Learn how to deploy a private instance of Llama 3.2 with a Retriever-Augmented Generation (RAG) API using Lightning AI Studios, enabling you to leverage large language models in a secure and customizable environment.
A step-by-step guide to run Llama3 locally with Python. Discusses the benefits of running local LLMs, including data privacy, cost-effectiveness, customization, offline functionality, and unrestricted use.
The author explains their decision to build a home lab using Raspberry Pis, Kubernetes, and 3D printing, providing reasons such as gaining exposure, experimenting with complex architectures, becoming a T-shaped engineer, and the cost-effectiveness of DIY projects.
Rundeck is an open source automation service that allows users to easily run automation tasks across a set of nodes. This repository contains the source code for Rundeck, with features such as a web console, command line tools, and a WebAPI. It is built with Gradle and requires Java 1.8 and NodeJs 16. Documentation, development guides, and issue tracker are available.
OliveTin is a web-based interface that provides safe and simple access to predefined shell commands. It offers a responsive, touch-friendly UI, dark mode, accessibility, container integration, and more. It allows you to run complex commands, give access to commands to less technical people, and simplify command execution on various devices.
llm-tool provides a command-line utility for running large language models locally. It includes scripts for pulling models from the internet, starting them, and managing them using various commands such as 'run', 'ps', 'kill', 'rm', and 'pull'. Additionally, it offers a Python script named 'querylocal.py' for querying these models. The repository also come
- Discusses the use of consumer graphics cards for fine-tuning large language models (LLMs)
- Compares consumer graphics cards, such as NVIDIA GeForce RTX Series GPUs, to data center and cloud computing GPUs
- Highlights the differences in GPU memory and price between consumer and data center GPUs
- Shares the author's experience using a GeForce 3090 RTX card with 24GB of GPU memory for fine-tuning LLMs
- Compares consumer graphics cards, such as NVIDIA GeForce RTX Series GPUs, to data center and cloud computing GPUs
- Highlights the differences in GPU memory and price between consumer and data center GPUs
- Shares the author's experience using a GeForce 3090 RTX card with 24GB of GPU memory for fine-tuning LLMs
Resource-efficient LLMs and Multimodal Models
A useful survey of resource-efficient LLMs and multimodal foundations models.
Provides a comprehensive analysis and insights into ML efficiency research, including architectures, algorithms, and practical system designs and implementations.
A useful survey of resource-efficient LLMs and multimodal foundations models.
Provides a comprehensive analysis and insights into ML efficiency research, including architectures, algorithms, and practical system designs and implementations.
