Marktechpost

OCTOBER 18, 2024

Large language models (LLMs) have revolutionized how machines process and generate human language, but their ability to reason effectively across diverse tasks remains a significant challenge. Finally, the LLM uses these reasoning structures to create detailed reasoning paths.

Large Language Models 112

Large Language Models Inference Engine AI AI 112

Marktechpost

OCTOBER 21, 2024

Accelerating inference in large language models (LLMs) is challenging due to their high computational and memory requirements, leading to significant financial and energy costs. Check out the Paper , Model Series on Hugging Face , and GitHub. Don’t Forget to join our 50k+ ML SubReddit.

Large Language Models 112

Large Language Models Inference Engine AI AI 112

Marktechpost

OCTOBER 27, 2024

Formal theorem proving has emerged as a critical benchmark for assessing the reasoning capabilities of large language models (LLMs), with significant implications for mathematical automation. Don’t Forget to join our 55k+ ML SubReddit.

Large Language Models 100

Large Language Models Metadata Inference Engine Automation 100

Marktechpost

OCTOBER 17, 2024

One of the biggest hurdles organizations face is implementing Large Language Models (LLMs) to handle intricate workflows effectively. Enterprises struggle with the cumbersome nature of configuring LLMs for seamless collaboration across data sources, making it challenging to adopt them for operational efficiency.

Large Language Models 108

Large Language Models Inference Engine Automation Data Scientist 108

Marktechpost

OCTOBER 22, 2024

Despite recent advances in multimodal large language models (MLLMs), the development of these models has largely centered around English and Western-centric datasets. PANGEA is trained on a newly curated dataset, PANGEAINS, which contains 6 million instruction samples across 39 languages.

Large Language Models 107

Large Language Models Data Scarcity Inference Engine LLM 107

Marktechpost

OCTOBER 17, 2024

Large Language Models (LLMs) have demonstrated remarkable proficiency in In-Context Learning (ICL), which is a technique that teaches them to complete tasks using just a few examples included in the input prompt and no further training. The team has shared their primary contributions as follows. Llama-3, and Qwen.

Large Language Models 108

Large Language Models Inference Engine LLM ML 108

Marktechpost

OCTOBER 18, 2024

Recent advancements in Large Language Models (LLMs) have reshaped the Artificial intelligence (AI)landscape, paving the way for the creation of Multimodal Large Language Models (MLLMs). It also provides multilingual support for languages such as English and Chinese.

Large Language Models 111

Large Language Models Natural Language Processing Inference Engine LLM 111

Marktechpost

FEBRUARY 21, 2025

SGLang is an open-source inference engine designed by the SGLang team to address these challenges. It optimizes CPU and GPU resources during inference, achieving significantly higher throughput than many competitive solutions. RadixAttention is central to SGLang, which reuses shared prompt prefixes across multiple requests.

Inference Engine 83

Inference Engine LLM Large Language Models Metadata 83

Marktechpost

OCTOBER 14, 2024

The problem with efficiently linearizing large language models (LLMs) is multifaceted. The quadratic attention mechanism in traditional Transformer-based LLMs, while powerful, is computationally expensive and memory-intensive. Don’t Forget to join our 50k+ ML SubReddit.

LLM 119

LLM Large Language Models Inference Engine AI 119

Marktechpost

OCTOBER 17, 2024

This requirement has prompted researchers to find effective ways to integrate real-time data and contextual understanding into Large Language Models (LLMs), which have difficulty interpreting real-world tasks. The IoT-LLM framework consists of these three steps: 1. Don’t Forget to join our 50k+ ML SubReddit.

LLM 121

LLM Inference Engine Large Language Models Machine Learning 121

Marktechpost

OCTOBER 17, 2024

There is a need for flexible and efficient adaptation of large language models (LLMs) to various tasks. Existing approaches, such as mixture-of-experts (MoE) and model arithmetic, struggle with requiring substantial tuning data, inflexible model composition, or strong assumptions about how models should be used.

LLM 112

LLM Algorithm AI Researcher AI Research 112

Marktechpost

OCTOBER 15, 2024

Large language models (LLMs) have become crucial in natural language processing, particularly for solving complex reasoning tasks. These models are designed to handle mathematical problem-solving, decision-making, and multi-step logical deductions. Check out the Paper.

Machine Learning 116

Machine Learning LLM AI Researcher AI Research 116

Marktechpost

OCTOBER 16, 2024

Large language models (LLMs) have revolutionized various fields by enabling more effective data processing, complex problem-solving, and natural language understanding. Specifically, while LLMs are becoming capable of handling longer input sequences, the increase in retrieved information can overwhelm the system.

