#lora

LoRA and QLoRA have democratized fine-tuning of open-source LLMs. This article explains how these methods work, when they are useful, and why prompting and RAG should be considered first.

TL;DR: llama-server's RAM prompt cache (--cache-ram, on by default) saves a slot's KV when another...

Modern LLMs have billions of parameters. Fine-tuning all of them for a new task means: Storing full...

IronEar's ESP32-S3 Sensor Hears: An open-source ESP32-S3 sensor node classifies 500+ household sounds on-device and reports to Home Assistant over MQTT or a private LoRa mesh.

Fine-Tuning LLMs with LoRA & QLoRA: Complete Practical Guide Master...

Fine-Tuning LLMs on Consumer Hardware: LoRA Guide 2024 Train custom models on your GPU...

Off-Grid LoRa Message Board: A battery-powered desk display that receives text messages over a LoRa mesh network with no WiFi needed, plus a built-in CO2 and humidity sensor.

For years, the charm of LoRa mesh networking has been its stubborn refusal to behave like ordinary...

When it helps, when RAG beats it, and why most teams never need it.

TL;DR: A PEFT LoRA adapter can give individual modules their own rank and alpha through rank_pattern...

A new EMNLP demonstration system uses teacher-generated examples to train a compact task-specific adapter from a natural-language specification, reporting 83.6% semantic accuracy on a difficult subset where a fast compiler achieved 22.4% mean LEM.

When an AI model fails to understand a company's specialized requirements, the instinct is often to...