-regressive language model based on the transformer decoder architecture. The model is a follow-up on the Stable Code Alpha 3B.

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Large Language Models Auto-complete LLM Natural Language Processing 128

IBM Journey to AI blog

SEPTEMBER 19, 2023

Auto-generated code suggestions can increase developers’ productivity and optimize their workflow by providing straightforward answers, handling routine coding tasks, reducing the need to context switch and conserving mental energy. It can also modernize legacy code and translate code from one programming language to another.

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Towards AI

NOVEMBER 15, 2024

Generated with Microsoft Designer With the second anniversary of the ChatGPT earthquake right around the corner, the rush to build useful applications based on large language models (LLMs) of its like seems to be in full force. I believe they are highly relevant to other LLM based applications just as much.

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LLM Auto-complete Software Engineer Automation 116

Unite.AI

SEPTEMBER 11, 2023

Last time we delved into AutoGPT and GPT-Engineering , the early mainstream open-source LLM-based AI agents designed to automate complex tasks. Enter MetaGPT — a Multi-agent system that utilizes Large Language models by Sirui Hong fuses Standardized Operating Procedures (SOPs) with LLM-based multi-agent systems.

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Marktechpost

JANUARY 23, 2024

The KV cache is not removed from the radix tree when a generation request is completed; it is kept for both the generation results and the prompts. In the second scenario, compiler optimizations like code relocation, instruction selection, and auto-tuning become possible. The researchers used Hugging Face TGI v1.3.0, advice v0.1.8,

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Unite.AI

MAY 27, 2024

Another innovative technique is the Tree of Thoughts (ToT) prompting, which allows the LLM to generate multiple lines of reasoning or “thoughts” in parallel, evaluate its own progress towards the solution, and backtrack or explore alternative paths as needed.

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Marktechpost

JUNE 19, 2024

MixEval Achieving a balance between thorough user inquiries and effective grading systems is necessary for evaluating LLMs. Conventional standards based on ground truth and LLM-as-judge benchmarks encounter difficulties such as biases in grading and possible contamination over time.

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Large Language Models Auto-complete Chatbots LLM 140

Marktechpost

OCTOBER 18, 2023

Large language models (LLMs) such as ChatGPT and Llama have garnered substantial attention due to their exceptional natural language processing capabilities, enabling various applications ranging from text generation to code completion. We are also on WhatsApp. Join our AI Channel on Whatsapp.

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Artificial Intelligence Artificial Intelligence LLM AI Researcher 120

Towards AI

MAY 6, 2024

Since Meta released the latest open-source Large Language Model (LLM), Llama3, various development tools and frameworks have been actively integrating Llama3. Compared to traditional auto-completion tools, Copilot produces more detailed and intelligent code.

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Unite.AI

FEBRUARY 11, 2025

Future AGIs proprietary technology includes advanced evaluation systems for text and images, agent optimizers, and auto-annotation tools that cut AI development time by up to 95%. Enterprises can complete evaluations in minutes, enabling AI systems to be optimized for production with minimal manual effort.

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Marktechpost

MAY 1, 2024

Although many LLM acceleration methods aim to decrease the number of non-zero weights, sparsity is the quantity of bits divided by weight. In addition, speculative decoding is a common trend in LLM acceleration. The researchers use an example prompt to examine what occurs in each tier of an LLM to support their approach.

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Marktechpost

NOVEMBER 7, 2023

It will be necessary to expand the capabilities of current code completion tools—which are presently utilized by millions of programmers—to address the issue of library learning to solve this multi-objective optimization. Figure 1: The LILO learning loop overview. (Al)

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NVIDIA

MARCH 18, 2024

For example, an Avatar configurator can allow designers to build unique, brand-inspired personas for their cars, complete with customized voices and emotional attributes. Li Auto unveiled its multimodal cognitive model, Mind GPT, in June.

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Unite.AI

MAY 29, 2024

Evaluating LLM Performance The challenge of evaluating LLMs' performance is met with a strategic approach, incorporating task-specific metrics and innovative evaluation methodologies. Image and Document Processing Multimodal LLMs have completely replaced OCR.

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Data Extraction Neural Network Large Language Models NLP 273

Flipboard

NOVEMBER 20, 2023

Retrieval Augmented Generation (RAG) allows you to provide a large language model (LLM) with access to data from external knowledge sources such as repositories, databases, and APIs without the need to fine-tune it. There are two models in this implementation: the embeddings model and the LLM that generates the final response.

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Marktechpost

NOVEMBER 24, 2024

However, the efficiency of LLMs in real-world deployment remains a challenge due to their substantial resource demands, particularly in tasks requiring sequential token generation. One promising solution is Speculative Decoding (SD), a method designed to accelerate LLM inference without compromising generated output quality.

