# lora

LoRA is a low-rank adaptation method and Python package for efficiently fine-tuning large language models with PyTorch.

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## M5Stack Module13.2 LoRa-1262 expansion board integrates 14x10 mm Stamp LoRa-1262 module

DevFeed: [M5Stack Module13.2 LoRa-1262 expansion board integrates 14x10 mm Stamp LoRa-1262 module](<https://devfeed.tech/articles/m5stack-module13-2-lora-1262-expansion-board-integrates-14-10-mm-stamp-lora-1262-module-14042.md>)

Original publisher: [Read original article](<https://www.cnx-software.com/2026/09/13/m5stack-module13-2-lora-1262-expansion-board-integrates-14x10-mm-stamp-lora-1262-module/>)

Author: Debashis Das

Published: 2026-09-13T05:02:54Z

Content type: news

Language: en

Sources: [CNX Software - Embedded Systems News](<https://devfeed.tech/sources/cnx-software-embedded-systems-news.md>)

Topics: [lora](<https://devfeed.tech/topics/lora.md>), [systems](<https://devfeed.tech/topics/systems.md>)

Tags: [connectivity](<https://devfeed.tech/tags/connectivity.md>), [development-board](<https://devfeed.tech/tags/development-board.md>), [embedded-systems](<https://devfeed.tech/tags/embedded-systems.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [i2c](<https://devfeed.tech/tags/i2c.md>), [io](<https://devfeed.tech/tags/io.md>), [iot](<https://devfeed.tech/tags/iot.md>), [lora](<https://devfeed.tech/tags/lora.md>), [lorawan](<https://devfeed.tech/tags/lorawan.md>), [low-power](<https://devfeed.tech/tags/low-power.md>), [lpwan](<https://devfeed.tech/tags/lpwan.md>), [m5stack](<https://devfeed.tech/tags/m5stack.md>), [module](<https://devfeed.tech/tags/module.md>), [news](<https://devfeed.tech/tags/news.md>), [power-management](<https://devfeed.tech/tags/power-management.md>), [routing](<https://devfeed.tech/tags/routing.md>), [series](<https://devfeed.tech/tags/series.md>), [smart-agriculture](<https://devfeed.tech/tags/smart-agriculture.md>), [smart-city](<https://devfeed.tech/tags/smart-city.md>), [spi](<https://devfeed.tech/tags/spi.md>), [wireless](<https://devfeed.tech/tags/wireless.md>)

### AI overview

M5Stack has launched the Stamp LoRa-1262 SMD module and the stackable Module13.2 LoRa-1262 expansion board for M5Stack Core controllers. Based on the Semtech SX1262, the module supports several modulation modes across 868-923 MHz, with up to +22 dBm transmit power and -147 dBm receive sensitivity. Module13.2 adds an RP-SMA antenna interface, M5IOE1 I/O expansion, configurable pin routing, and selectable I2C addresses for multi-module stacking.

### Source excerpt

After launching the SX1262-based M5Stamp C6LoRa tiny module, M5Stack has launched two SX1262-based LoRa add-ons for the 868-923 MHz band: the tiny Stamp LoRa-1262 SMD module for custom PCBs, and the stackable Module13.2 LoRa-1262 module for the M5Stack Core series. The Stamp LoRa-1262 supports LoRa, FSK, GFSK, MSK, GMSK, and OOK modulation, with up to +22 dBm transmit power and -147 dBm receive sensitivity. The Module13.2 adds an RP-SMA antenna interface, an M5IOE1 IO expansion chip for reset and power management, and DIP switches for flexible pin routing and configurable I2C addresses, enabling multi-module stacking. Stamp LoRa-1262 SMD module M5Stack Stamp LoRa-1262 specifications: LoRa Transceiver - Semtech SX1262 Wireless Connectivity (LoRa) Frequency Band - 868 to 923 MHz Modulation Modes - LoRa, FSK, GFSK, MSK, GMSK, OOK Bitrate - Up to 300 kbps (programmable) Transmit power (Tx) - Up to +22 dBm Receive sensitivity (Rx) - Down to -147 dBm [...] The post M5Stack Module13.2 LoRa-1262 expansion board integrates 14x10 mm Stamp LoRa-1262 module appeared first on CNX Software - Embedded Systems News.

