Kamal Reader

Table of Links Abstract and 1. Introduction 2 Architectural details and 2.1 Sparse Mixture of Experts 3 Results 3.1 Multilingual benchmarks, 3.2 Long range performance, and 3.3 Bias Benchmarks 4 Instruction Fine-tuning 5 Routing analysis 6 Conclusion, Acknowledgements, and References 4 Instruction Fine-tuning We train Mixtral – Instruct using supervised fine-tuning (SFT) on an instruction … Read more

Table of Links Abstract and 1. Introduction 2 Architectural details and 2.1 Sparse Mixture of Experts 3 Results 3.1 Multilingual benchmarks, 3.2 Long range performance, and 3.3 Bias Benchmarks 4 Instruction Fine-tuning 5 Routing analysis 6 Conclusion, Acknowledgements, and References 3.1 Multilingual benchmarks Compared to Mistral 7B, we significantly upsample the proportion of multilingual data … Read more

Table of Links Abstract and 1. Introduction 2 Architectural details and 2.1 Sparse Mixture of Experts 3 Results 3.1 Multilingual benchmarks, 3.2 Long range performance, and 3.3 Bias Benchmarks 4 Instruction Fine-tuning 5 Routing analysis 6 Conclusion, Acknowledgements, and References 3 Results We compare Mixtral to Llama, and re-run all benchmarks with our own evaluation … Read more

Table of Links Abstract and 1. Introduction 2 Architectural details and 2.1 Sparse Mixture of Experts 3 Results 3.1 Multilingual benchmarks, 3.2 Long range performance, and 3.3 Bias Benchmarks 4 Instruction Fine-tuning 5 Routing analysis 6 Conclusion, Acknowledgements, and References 2 Architectural details Mixtral is based on a transformer architecture [31] and uses the same … Read more

:::info Authors: (1) Albert Q. Jiang; (2) Alexandre Sablayrolles; (3) Antoine Roux; (4) Arthur Mensch; (5) Blanche Savary; (6) Chris Bamford; (7) Devendra Singh Chaplot; (8) Diego de las Casas; (9) Emma Bou Hanna; (10) Florian Bressand; (11) Gianna Lengyel; (12) Guillaume Bour; (13) Guillaume Lample; (14) Lélio Renard Lavaud; (15) Lucile Saulnier; (16) Marie-Anne … Read more

Hello, writers! We love fall, but the last thing we want is for you to fall out of your writing habits lol! One of the best ways to create content that truly resonates with your audience is to ask them directly what they want to see. You can ensure your content aligns with their interests … Read more

Alright, folks, let’s talk about the paradox of AI. I mean, yeah, sure—AI can’t exactly replace us. We’ve all read those headlines: “AI Takes Jobs,” “Chatbots to Replace Customer Service Reps,” and the occasional “AI Now Writes Better Love Letters Than You Ever Did” (not that it’s hard, buddy, you just haven’t been loved enough). … Read more

Recently came an interesting task, changing custom SVG, to a Webflow project for the animation. I thought it was no big deal, opened a Figma, took the source code of the SVG, threw it in and …. ==didn’t work== 😔 Researching the problem, it turned out that the library that animates SVG works directly with … Read more

Hey there! In case you missed it, the #AI-chatbot writing contest has been going strong for a little over two months now, since it kicked off on August 6, 2024. In the spirit of the AI season, Coze and HackerNoon teamed up to invite all AI enthusiasts, developers, and writers to build personalized AI chatbots … Read more

The LLM Knowledge Graph Builder is one of Neo4j’s GraphRAG Ecosystem Tools that empowers you to transform unstructured data into dynamic knowledge graphs. It is integrated with a Retrieval-Augmented Generation (RAG) chatbot, enabling natural language querying and explainable insights into your data. Get Started With GraphRAG: Neo4j’s Ecosystem Tools What Is the Neo4j LLM Knowledge Graph Builder? The Neo4j LLM … Read more