Edge Machine Learning

XRAG-Ollama: Enabling Lightweight Local RAG Framework Deployment

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XRAG-Ollama: Enabling Lightweight Local RAG Framework Deployment

XRAG supports comprehensive RAG evaluation benchmarks and toolkits, covering over 50 testing metrics for thorough evaluation and optimization of failure points in RAG. It supports comparisons among four types of advanced RAG modules (query rewriting, advanced retrieval, question-answering models, post-processing) and integrates various specific implementations within the modules, supporting the OpenAI large model API. The … Read more

In-Depth Analysis of RL Strategies in Mainstream Open-Source LLMs

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In-Depth Analysis of RL Strategies in Mainstream Open-Source LLMs

The author is from Meta, an internet practitioner, focusing on LLM4Code and LLMinfra. The original text is from Zhihu, link: https://zhuanlan.zhihu.com/p/16270225772 This article is for academic/technical sharing only. If there is any infringement, please contact for removal. RLHF is an important part of LLM training. With the development of open-source models, we observe that some … Read more

VideoLLaMA3: Advanced Multimodal Foundation Model

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VideoLLaMA3: Advanced Multimodal Foundation Model

Click belowCard, follow “AICV and Frontier“ Paper: https://arxiv.org/abs/2412.09262 Code: https://github.com/DAMO-NLP-SG/VideoLLaMA3 01 Introduction A more advanced multimodal foundation model for image and video understanding. The core design philosophy of VideoLLaMA3 is vision-centric: Vision-centric training paradigm Vision-centric framework design. The key point of the vision-centric training paradigm is that high-quality image-text data is crucial for understanding both … Read more

Understanding Transformers Through Llama Model Architecture

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Understanding Transformers Through Llama Model Architecture

Understanding Transformers Through Llama Model Architecture Llama Nuts and Bolts is an open-source project on GitHub that rewrites the inference process of the Llama 3.1 8B-Instruct model (80 billion parameters) from scratch using the Go language. The author is Adil Alper DALKIRAN from Turkey. If you are interested in how LLMs (Large Language Models) and … Read more

Windsurf Wave 2: Building a Comprehensive AI-Assisted Development Pipeline

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Windsurf Wave 2: Building a Comprehensive AI-Assisted Development Pipeline

Windsurf Wave 2 provides developers with powerful AI-assisted development capabilities, creating an efficient development ecosystem through three levels: knowledge acquisition, storage, and application. Knowledge Acquisition Layer Web Search has become an important source of information following private code repositories and image inputs. It matches user query intentions through natural language processing, efficiently retrieving public resources … Read more

How Front-End Developers Can Ride the Waves in AIGC

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How Front-End Developers Can Ride the Waves in AIGC

AliMei’s Guide The author has been working on AIGC-related projects since July and has gathered some insights and experiences to share with everyone. The improvement in the quality of generated images comes from the rapid development of large models and open-source plugins in the AIGC field, as well as a deeper understanding of the generation … Read more

Impact of Irrelevant Inputs on LLMs in RAG Systems

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Impact of Irrelevant Inputs on LLMs in RAG Systems

Introduction Hello everyone, I am Liu Cong from NLP. RAG (Retrieval-Augmented Generation) finds information fragments relevant to user questions through a retrieval system, utilizing large models to synthesize an answer. This greatly addresses issues such as hallucination and outdated information in large models, and has become an important means for the practical application of large … Read more

Detailed Explanation of RAG 2.0 Architecture: Building End-to-End Retrieval-Augmented Generation Systems

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Detailed Explanation of RAG 2.0 Architecture: Building End-to-End Retrieval-Augmented Generation Systems

Click on “Deephub Imba“, follow the public account, and don’t miss out on great articles! There have been many articles about Retrieval-Augmented Generation (RAG). If we could create a trainable retriever, or if the entire RAG could be customized like fine-tuning a large language model (LLM), we would definitely achieve better results. However, the current … Read more

RAG Demo in One Week, But Takes Six Months to Launch – Solutions

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RAG Demo in One Week, But Takes Six Months to Launch - Solutions

Many practitioners have found that although RAG can quickly build a demo in a short time, it faces numerous challenges in actual production environments. This article analyzes the core issue of RAG’s industrial implementation from the perspective of entrepreneurs in the AI large model field—problem grading—and discusses the challenges and solutions of four types of … Read more

Summary of Baichuan Intelligent RAG Approach: The Journey of the Baichuan Intelligent Model RAG

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Summary of Baichuan Intelligent RAG Approach: The Journey of the Baichuan Intelligent Model RAG

Happy New Year, everyone! Today, I will interpret Baichuan’s RAG approach. Baichuan Intelligent has a profound background in search; let’s see how they navigated the pitfalls of RAG! In general, Baichuan combines a long context model (192k) with search enhancement methods to address knowledge updates and reduce model hallucinations, achieving 95% accuracy on a dataset … Read more