## Autonomous LLM post-training with Tunix on TPUs

DevFeed: [Autonomous LLM post-training with Tunix on TPUs](<https://devfeed.tech/articles/autonomous-llm-post-training-with-tunix-on-tpus-4205.md>)

Original publisher: [Read original article](<https://developers.googleblog.com/autonomous-llm-post-training-with-tunix-on-tpus/>)

Author: Wei Wei

Published: 2026-09-12T11:04:33.891311Z

Content type: article

Language: en

Sources: [Google Developers Blog](<https://devfeed.tech/sources/google-developers-blog.md>)

Topics: [post-training](<https://devfeed.tech/topics/post-training.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [grpo](<https://devfeed.tech/topics/grpo.md>), [lora](<https://devfeed.tech/topics/lora.md>), [gemma](<https://devfeed.tech/topics/gemma.md>), [Google](<https://devfeed.tech/topics/google.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [Command-line interface](<https://devfeed.tech/topics/cli.md>)

Tags: [autonomous](<https://devfeed.tech/tags/autonomous.md>), [cli](<https://devfeed.tech/tags/cli.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gemma](<https://devfeed.tech/tags/gemma.md>), [git](<https://devfeed.tech/tags/git.md>), [google](<https://devfeed.tech/tags/google.md>), [grpo](<https://devfeed.tech/tags/grpo.md>), [llm](<https://devfeed.tech/tags/llm.md>), [lora](<https://devfeed.tech/tags/lora.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

This article presents autofinetune, an autonomous research loop for LLM post-training. Using AI agents and Google's AI stack, including Tunix, Gemma, Cloud TPUs, Antigravity CLI, and Gemini Flash 3.7, it automates supervised fine-tuning and reinforcement learning with GRPO, exploring hyperparameters such as LoRA configurations, learning rates, batch sizes, and rollout settings.

### Source excerpt

Imagine going to sleep after writing a single Markdown specification and waking up to find that an A...

## The Architecture for Serving 100 Fine-Tuned Models on One GPU

DevFeed: [The Architecture for Serving 100 Fine-Tuned Models on One GPU](<https://devfeed.tech/articles/the-architecture-for-serving-100-fine-tuned-models-on-one-gpu-18244.md>)

Original publisher: [Read original article](<https://blog.dailydoseofds.com/p/the-architecture-for-serving-100>)

Author: Avi Chawla

Published: 2026-09-11T21:25:15Z

Content type: tutorial

Language: en

Sources: [Daily Dose of Data Science](<https://devfeed.tech/sources/daily-dose-of-data-science.md>)

Topics: [GPU](<https://devfeed.tech/topics/gpu.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [lora](<https://devfeed.tech/topics/lora.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [code](<https://devfeed.tech/tags/code.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [lora](<https://devfeed.tech/tags/lora.md>), [memory](<https://devfeed.tech/tags/memory.md>), [models](<https://devfeed.tech/tags/models.md>), [production](<https://devfeed.tech/tags/production.md>), [serverless](<https://devfeed.tech/tags/serverless.md>), [vllm](<https://devfeed.tech/tags/vllm.md>), [workers](<https://devfeed.tech/tags/workers.md>)

### AI overview

This tutorial compares architectures for serving 100 fine-tuned 7B model variants on GPUs. It explains how separate merged models increase storage, GPU memory use, scaling pools, cold starts, and idle capacity, while a shared base model with LoRA adapters enables adapter reuse through vLLM. The article plans to test merged, unmerged startup-loaded, request-time adapter loading, and hosted-per-tenant deployments on Runpod Serverless.

### Source excerpt

...explained with code.

