Best of LLM — May 2024
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GoPenAI·2y
Advanced RAG with Self-Correction | LangGraph | No Hallucination | Agents | GROQ
Learn how to make Large Language Models smarter and more reliable with Advanced Retrieval-Augmented Generation (RAG) using LangGraph. Build an Adaptive RAG application that auto-critiques itself, integrates powerful agents, and reduces latency on LLM responses leveraging GROQ.
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Medium·2y
Large Language Model (LLM) Stack — Version 6
The post discusses the current market trends and updates in the Large Language Model (LLM) Stack. It mentions the interest in private/self hosting of models, productivity hubs, the growth of RAG, fine-tuning LLMs and SLMs, and expanding functionality of default LLMs. It also highlights the vulnerability of higher-level stack products and the release of new products and features by LLM providers.
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Machine Learning News·2y
ScrapeGraphAI: A Web Scraping Python Library that Uses LLMs to Create Scraping Pipelines for Websites, Documents, and XML Files
ScrapeGraphAI is an advanced web scraping library that simplifies data collection using large language models (LLMs) and a unique direct graph logic. It minimizes the time and technical skills required for web scraping projects, allowing users to focus more on analyzing the extracted data.
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KDnuggets·2y
5 Steps to Learn AI for Free in 2024
Learn AI for free in 2024 with these 5 steps. Start by learning Python and then take free courses from Harvard, Google, and more. Understand the basics of Large Language Models and fine-tune them for specific tasks. Also, learn Git and GitHub for effective code management and collaboration. Don't forget to work on projects, stay updated with AI trends, and join a community to deepen your knowledge and skills.
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Elia: An Open Source Terminal UI for Interacting with LLMs
Elia is an open source terminal UI application that allows users to interact with large language models directly from their terminal. It offers a fast and easy-to-use solution, supporting both proprietary and local models.
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Community Picks·2y
Go or Rust? Just Listen to the Bots
The post describes the journey of building conversational bots with voices using Go and Rust. The author shares their inspiration for the project, discusses the design implementation details, and provides code snippets for both the Go and Rust implementations.
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Machine Learning Mastery·2y
5 Essential Free Tools for Getting Started with LLMs
This post introduces 5 essential free tools for getting started with LLMs: Transformers, LlamaIndex, Langchain, Ollama, and Llamafile. Each tool has its own unique set of tasks, advantages, and features to help beginners grasp the subtleties of LLM development and interact with it.
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State of “Function Calling” in LLM
LLM introduces function calling capabilities in the Chat Completions API. It allows users to describe functions in an API call and receive a JSON object as output. This feature provides data privacy and unlimited connectivity with external tools and APIs. There are multiple ways to implement function calling in LLM, such as using the OpenAI Python Client, vLLM, and the Function Calling Generation Model.
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Towards Data Science·2y
From Prompt Engineering to Agent Engineering
The post introduces the concept of transitioning from prompt engineering to agent engineering, exploring the key ideas and precepts of agent engineering. It outlines the Agent Engineering Framework, which includes sections on Agent Capabilities Requirements and Agent Engineering & Design. The framework focuses on defining the jobs and actions of AI agents, identifying the required capabilities and proficiency levels, and mapping the required proficiencies to technologies and techniques. The post concludes by emphasizing the importance of a strong and flexible foundation for agent design and engineering.
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gitconnected·2y
Python and LLM for Market Analysis — Part III — Allow your trading System to react for Daily News
This post explores the integration of news sentiment and technical indicators in a trading system. It highlights the potential of Language Models (LLMs) in finance and provides a step-by-step guide on how to extract news articles, summarize them using LLMs, perform sentiment analysis, and build a trading strategy based on news sentiment and technical indicators.
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It's Foss·2y
14 Top Open Source LLMs For Research and Commercial Use
Large Language Models (LLMs) are machine learning models that aim to solve language problems. Open-source LLMs offer benefits like transparency, no vendor lock-in, and customization. Some examples include Falcon 180B, Dolly 2.0, and Cerebras-GPT.
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Hacker News·2y
metaskills/experts: Experts.js is the easiest way to create and deploy OpenAI's Assistants and link them together as Tools to create advanced Multi AI Agent Systems with expanded memory and attention
Experts.js simplifies the usage of OpenAI's Assistants API by removing the complexity of managing Run objects and allowing Assistants to be linked together as Tools. It introduces Assistants as Tools, enabling the creation of Multi AI Agent Systems. Threads are used as a managed context window for agents.
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Closed as unhelpful: an elegy for Stack Overflow
Stack Overflow, despite its flaws, has been a valuable resource for programmers seeking help. However, the rise of LLMs poses a threat to its relevance. The article discusses the challenges faced by Stack Overflow and the potential impact of LLMs in providing programming solutions.
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Python and LLM for Stock Market Analysis Part IV — ElasticSearch for Stock Symbol/Ticker accuracy
This post discusses the use of ElasticSearch for obtaining accurate stock symbols/tickers in stock market analysis. It explains the limitations of using LLM/NLP models alone and introduces ElasticSearch as an alternative. It also provides a step-by-step guide for setting up ElasticSearch and indexing stock data, as well as integrating it with Yahoo Finance API for symbol lookup. The post highlights the benefits of using ElasticSearch's fuzzy search feature and addresses potential issues with symbol identification.
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Substack·2yGraphRAG: Design Patterns, Challenges, Recommendations
GraphRAG enhances traditional RAG method by integrating knowledge graphs with large language models, providing more accurate and relevant answers to user queries. It offers various architectures and presents challenges in implementing and maintaining a knowledge graph.
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Inside One of the Most Important Papers of the Year: Anthropic’s Dictionary Learning is a Breakthrough Towards Understanding LLMs
Anthropic's dictionary learning approach aims to improve the interpretability of LLMs by identifying recurring neuron activation patterns. The technique uses sparse autoencoders to decompose model activations into more interpretable pieces. Features discovered through dictionary learning include concepts like the Golden Gate Bridge and immunology-related clusters.
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Langfuse : OpenSource LLM Tracking Tool
Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications. It offers simplified self-hosting, custom dashboards, prompt management, traces and sessions, monitoring, integrations, exports, and datasets.