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LLM AI Researcher AI Research Auto-complete 87

Mlearning.ai

JULY 4, 2023

This is probably the easiest way to run an LLM for free on your PC Created using Midjourney. If you would like to be able to test different LLMs locally for free and happen to have a GPU powered PC at home you’re in luck — thanks to the wonderful Open Source community, running different LLMs on Windows is very straightforward.

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LLM Auto-complete Python ML 98

Marktechpost

JUNE 15, 2024

This limitation hinders the advancement of LLM capabilities and their application in diverse, real-world scenarios. Existing methods for generating instruction datasets fall into two categories: human-curated data and synthetic data produced by LLMs. The model then generates diverse user queries based on these templates.

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Prompt Engineer Prompt Engineering Auto-complete Large Language Models 134

NVIDIA

MARCH 6, 2024

AI development is always oriented around developing systems that perform tasks that would otherwise require human intelligence, and often significant levels of input, to complete — only at speeds beyond any individual’s or group’s capabilities. Magic Mask has completely changed that workflow. It’s up to 4.5x faster on RTX vs. Mac.

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Flipboard

JUNE 13, 2023

For instance, we’ve used LLM models, including ChatGPT, with a fair amount of success to assist with internal tasks like migrating from one programming language to another, helping developers understand legacy code written by other colleagues, or writing functions for converting data formats. .”

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Generative AI Auto-complete Software Engineer AI 181

Marktechpost

JUNE 4, 2023

Augmented LLMs are the ones that are added with external tools and skills in order to increase their performance so that they perform beyond their inherent capabilities. Applications like Auto-GPT for autonomous task execution have been made possible by Augmented Language Models (ALMs) only.

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AssemblyAI

FEBRUARY 21, 2025

LLM-powered meeting summaries This tutorial shows how to use our dedicated AI summarization model. If you want to see how to generate meeting summaries with LLMs, see our related blog. Virtual meetings have become a cornerstone of modern work, but reviewing lengthy recordings can be time-consuming.

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Python Auto-complete LLM AI 130

Marktechpost

MARCH 16, 2024

Auto-parallelization: This feature enables the system to optimize the execution of large workflows, further improving computational performance. The system has achieved a more than 15% improvement in CPU/Memory utilization and a 17% increase in the workflow completion rate.

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AWS Machine Learning Blog

MARCH 28, 2024

This technique provides targeted yet broad-ranging search capabilities, furnishing the LLM with a wider perspective. It tackles the issue of information overload and irrelevant data processing head-on, leading to improved response quality, more cost-effective LLM operations, and a smoother overall retrieval process.

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IBM Journey to AI blog

NOVEMBER 24, 2023

Generating configuration management inputs (for CMDB)and changing management inputs based on release notes generated from Agility tool work items completed per release are key Generative AI leverage areas. The ability to generate insights for security validation (from application and platform logs, design points, IAC, etc.)

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Marktechpost

APRIL 16, 2024

LangChain is an open-source framework that allows developers to build LLM-based applications easily. It provides for easily connecting LLMs with external data sources to augment the capabilities of these models and achieve better results. It teaches how to build LLM-powered applications using LangChain using hands-on exercises.

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Large Language Models Auto-complete Prompt Engineer Prompt Engineering 104

The MLOps Blog

SEPTEMBER 26, 2024

TL;DR Hallucinations are an inherent feature of LLMs that becomes a bug in LLM-based applications. This “making up” event is what we call a hallucination, a term popularized by Andrej Karpathy in 2015 in the context of RNNs and extensively used nowadays for large language models (LLMs). What are LLM hallucinations?

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LLM Prompt Engineering Prompt Engineer Auto-complete 72

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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Large Language Models LLM Prompt Engineer Prompt Engineering 70

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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AWS Machine Learning Blog

MAY 17, 2024

FMs and LLMs, even though they’re pre-trained, can continue to learn from data inputs or prompts during inference. A prompt is the information you pass into an LLM to elicit a response. To accomplish the overall task, your application feeds each subtask prompt to the LLM in a pre-defined order or according to a set of rules.

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Generative AI LLM Prompt Engineering Prompt Engineer 137

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. What Is a Large Language Model?

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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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AWS Machine Learning Blog

APRIL 8, 2024

This version offers support for new models (including Mixture of Experts), performance and usability improvements across inference backends, as well as new generation details for increased control and prediction explainability (such as reason for generation completion and token level log probabilities).

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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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AWS Machine Learning Blog

JANUARY 7, 2025

However, the industry is seeing enough potential to consider LLMs as a valuable option. The following are a few potential benefits: Improved accuracy and consistency LLMs can benefit from the high-quality translations stored in TMs, which can help improve the overall accuracy and consistency of the translations produced by the LLM.

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Marktechpost

JANUARY 12, 2025

Researchers want to create a system that eventually learns to bypass humans completely by completing the research cycle without human involvement. Fudan University and the Shanghai Artificial Intelligence Laboratory have developed DOLPHIN, a closed-loop auto-research framework covering the entire scientific research process.

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Auto-classification Automation Auto-complete BERT 84