--- title: Best LoRA posts of 2025 | daily.dev description: The most upvoted LoRA posts of 2025, curated by the daily.dev community. canonical: https://daily.dev/tags/lora/best-of/2025 twitter:card: summary_large_image twitter:site: @dailydotdev og:url: https://daily.dev/tags/lora/best-of/2025 og:type: website og:site_name: daily.dev og:title: Best LoRA posts of 2025 | daily.dev og:description: The most upvoted LoRA posts of 2025, curated by the daily.dev community. og:image: https://media.daily.dev/image/upload/s--VAY5ToZt--/f_auto/v1724209435/public/daily.dev%20-%20open%20graph --- # Best of LoRA — 2025 1. 1 [](/posts/my-first-impressions-of-meshcore-off-grid-messaging-3qdngsnoq "My First Impressions of MeshCore Off-Grid Messaging") Article Michael Lynch · 44w My First Impressions of MeshCore Off-Grid Messaging MeshCore enables encrypted text messaging over long-range (LoRa) radios without cellular infrastructure, forming mesh networks where devices relay messages through each other. Testing three devices (Heltec v3, SenseCAP T-1000e, and Lilygo T-Deck+) revealed significant usability challenges: confusing onboarding, poor UI/UX design, limited range (only 5 blocks in suburban areas), and critical components being closed-source despite appearing open. While the concept appeals for emergency communication, the technology isn't yet practical for non-technical users or reliable disaster scenarios. 66 6 2. 2 [](/posts/fine-tuning-gemma-3-270m-locally-3mpyadrpo "Fine-tuning Gemma 3 270M Locally") Article Daily Dose of Data Science | Avi Chawla | Substack · 1y Fine-tuning Gemma 3 270M Locally Google's Gemma 3 270M model can be fine-tuned locally using just 0.5 GB RAM. The tutorial demonstrates using Unsloth and HuggingFace transformers to fine-tune the model for chess move prediction. The process involves loading the model, configuring LoRA for efficient training, preparing a chess dataset, and training with decreasing loss. After fine-tuning, the model successfully predicts missing chess moves instead of generating random moves. 28 3. 3 [](/posts/lora-fine-tuning-flux-1-dev-on-consumer-hardware-wsd0uwpjp "(LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware") Article Hugging Face · 1y (LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware QLoRA enables fine-tuning of FLUX.1-dev diffusion models on consumer hardware with under 10GB VRAM by combining 4-bit quantization with Low-Rank Adaptation. The approach uses bitsandbytes for quantization, 8-bit AdamW optimizer, gradient checkpointing, and cached latents to dramatically reduce memory usage from \~120GB to \~9GB. Training on RTX 4090 takes 41 minutes for 700 steps, while FP8 training with torchao on H100 reduces time to 20 minutes. The technique maintains high-quality results while making advanced model customization accessible to developers without enterprise-grade hardware. 11 [See all LoRA archives](/tags/lora/best-of) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@graph":[{"@type":"CollectionPage","@id":"https://daily.dev/tags/lora/best-of/2025#page","url":"https://daily.dev/tags/lora/best-of/2025","name":"Best LoRA Posts of 2025","description":"The most upvoted LoRA posts of 2025, curated by the daily.dev community.","isPartOf":{"@type":"WebSite","url":"https://daily.dev"}},{"@type":"ItemList","@id":"https://daily.dev/tags/lora/best-of/2025#items","numberOfItems":3,"itemListElement":[{"@type":"ListItem","position":1,"url":"https://daily.dev/posts/my-first-impressions-of-meshcore-off-grid-messaging-3qdngsnoq","name":"My First Impressions of MeshCore Off-Grid Messaging"},{"@type":"ListItem","position":2,"url":"https://daily.dev/posts/fine-tuning-gemma-3-270m-locally-3mpyadrpo","name":"Fine-tuning Gemma 3 270M Locally"},{"@type":"ListItem","position":3,"url":"https://daily.dev/posts/lora-fine-tuning-flux-1-dev-on-consumer-hardware-wsd0uwpjp","name":"(LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware"}]},{"@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Tags","item":"https://daily.dev/tags"},{"@type":"ListItem","position":3,"name":"LoRA","item":"https://daily.dev/tags/lora"},{"@type":"ListItem","position":4,"name":"Best of","item":"https://daily.dev/tags/lora/best-of"},{"@type":"ListItem","position":5,"name":"2025"}]}]} ```