A monitoring tool for Meshtastic MQTT root topic in the Sacramento Valley, California. Offgrid mesh nodes network with options to view messages, positions, node info, telemetry, traceroute, and neighbor info.
A review of Meshtastic, a cheap, encrypted, offgrid communicator using T-Beam devices. The review includes both positive and negative aspects of the project.
T-Beam Meshtastic is a wireless module with ESP32, LoRa, GPS, WiFi, and Bluetooth capabilities. It features a 0.96-inch OLED display and supports various frequency bands including 433/868/915/923Mhz.
Sergey Pletenev et al. explore the integration of new knowledge into Large Language Models (LLMs) using Low-Rank Adaptation (LoRA). The study focuses on fine-tuning the Llama-3.1-8B-instruct model with varying amounts of new information while aiming to retain previously learned knowledge. The researchers found that mixing known and new facts in training data yields the best results but also noted potential drawbacks, such as a decline in performance on external benchmarks and a bias towards overrepresented answers when the data is skewed. Additionally, the model sometimes becomes overly confident and hesitant to answer. These findings emphasize the need for careful consideration of training data composition and tuning parameters to balance the incorporation of new knowledge with maintaining overall model capabilities.
This tutorial guides readers on how to fine-tune the Mistral 7B large language model using QLoRA with the Axolotl library, focusing on managing limited GPU resources for efficient training. It covers environment setup, dataset creation, configuration of QLoRA hyperparameters, the fine-tuning process, and testing the fine-tuned model.
Nick Farrow has created MeshBoard, a text-based bulletin board system inspired by the BBSes of the 1970s and 1980s, running on a Raspberry Pi using the Meshtastic mesh network. The project allows for menu navigation and interactive games like Tic Tac Toe and an Escape Room, with no internet required. It leverages Python to create a modular and easily extensible platform, with plans to expand features such as file transfer over the Meshtastic network.
The article explores techniques to improve Large Language Model (LLM) accuracy, focusing on Lamini Memory Tuning. It discusses fine-tuning methods like Low-Rank Adaptation (LoRA), the advantages and disadvantages of fine-tuning, and practical steps using Lamini to achieve higher precision in SQL query generation. The author demonstrates a step-by-step approach to creating a high-quality dataset, fine-tuning, and evaluating model accuracy.
MeshCom is an open-source project for exchanging text messages, positions, and data using low-power, low-cost LoRa radio modules. It aims to create off-grid, mesh network communication systems.
This article provides a comprehensive guide on fine-tuning the Llama 3.1 language model using Unsloth for efficient parameter-efficient training. It covers concepts like supervised fine-tuning, LoRA, QLoRA, and practical steps for training on a high-quality dataset.
A light-weight codebase that enables memory-efficient and performant finetuning of Mistral's models. It is based on LoRA, a training paradigm where most weights are frozen and only 1-2% additional weights in the form of low-rank matrix perturbations are trained.
