This article provides a step-by-step guide on fine-tuning the Llama 3 language model for customer service use cases. It covers the process of data preparation, fine-tuning techniques, and the benefits of leveraging LLMs in customer service applications.
Learn how to fine-tune large language models like Llama 3 for function calling, enabling interaction with external tools and APIs for tasks like web search and math operations.
Explore the top small language models of 2024, including Llama 3, Phi 3, Mixtral 8x7B, Gemma, and OpenELM. Learn about their features, benefits, and significance in the AI landscape.
This article guides you through the process of building a local RAG (Retrieval-Augmented Generation) system using Llama 3, Ollama for model management, and LlamaIndex as the RAG framework. The tutorial demonstrates how to get a basic local RAG system up and running with just a few lines of code.
This article provides a step-by-step guide on building a generative search engine for local files using Qdrant, NVidia NIM API, or Llama 3. It includes system design, indexing local files, and creating a user interface.
Learn how to build an open LLM app using Hermes 2 Pro, a powerful LLM based on Meta's Llama 3 architecture. This tutorial explains how to deploy Hermes 2 Pro locally, create a function to track flight status using FlightAware API, and integrate it with the LLM.
LlamaFS is a self-organizing file manager that automatically renames and organizes files based on their contents. It supports various file types and even images and audio. It can run in two modes - batch mode and watch mode. In batch mode, LlamaFS suggests a file structure and organizes files. In watch mode, it watches your directory and proactively learns your file organization habits. The project is built on a Python backend and Electron for the frontend.
This article discusses the latest open LLM (large language model) releases, including Mixtral 8x22B, Meta AI's Llama 3, and Microsoft's Phi-3, and compares their performance on the MMLU benchmark. It also talks about Apple's OpenELM and its efficient language model family with an open-source training and inference framework. The article also explores the use of PPO and DPO algorithms for instruction finetuning and alignment in LLMs.
