QA-LoRA: Fine-Tune a Quantized Large Language Model on Your GPU

IBM’s PowerLM-3B and PowerMoE-3B: Revolutionizing Language Models Practical Solutions and Value IBM’s release of PowerLM-3B and PowerMoE-3B signifies a significant leap in improving the efficiency and scalability of language model training. The models are built on…

Artificial Intelligence and Its Challenges AI systems have improved significantly, but they still struggle with advanced mathematical reasoning. Currently, these models can only solve about 2% of complex math problems, showing a clear gap between AI…

“`html Practical Business Solutions with LLM-MA Systems Introduction to LLM-MA Systems LLM-based multi-agent (LLM-MA) systems allow multiple language model agents to work together on complex tasks by sharing responsibilities. These systems are beneficial in various fields…

Practical Solutions and Value of Conservative Algorithms for Zero-Shot Reinforcement Learning on Limited Data Overview: Reinforcement learning (RL) trains agents to make decisions through trial and error. Limited data can hinder learning efficiency, leading to poor…

Neural Radiance Fields (NeRF) is a neural network-based technique for capturing 3D scenes and objects from 2D images or sparse 3D data. It consists of two main components, “NeRF in” and “NeRF out” network. NeRF-based human…

Understanding Deliberative Alignment in AI Challenge in AI Safety The use of large-scale language models (LLMs) in critical areas raises a key issue: ensuring they follow ethical and safety guidelines. Current methods like supervised fine-tuning (SFT)…

Introduction to TTS Technology Text-to-Speech (TTS) systems are essential for converting written text into spoken words. This technology helps users understand complex documents, like scientific papers and technical manuals, by providing audible interaction. Challenges with Current…

A new model, MM-Grounding-DINO, is proposed by Shanghai AI Lab and SenseTime Research for unified object grounding and detection tasks. This user-friendly and open-source pipeline outperforms existing models in various domains, achieving state-of-the-art performance and setting…

Understanding AI Agents and Agentic AI Artificial intelligence has advanced significantly, evolving from simple systems to sophisticated entities capable of performing complex tasks. This article discusses two key concepts: AI Agents and Agentic AI. While they…

Practical Solutions to Reduce Large Language Model (LLM) Inference Costs Quantization Decrease precision of model weights and activations to save memory and computational resources. Pruning Remove insignificant weights to reduce neural network size without performance loss.…

The text discusses techniques to improve the efficiency of large language models (LLMs) through prompt compression, focusing on methods such as AutoCompressors and LongLLMLingua. The goal is to reduce inference costs and enable faster and accurate…

Advancing Sign Language Research with YouTube-SL-25 Practical Solutions and Value Sign language research aims to enhance technology for better understanding, translation, and interpretation of sign languages used by Deaf and hard-of-hearing communities globally. This research supports…

Project Guideline is an innovative initiative aimed at enhancing the independence of individuals with visual impairments. It leverages on-device machine learning on Google Pixel phones to enable users to walk or run independently. The system includes…

Challenges in Lesson Structuring Effective lesson structuring is a major challenge in education, especially when discussions need to focus on specific topics or problems. Teachers often struggle to manage time and organize lessons, particularly novice educators…

LLMs like GPT-4 and Llama-2, while powerful, are vulnerable to safety threats like FJAttack during fine-tuning. Researchers from multiple universities devised a Backdoor Enhanced Safety Alignment method to counter this, integrating a hidden trigger into safety…

NASA has released a ChatGPT SuperPrompt called BIDARA to guide engineers through the biomimicry design process. The process involves defining the problem, finding the equivalent challenge in nature, discovering natural models, abstracting design strategies, and emulating…