LLM 108

LLM AI Researcher AI Research Inference Engine 108

Marktechpost

OCTOBER 15, 2024

The ever-increasing size of Large Language Models (LLMs) presents a significant challenge for practical deployment. Despite their transformative impact on natural language processing, these models are often hindered by high memory transfer requirements, which pose a bottleneck during autoregressive generation.

LLM 108

LLM Natural Language Processing Inference Engine Large Language Models 108

Unite.AI

SEPTEMBER 24, 2024

NVIDIA Inference Microservices (NIM) and LangChain are two cutting-edge technologies that meet these needs, offering a comprehensive solution for deploying AI in real-world environments. Understanding NVIDIA NIM NVIDIA NIM, or NVIDIA Inference Microservices, is simplifying the process of deploying AI models.

Inference Engine 162

Inference Engine Large Language Models AI AI 162

Marktechpost

OCTOBER 15, 2024

The challenge lies in generating effective agentic workflows for Large Language Models (LLMs). Despite their remarkable capabilities across diverse tasks, creating workflows that combine multiple LLMs into coherent sequences is labor-intensive, which limits scalability and adaptability to new tasks.

Artificial Intelligence 124

Artificial Intelligence Artificial Intelligence Automation Inference Engine 124

Marktechpost

OCTOBER 18, 2024

Large language models (LLMs) have demonstrated significant reasoning capabilities, yet they face issues like hallucinations and the inability to conduct faithful reasoning. GCR introduces a trie-based index named KG-Trie to integrate KG structures directly into the LLM decoding process.

LLM 112

LLM Inference Engine Large Language Models AI 112

Marktechpost

OCTOBER 25, 2024

Accurate assessment of Large Language Models is best done with complex tasks involving long input sequences. While examining the performance of capable LLMs on tasks involving long context lengths, researchers noticed a few underlying problems. Relative position introduces a bias in LLMs, thus affecting their performance.

Large Language Models 61

Large Language Models Inference Engine Explainability LLM 61

Bugra Akyildiz

FEBRUARY 2, 2025

Google Research has introduced a novel framework called Chain-of-Agents (CoA) to address the challenges of long-context tasks in large language models (LLMs). LLMs have demonstrated different capabilities in various tasks such as reasoning, knowledge retrieval, and generation.

Inference Engine 59

Inference Engine LLM Large Language Models OpenAI 59

Marktechpost

OCTOBER 15, 2024

Large Language Models (LLMs) need to be evaluated within the framework of embodied decision-making, i.e., the capacity to carry out activities in either digital or physical environments. In conclusion, the Embodied Agent Interface offers a thorough framework for evaluating LLM performance in tasks involving embodied AI.

Large Language Models 57

Large Language Models Inference Engine LLM AI 57

Marktechpost

JUNE 8, 2023

There are rising worries about the potential negative impacts of large language models (LLMs), such as data memorization, bias, and unsuitable language, despite LLMs’ widespread praise for their capacity to generate natural-sounding text. Regular Expression engine for LMs, or ReLM for short.

Large Language Models 108

Large Language Models LLM Inference Engine AI 108

NVIDIA

APRIL 9, 2024

Teams from the companies worked closely together to accelerate the performance of Gemma — built from the same research and technology used to create Google DeepMind’s most capable model yet, Gemini — with NVIDIA TensorRT-LLM , an open-source library for optimizing large language model inference, when running on NVIDIA GPUs.

AI Developer 134

AI Developer AI Development Generative AI Inference Engine 134

Marktechpost

OCTOBER 19, 2024

Large language models (LLMs) like GPT-4, Gemini, and Llama 3 have revolutionized natural language processing through extensive pre-training and supervised fine-tuning (SFT). However, these models come with high computational costs for training and inference. Check out the Paper.

Large Language Models 112

Large Language Models Natural Language Processing Inference Engine LLM 112

Marktechpost

OCTOBER 16, 2024

Large language models (LLMs) have gained widespread adoption due to their advanced text understanding and generation capabilities. By combining these features, AutoDAN-Turbo represents a significant advancement in the field of automated jailbreak attacks against large language models.

Large Language Models 112

Large Language Models Inference Engine LLM Algorithm 112

Marktechpost

OCTOBER 2, 2024

Large language models (LLMs) have advanced significantly in recent years. The need to make LLMs more accessible on smaller and resource-limited devices drives the development of more efficient frameworks for model inference and deployment.

