--- title: LoRA News & Updates | daily.dev description: LoRA news and updates covering Low-Rank Adaptation, a method for fine-tuning models by training small adapter weights instead of the full network. Readers can learn about rank and hyperparameter choices, training cost and memory savings, merging and serving adapters. canonical: https://daily.dev/tags/lora twitter:card: summary_large_image twitter:site: @dailydotdev og:url: https://daily.dev/tags/lora og:type: website og:site_name: daily.dev og:title: LoRA News & Updates | daily.dev og:description: LoRA news and updates covering Low-Rank Adaptation, a method for fine-tuning models by training small adapter weights instead of the full network. Readers can learn about rank and hyperparameter choices, training cost and memory savings, merging and serving adapters. og:image: https://api.daily.dev/og/tags/lora.png og:image:width: 1200 og:image:height: 630 --- ## Recommended LoRA stories ## Who to follow for LoRA [](https://daily.dev/cnxsoft) [CNXSoft](https://daily.dev/cnxsoft) [@cnxsoft](https://daily.dev/cnxsoft) Joined Feb 18\. 2022 370 [](https://daily.dev/huyyy) [Huyyy](https://daily.dev/huyyy) [@huyyy](https://daily.dev/huyyy) Joined Jul 25\. 2026 10 ## Top sources covering LoRA ## Most upvoted LoRA posts ## Best discussed LoRA posts ## All posts about LoRA ```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#page","url":"https://daily.dev/tags/lora","name":"LoRA News & Updates","description":"LoRA news and updates covering Low-Rank Adaptation, a method for fine-tuning models by training small adapter weights instead of the full network. Readers can learn about rank and hyperparameter choices, training cost and memory savings, merging and serving adapters.","isPartOf":{"@type":"WebSite","url":"https://daily.dev"}},{"@type":"ItemList","@id":"https://daily.dev/tags/lora#items","numberOfItems":10,"itemListElement":[{"@type":"ListItem","position":1,"url":"https://daily.dev/posts/fine-tuning-gemma-2b-for-downstream-tasks-with-keras-38lgubdfm","name":"Fine-Tuning Gemma-2B for Downstream Tasks with Keras"},{"@type":"ListItem","position":2,"url":"https://daily.dev/posts/tune-and-deploy-lora-llms-with-nvidia-tensorrt-llm-rab83f5ws","name":"Tune and Deploy LoRA LLMs with NVIDIA TensorRT-LLM"},{"@type":"ListItem","position":3,"url":"https://daily.dev/posts/efficiency-breakthroughs-in-llms-combining-quantization-lora-and-pruning-for-scaled-down-inferenc-bqopqrl8z","name":"Efficiency Breakthroughs in LLMs: Combining Quantization, LoRA, and Pruning for Scaled-down Inference and Pre-training"},{"@type":"ListItem","position":4,"url":"https://daily.dev/posts/google-ai-proposes-perl-a-parameter-efficient-reinforcement-learning-technique-that-can-train-a-rew-frsirw4h0","name":"Google AI Proposes PERL: A Parameter Efficient Reinforcement Learning Technique that can Train a Reward Model and RL Tune a Language Model Policy with LoRA"},{"@type":"ListItem","position":5,"url":"https://daily.dev/posts/an-overview-of-the-lora-family-agvhtuiyq","name":"An Overview of the LoRA Family"},{"@type":"ListItem","position":6,"url":"https://daily.dev/posts/carbon-footprint-of-llm-fine-tuning-a-case-study-j27ofp0pe","name":"Carbon Footprint of LLM Fine Tuning — A Case Study"},{"@type":"ListItem","position":7,"url":"https://daily.dev/posts/breaking-generative-ai-barriers-with-efficient-fine-tuning-techniques-k67s9lxbn","name":"Breaking Generative AI Barriers with Efficient Fine-Tuning Techniques"},{"@type":"ListItem","position":8,"url":"https://daily.dev/posts/benchmarking-latency-across-common-wireless-links-for-microcontrollers-rpdo5mt3k","name":"Benchmarking latency across common wireless links for microcontrollers"},{"@type":"ListItem","position":9,"url":"https://daily.dev/posts/how-to-fine-tune-mixtral-8x7b-instruct-on-your-own-data--vdytxv8kc","name":"How to fine-tune Mixtral-8x7B-Instruct on your own data?"},{"@type":"ListItem","position":10,"url":"https://daily.dev/posts/low-power-wi-fi-extends-signals-up-to-3-kilometers-og64kphnj","name":"Low-Power Wi-Fi Extends Signals up to 3 Kilometers"}]},{"@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"}]}]} ```