## Async GRPO with LoRA across HF Jobs: a bucket, a proxy, and no NCCL

DevFeed: [Async GRPO with LoRA across HF Jobs: a bucket, a proxy, and no NCCL](<https://devfeed.tech/articles/async-grpo-with-lora-across-hf-jobs-a-bucket-a-proxy-and-no-nccl-17376.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/asyncgrpo-lora-hfjobs>)

Author: Amine Dirhoussi; Quentin Gallouédec; Kashif Rasul; Sergio Paniego

Published: 2026-09-10T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [lora](<https://devfeed.tech/topics/lora.md>), [grpo](<https://devfeed.tech/topics/grpo.md>), [jobs](<https://devfeed.tech/topics/jobs.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>), [async](<https://devfeed.tech/topics/async.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>)

Tags: [async](<https://devfeed.tech/tags/async.md>), [grpo](<https://devfeed.tech/tags/grpo.md>), [hub](<https://devfeed.tech/tags/hub.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [jobs](<https://devfeed.tech/tags/jobs.md>), [llm](<https://devfeed.tech/tags/llm.md>), [lora](<https://devfeed.tech/tags/lora.md>), [nccl](<https://devfeed.tech/tags/nccl.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [rl](<https://devfeed.tech/tags/rl.md>), [storage](<https://devfeed.tech/tags/storage.md>), [trl](<https://devfeed.tech/tags/trl.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

This article describes asynchronous GRPO training with a LoRA adapter across separate Hugging Face Jobs. The adapter is synchronized to vLLM replicas through a shared Storage Bucket, while a proxy handles authentication, rollout routing, and adapter-load broadcasts. Five runs reduced the time for 500 steps from 3 hours 27 minutes to 53 minutes.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Personalize your product's text-to-speech voice for any language: Fine-tuning with Kubeflow Trainer on Red Hat OpenShift AI

DevFeed: [Personalize your product's text-to-speech voice for any language: Fine-tuning with Kubeflow Trainer on Red Hat OpenShift AI](<https://devfeed.tech/articles/personalize-your-product-s-text-to-speech-voice-for-any-language-fine-tuning-with-kubeflow-trainer-on-red-hat-openshift-ai-12350.md>)

Original publisher: [Read original article](<https://developers.redhat.com/articles/2026/09/09/text-to-speech-for-any-language-fine-tuning-with-kubeflow-trainer-on-red-hat-openshift-ai>)

Author: Dmytro Hryshchenko, Abhijeet Dhumal

Published: 2026-09-09T03:32:28Z

Content type: tutorial

Language: en

Sources: [Red Hat](<https://devfeed.tech/sources/red-hat.md>), [Red Hat Developer](<https://devfeed.tech/sources/red-hat-developer.md>)

Topics: [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [voice ai](<https://devfeed.tech/topics/voice-ai.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [distributed-training](<https://devfeed.tech/topics/distributed-training.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [lora](<https://devfeed.tech/topics/lora.md>), [data](<https://devfeed.tech/topics/data.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [distributed-training](<https://devfeed.tech/tags/distributed-training.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [lora](<https://devfeed.tech/tags/lora.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [speech](<https://devfeed.tech/tags/speech.md>), [text-to-speech](<https://devfeed.tech/tags/text-to-speech.md>), [training](<https://devfeed.tech/tags/training.md>), [voice](<https://devfeed.tech/tags/voice.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

### AI overview

This tutorial explains how to fine-tune the open source Orpheus-3B text-to-speech model for Turkish using Red Hat OpenShift AI and Kubeflow Trainer. It describes packaging distributed training in a TrainJob, scaling across nodes and GPUs, and using LoRA to keep memory usage below 16 GB. The reported result reduces speech errors by more than 90% compared with the base model.

### Source excerpt

Can't Read, Won't Buy. That is the title CSA Research gave its survey of 8,709 consumers across 29 countries, and the numbers justify it: 76% prefer to buy in their own language, and 40% will never buy in another. The same rule governs what your product says out loud. The post Personalize your product's text-to-speech voice for any language: Fine-tuning with Kubeflow Trainer on Red Hat OpenShift AI appeared first on Red Hat Developer.