LLM 65

LLM Python Large Language Models Inference Engine 65

Marktechpost

OCTOBER 24, 2024

In PROVE, researchers use a high-fidelity scene graph representation constructed from hyper-detailed image captions and employ a large language model (LLM) to generate diverse question-answer (QA) pairs along with executable programs to verify each QA pair. This approach allows the creation of a benchmark dataset of 10.5k

AI Researcher 118

AI Researcher AI Research Inference Engine Large Language Models 118

Marktechpost

OCTOBER 18, 2024

Current evaluation frameworks, such as LLM-as-a-Judge, which uses large language models to judge outputs from other AI systems, must account for the entire task-solving process. These models often overlook intermediate stages, crucial for agentic systems because they mimic human-like problem-solving strategies.

Large Language Models 108

Large Language Models LLM AI Developer AI Development 108

Marktechpost

OCTOBER 25, 2024

Large Language Models (LLMs) have potential applications in education, healthcare, mental health support, and other domains. Sometimes, even sophisticated models misunderstand instructions or depart from them, which might reduce their effectiveness, particularly in delicate situations.

LLM 101

LLM Inference Engine Large Language Models ML 101

Unite.AI

AUGUST 6, 2024

Large language models (LLMs) are increasingly utilized for complex tasks requiring multiple generation calls, advanced prompting techniques, control flow, and structured inputs/outputs. SGLang, a newly introduced system, aims to address this by providing efficient execution of complex language model programs.

LLM 130

LLM Inference Engine Auto-complete Python 130

Unite.AI

JUNE 21, 2024

Accelerating LLM Inference with NVIDIA TensorRT While GPUs have been instrumental in training LLMs, efficient inference is equally crucial for deploying these models in production environments. Accelerating LLM Training with GPUs and CUDA. 122 ~/local 1 Verify the installation: ~/local/cuda-12.2/bin/nvcc

Deep Learning 130

Deep Learning Neural Network Convolutional Neural Networks Large Language Models 130

Towards AI

JULY 1, 2024

The deployment of these super powerful models into production environments is NOT easy and time efficient. As we are halfway there to 2025, companies have to move beyond API calling pretrained Large Language Models and go deep into thinking about deploying these full-scale models into production environment.

Generative AI 98

Generative AI Inference Engine Large Language Models OpenAI 98

Marktechpost

OCTOBER 21, 2024

Large language models (LLMs) can understand and generate human-like text across various applications. However, despite their success, LLMs often need help in mathematical reasoning, especially when solving complex problems requiring logical, step-by-step thinking. Don’t Forget to join our 50k+ ML SubReddit.

Large Language Models 112

Large Language Models Inference Engine AI AI 112

NVIDIA

JUNE 12, 2024

This is the kind of horsepower needed to handle AI-assisted digital content creation, AI super resolution in PC gaming, generating images from text or video, querying local large language models (LLMs) and more. LLM performance is measured in the number of tokens generated by the model. Source: Jan.ai

LLM 105

LLM Generative AI AI AI 105

Marktechpost

OCTOBER 18, 2024

Traditionally, large language models (LLMs) used for building TTS pipelines convert speech to text using automatic speech recognition (ASR), process it using an LLM, and then convert the output back to speech via TTS. If you like our work, you will love our newsletter. Don’t Forget to join our 50k+ ML SubReddit.

Inference Engine 119

Inference Engine Large Language Models AI AI 119

Marktechpost

OCTOBER 22, 2024

A recent approach, Reinforcement Learning from Human Feedback (RLHF), has brought remarkable improvements to large language models (LLMs) by incorporating human preferences into the training process. GenRM leverages a large pre-trained LLM to generate reasoning chains that help decision-making.

Inference Engine 115

Inference Engine Large Language Models LLM AI 115

Marktechpost

OCTOBER 20, 2024

This is indeed a very serious challenge for the larger and more effective scaling of LLM modalities applied to real-world applications. These results further establish CREAM as a strong solution to the alignment problem by providing a scalable and effective method for optimizing large language models.

Inference Engine 107

Inference Engine LLM Large Language Models ML 107

Marktechpost

OCTOBER 21, 2024

As large language models (LLMs) become increasingly capable and better day by day, their safety has become a critical topic for research. To create a safe model, model providers usually pre-define a policy or a set of rules. The adapted strategy first produces an LLM that is easily controllable for safety.

Large Language Models 104

Large Language Models Inference Engine LLM AI 104