Meshnology offers a range of IoT and Meshtastic related products including ESP32 LoRa modules, batteries, antennas, development boards, sensors, and cases.
A user has generated higher zoom level map tiles for Meshtastic 2.6, based on OpenStreetMap data, and is sharing download links for various regions (World, Europe, USA, Canada, South America, Oceania). They also suggest formatting the SD card with a small block size (8K with FAT32) for optimal performance.
Meshtastic is an open-source project that provides long-range, off-grid communication using inexpensive LoRa radios. It allows users to send text messages, GPS locations, and voice messages over long distances without relying on internet or mobile networks.
It is a 4-inch touchscreen device designed for Meshtastic®, powered by dual MCUs, the ESP32 and RP2040, and supports Wi-Fi, BLE, and LoRa®. It is an open-source, powerful IoT development platform.
The Meshtastic 2.6 Preview introduces major new features including the Meshtastic UI (MUI) for standalone devices, next-hop routing for direct messages, and InkHUD for e-ink devices. These updates aim to enhance user experience, improve routing efficiency, and maintain device data integrity. The release is in preview stage to gather feedback and ensure robust performance.
The T-Deck is a compact device with a 2.8-inch IPS LCD touch screen, integrated keyboard, trackball, microphone, and speaker, running on an ESP32-S3 dual-core processor. It supports various frequencies and includes a GPS module and battery in its Plus variant. It features a U.FL/IPEX antenna connector for LoRa and can be flashed using Espressif's firmware download mode.
Bandpass filter for 915 MHz center frequency suitable for LoRa, LoRaWAN, GSM / 3G with SMA-male and SMA-female connectors. Enhances receiver sensitivity, mitigates interference, and aids in frequency planning.
A look at this year’s crop of LoRA alternatives, including SVF, SVFT, MiLoRA, PiSSA, and LoRA-XS, all based on SVD (Singular Value Decomposition). The article compares these techniques to the original LoRA method for fine-tuning Large Language Models.
| Method | Description | Key Feature(s) | Reference |
|---|---|---|---|
| LoRA | Freezes the model and trains a small pair of low-rank “adapter” matrices. | Saves memory and compute cycles by reducing the number of trainable parameters. | arxiv.org/abs/2106.09685 |
| SVF | Uses SVD on the model’s weight matrices and fine-tunes the singular values directly. | More economical in parameters than LoRA; makes tuned models composable. | arxiv.org/abs/2501.06252v2 |
| SVFT | Adds more trainable weights on the diagonal and evaluates various alternatives. | Provides more trainable values than just the diagonal, useful for better fine-tuning. | arxiv.org/abs/2405.19597 |
| PiSSA | Tunes only the large principal values. | Designed to approximate full fine-tuning by adapting the principal singular components. | arxiv.org/abs/2404.02948 |
| MiLoRA | Tunes only the small principal values. | Retains base model’s knowledge while adapting to new tasks. | arxiv.org/abs/2406.09044 |
| LoRA-XS | Similar to PiSSA but with a slightly different mechanism. | Shows good results with significantly fewer parameters than LoRA. | arxiv.org/abs/2405.17604 |
| DoRA | Splits weights into magnitudes and directions then tunes those. | arxiv.org/abs/2402.09353 | |
| AdaLoRA | Complex mechanism for finding the best tuning rank for a given budget of trainable weights. | arxiv.org/abs/2303.10512 |
Meshtastic radio settings overview, including frequency bands, data rates, and custom settings for various regions.
Emojis can add a whole new level of personalization and fun to your Meshtastic devices. Learn how to customize your Short Names, add waypoints, and display expressive messages on OLED screens.