## DIY ESP32-P4 weather station & air quality monitor connects to LoRa and ESP-NOW sensors, various weather APIs

DevFeed: [DIY ESP32-P4 weather station & air quality monitor connects to LoRa and ESP-NOW sensors, various weather APIs](<https://devfeed.tech/articles/diy-esp32-p4-weather-station-air-quality-monitor-connects-to-lora-and-esp-now-sensors-various-weather-apis-14023.md>)

Original publisher: [Read original article](<https://www.cnx-software.com/2026/09/03/diy-esp32-p4-weather-station-air-quality-monitor-connects-to-lora-and-esp-now-sensors-various-weather-apis/>)

Author: Jean-Luc Aufranc (CNXSoft)

Published: 2026-09-03T04:55:46Z

Content type: article

Language: en

Sources: [CNX Software - Embedded Systems News](<https://devfeed.tech/sources/cnx-software-embedded-systems-news.md>)

Topics: [ESP32-P4](<https://devfeed.tech/topics/esp32-p4.md>), [ESP32](<https://devfeed.tech/topics/esp32.md>), [ESP-NOW](<https://devfeed.tech/topics/esp-now.md>), [lora](<https://devfeed.tech/topics/lora.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [monitor](<https://devfeed.tech/topics/monitor.md>), [Embedded Systems](<https://devfeed.tech/topics/embedded-systems.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>)

Tags: [arduino](<https://devfeed.tech/tags/arduino.md>), [ble](<https://devfeed.tech/tags/ble.md>), [c-c-plus-plus](<https://devfeed.tech/tags/c-c-plus-plus.md>), [display](<https://devfeed.tech/tags/display.md>), [diy](<https://devfeed.tech/tags/diy.md>), [esp-now](<https://devfeed.tech/tags/esp-now.md>), [esp32](<https://devfeed.tech/tags/esp32.md>), [esp32-p4](<https://devfeed.tech/tags/esp32-p4.md>), [espressif](<https://devfeed.tech/tags/espressif.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [iot](<https://devfeed.tech/tags/iot.md>), [lora](<https://devfeed.tech/tags/lora.md>), [monitor](<https://devfeed.tech/tags/monitor.md>), [sensors](<https://devfeed.tech/tags/sensors.md>), [touchscreen](<https://devfeed.tech/tags/touchscreen.md>), [waveshare](<https://devfeed.tech/tags/waveshare.md>), [wifi](<https://devfeed.tech/tags/wifi.md>), [wifi-6](<https://devfeed.tech/tags/wifi-6.md>), [wireless](<https://devfeed.tech/tags/wireless.md>)

### AI overview

This article describes an ESP32-P4 weather station and air quality monitor built from off-the-shelf boards. It uses a 10.1-inch dashboard, a Sensirion SEN66 multisensor, and LoRa and ESP-NOW sensor nodes, with optional weather data from several APIs and no required cloud account.

### Source excerpt

Harald Kreuzer has built an ESP32-P4 weather station and air quality monitor that features a 10.1-inch dashboard display and a built-in Sensirion SEN66 sensor for particulate matter, CO2, VOC, and NOX, and connects to wireless LoRa and ESP-NOW sensors. The project is entirely made of off-the-shelf boards, so no custom hardware is used, and it should be relatively easy to reproduce. It doesn't require any cloud or user account, although it can connect to a choice of weather APIs (Open-Meteo, OpenWeatherMap or Visual Crossing) to display weather forecasts. The build is fully documented in a long blog post, so we'll highlight the key features of the project in this article. Hardware specifications for the main unit: Based on ESP32-P4-Module-DEV-KIT-C Credit card-sized ESP32-P4-Module-DEV-KIT SBC based on an ESP32-P4NRW32 module with ESP32-P4 with 32MB PSRAM and ESP32-C6 for WiFi and BLE 10.1-inch IPS panel with 1280 x 800 resolution, 800:1 contrast ratio, [...] The post DIY ESP32-P4 weather station & air quality monitor connects to LoRa and ESP-NOW sensors, various weather APIs appeared first on CNX Software - Embedded Systems News.

