This information is sourced from company announcements, related news, public research reports, and social media networks, and does not constitute investment advice regarding the industries and stocks mentioned in the text. If there are any copyright infringements or violations of information disclosure, please leave a message in the background to contact for deletion. New Year’s … Read more
Introduction From many people’s perspective, DeepSeek’s intensive release of multimodal open-source models before the Spring Festival aims to capitalize on the momentum to take away “ClosedAI”. However, when I checked GitHub, I found that the previous Janus Flow was already several months old, and this Pro version is merely an “ordinary” upgrade for them. It … Read more
Introduction to Deepseek Janus-Pro Multimodal Framework Overview Introduction Janus-Pro is a novel self-regressive framework that unifies the capabilities of multimodal understanding and generation. By decomposing visual encoding into independent channel processing while still employing a single, unified transformer architecture for computation, Janus-Pro addresses the limitations of previous methods. This decoupling not only alleviates the role … Read more
Machine Heart Reports Editor: Panda Currently, there are numerous providers offering deep learning services. When using these services, users need to send their information included in the prompt to these providers, which can lead to privacy leakage issues. On the other hand, service providers are generally unwilling to disclose the model parameters they have painstakingly … Read more
↑ ClickBlue Text Follow the Jishi platform Author丨Jarvis73@Zhihu (Authorized) Source丨https://zhuanlan.zhihu.com/p/601905339 Editor丨Jishi Platform Jishi Guide This article summarizes Prompt Learning/Tuning. >> Join the Jishi CV technology exchange group to stay at the forefront of computer vision Since the advent of Self-Attention and Transformer, they have become the new stars in the field of natural language processing. … Read more
Paper Link:https://arxiv.org/pdf/2412.15115 Github Code: https://github.com/QwenLM/Qwen2.5 The technical report of the Qwen2.5 series large language model launched by Alibaba Cloud has been released, covering improvements in model architecture, pre-training, post-training, evaluation, and more. Today, we will provide a simple interpretation. Summary: 1. Core Insights 1.1. Model Improvements ● Architecture and Tokenizer: The Qwen2.5 series includes dense … Read more
Shadows slant across the shallow water, a faint fragrance drifts in the moonlight at dusk. Hello everyone, I am the little girl selling hot dry noodles. I am very happy to share cutting-edge technology and thoughts in the field of artificial intelligence with my friends. Following the previous shares in the same series: Qwen Series … Read more
Click the “CVer” above to add it to your “Favorites” list. Essential insights delivered promptly. This article is reprinted from: Smarter Since the introduction of the Transformer, it has dominated the NLP field. However, its impact in the CV domain has been moderate, with initial thoughts suggesting it was unsuitable for CV until recently. A … Read more
The Transformer architecture has become a major component of the success of large language models (LLMs). To further improve LLMs, new architectures that may outperform the Transformer architecture are being developed. One such approach is Mamba, a state space model. The paper “Mamba: Linear-Time Sequence Modeling with Selective State Spaces” introduces Mamba, which we have … Read more
This article summarizes the attention mechanism in natural language processing (NLP) in a Q&A format and provides an in-depth analysis of the Transformer. Table of Contents 1. Analysis of Attention Mechanism1. Why introduce the attention mechanism?2. What types of attention mechanisms are there? (How are they classified?)3. What is the computational process of the attention … Read more