marktechpost21h
Reinforcement Learning
Developing compact yet high-performing language models remains a significant challenge in artificial intelligence. Large-scale models often require extensive computational resources, making them ...
marktechpost21h
Generative AI
Developing compact yet high-performing language models remains a significant challenge in artificial intelligence. Large-scale models often require extensive computational resources, making them ...
marktechpost3d
NVIDIA AI Releases Eagle2 Series Vision-Language Model: Achieving SOTA Results Across Various Multimodal Benchmarks
Vision-Language Models (VLMs) have significantly expanded AI’s ability to process multimodal information, yet they face persistent challenges. Proprietary models such as GPT-4V and Gemini-1.5-Pro ...
marktechpost3d
TensorLLM: Enhancing Reasoning and Efficiency in Large Language Models through Multi-Head Attention Compression and Tensorisation
LLMs based on transformer architectures, such as GPT and LLaMA series, have excelled in NLP tasks due to their extensive parameterization and large training datasets. However, research indicates that ...
marktechpost4d
Qwen AI Introduces Qwen2.5-Max: A large MoE LLM Pretrained on Massive Data and Post-Trained with Curated SFT and RLHF Recipes
The field of artificial intelligence is evolving rapidly, with increasing efforts to develop more capable and efficient language models. However, scaling these models comes with challenges, ...
marktechpost3d
Meta AI Introduces MR.Q: A Model-Free Reinforcement Learning Algorithm with Model-Based Representations for Enhanced Generalization
Reinforcement learning (RL) trains agents to make sequential decisions by maximizing cumulative rewards. It has diverse applications, including robotics, gaming, and automation, where agents interact ...
marktechpost5d
This AI Paper Introduces IXC-2.5-Reward: A Multi-Modal Reward Model for Enhanced LVLM Alignment and Performance
Artificial intelligence has grown significantly with the integration of vision and language, allowing systems to interpret and generate information across multiple data modalities. This capability ...
marktechpost5d
Test-Time Preference Optimization: A Novel AI Framework that Optimizes LLM Outputs During Inference with an Iterative Textual Reward Policy
Large Language Models (LLMs) have become an indispensable part of contemporary life, shaping the future of nearly every conceivable domain. They are widely acknowledged for their impressive ...
marktechpost5d
DeepSeek-AI Releases Janus-Pro 7B: An Open-Source multimodal AI that Beats DALL-E 3 and Stable Diffusion
Multimodal AI integrates diverse data formats, such as text and images, to create systems capable of accurately understanding and generating content. By bridging textual and visual data, these models ...
marktechpost5d
Building a Retrieval-Augmented Generation (RAG) System with DeepSeek R1: A Step-by-Step Guide
With the release of DeepSeek R1, there is a buzz in the AI community. The open-source model offers some best-in-class performance across many metrics, even at par with state-of-the-art proprietary ...
marktechpost4d
Qwen AI Releases Qwen2.5-VL: A Powerful Vision-Language Model for Seamless Computer Interaction
In the evolving landscape of artificial intelligence, integrating vision and language capabilities remains a complex challenge. Traditional models often struggle with tasks requiring a nuanced ...
marktechpost7d
Alibaba Researchers Propose VideoLLaMA 3: An Advanced Multimodal Foundation Model for Image and Video Understanding
Advancements in multimodal intelligence depend on processing and understanding images and videos. Images can reveal static scenes by providing information regarding details such as objects, text, and ...