DeepCoder-14B: Superior Open-Source LLM

Not all AI breakthroughs come wrapped in billion-dollar hype. Some just quietly show up, crush benchmarks, and leave researchers wondering how something so small can be so powerful. That’s DeepCoder-14B in a nutshell. What is DeepCoder-14B? DeepCoder-14B is a 14-billion...

April 11, 2025

European AI allies unveils LLM alternative to Big Tech, DeepSeek

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European AI allies unveils LLM alternative to Big Tech, DeepSeek

As China’s DeepSeek threatens to dismantle Silicon Valley’s AI monopoly, a European alliance has emerged with an alternative to tech’s global order. They call their project OpenEuroLLM. Like DeepSeek, they aim to develop next-generation open-source language models — but their...

February 3, 2025

Increased LLM Vulnerabilities from Fine-tuning and Quantization: Appendix | HackerNoon

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Increased LLM Vulnerabilities from Fine-tuning and Quantization: Appendix | HackerNoon

Authors: (1) Divyanshu Kumar, Enkrypt AI; (2) Anurakt Kumar, Enkrypt AI; (3) Sahil Agarwa, Enkrypt AI; (4) Prashanth Harshangi, Enkrypt AI. Table of Links Abstract and 1 Introduction 2 Problem Formulation and Experiments 3 Experiment Set-up & Results 4 Conclusion...

October 17, 2024

Decoding LLM Parameters, Part 2: Top-P – DZone

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Decoding LLM Parameters, Part 2: Top-P – DZone

LLM Parameters Like any machine learning model, large language models have various parameters that control the variance of the generated text output. We have started a multi-part series to explain the impact of these parameters in detail. We will conclude...

October 9, 2024

From RAGs to Riches: An In-Depth Look at Retrieval-Augmented Generation

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From RAGs to Riches: An In-Depth Look at Retrieval-Augmented Generation

Machine learning has evolved dramatically over the past few years, with large language models (LLMs) at the forefront. However, as with any powerful technology, optimizing LLMs can be a real headache. In this blog post, we’ll dig into what the...

September 25, 2024

Decoding LLM Parameters, Part 1: Temperature – DZone

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Decoding LLM Parameters, Part 1: Temperature – DZone

September 20, 2024

LLM: Code Generation, Debugging, CI/CD Integration – DZone

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LLM: Code Generation, Debugging, CI/CD Integration – DZone

With AI, the domain of software development is experiencing a breakthrough phase with the continuous integration of state-of-the-art Large Language Models like GPT-4 and Claude Opus. These models extend beyond the role of traditional developer tools to directly assist developers...

August 15, 2024

5 Ways to Boost Growth with Gen AI for Business

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5 Ways to Boost Growth with Gen AI for Business

The world of business is undergoing a revolutionary shift, driven by the transformative power of Generative AI (Gen AI). What once seemed like a futuristic concept is now a crucial tool for businesses seeking to innovate and scale. According to...

August 9, 2024

金融业专家边聊:MongoDB Atlas矢量搜索实际用户案例(搜索内部PDF文档)

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金融业专家边聊:MongoDB Atlas矢量搜索实际用户案例(搜索内部PDF文档)

用例1: 使用矢量搜索和LLM的PDF搜索应用程序检索增强型生成(RAG) 问题: PDF是非结构化数据难以搜索领先的理赔调查员审查合同和理赔需要高效和准确性来解决这一历史性的繁琐任务总结文件内容指示信息来源挑战: 承保人和理赔调查员要浏览大量的指南、合同和报告页面耗时导致昂贵的错误(风险估算不准确) 详情: 根据用户所在国家提供PDF 矢量搜索查询特定文档过滤掉无关的PDF 索引预筛选: 必须索引具有数据类型的字段只包括具有正确数据类型的记录专用搜索节点架构: 同时支持Atlas搜索和矢量搜索工作负载用例2: 使用LLM和矢量搜索的理赔管理,用于RAG 挑战: 理赔调查员从不同格式的系统中提取和汇总信息内容共享平台客户信息被锁定在遗留的客户关系管理系统中与理赔有关的图片和语音报告非结构化数据简化操作解决方案: Atlas矢量搜索大语言模型(LLM) 检索增强型生成(RAG) 矢量搜索查询: 获取矢量嵌入和纯文本元数据不需要检索数据聚合管道:...

August 5, 2024

Wondering what to watch? Explore movies using LLM

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Wondering what to watch? Explore movies using LLM

Background (The Story) How often do you wonder what to watch?especially on weekends!There are so many good movies to pick from! I got you covered! Find similar movies quick and easy! With LLM we get results which are not just...

July 23, 2024

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