## Engineering log: fine-tuning Gemma 4 E4B with LoRA to bring FormAI's coaching on-device

DevFeed: [Engineering log: fine-tuning Gemma 4 E4B with LoRA to bring FormAI's coaching on-device](<https://devfeed.tech/articles/engineering-log-fine-tuning-gemma-4-e4b-with-lora-to-bring-formai-s-coaching-on-device-25192.md>)

Original publisher: [Read original article](<https://johnoreilly.dev/posts/formai-gemma4-lora/>)

Published: 2026-08-28T23:00:00Z

Content type: article

Language: en

Sources: [John O'Reilly](<https://devfeed.tech/sources/john-o-reilly.md>)

Topics: [gemma4](<https://devfeed.tech/topics/gemma4.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [lora](<https://devfeed.tech/topics/lora.md>), [On-device AI](<https://devfeed.tech/topics/on-device-ai.md>), [multiplatform](<https://devfeed.tech/topics/multiplatform.md>), [LiteRT](<https://devfeed.tech/topics/litert.md>), [Android](<https://devfeed.tech/topics/android.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [API](<https://devfeed.tech/topics/api.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [android](<https://devfeed.tech/tags/android.md>), [api](<https://devfeed.tech/tags/api.md>), [data](<https://devfeed.tech/tags/data.md>), [data-quality](<https://devfeed.tech/tags/data-quality.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gemini](<https://devfeed.tech/tags/gemini.md>), [gemma](<https://devfeed.tech/tags/gemma.md>), [gemma-4](<https://devfeed.tech/tags/gemma-4.md>), [inference](<https://devfeed.tech/tags/inference.md>), [kotlin-multiplatform](<https://devfeed.tech/tags/kotlin-multiplatform.md>), [litert](<https://devfeed.tech/tags/litert.md>), [lora](<https://devfeed.tech/tags/lora.md>), [multiplatform](<https://devfeed.tech/tags/multiplatform.md>), [on-device](<https://devfeed.tech/tags/on-device.md>)

### AI overview

This engineering log describes a prototype that distils FormAI's Gemini-based sports coaching feedback into Gemma 4 E4B fine-tuned with LoRA. The local pipeline uses seed videos, filters inadequate critiques, extracts frames, trains and merges the adapter, then converts the model to LiteRT-LM for possible on-device Android inference.

### Source excerpt

⚠ Note: this post is AI-generated. The text below was written by Claude, and documents findings from a series of Claude Code sessions working on this project -- the experiments, bugs and measurements described are ones that came out of those sessions. The engineering work is real and the numbers were measured rather than estimated, but the write-up is the model's own account of what it did, not a human's independent retelling of it.

## 🗓 This Week In AI Research (25-31 July 26)

DevFeed: [🗓 This Week In AI Research (25-31 July 26)](<https://devfeed.tech/articles/this-week-in-ai-research-25-31-july-26-18286.md>)

Original publisher: [Read original article](<https://www.intoai.pub/p/this-week-in-ai-research-25-31-july>)

Author: Dr. Ashish Bamania

Published: 2026-08-07T01:00:43Z

Content type: article

Language: en

Sources: [Into AI](<https://devfeed.tech/sources/into-ai.md>)

Topics: [releases](<https://devfeed.tech/topics/releases.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [deepseek](<https://devfeed.tech/topics/deepseek.md>), [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [lora](<https://devfeed.tech/topics/lora.md>), [codex](<https://devfeed.tech/topics/codex.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [deepseek](<https://devfeed.tech/tags/deepseek.md>), [lora](<https://devfeed.tech/tags/lora.md>), [ml](<https://devfeed.tech/tags/ml.md>), [moe](<https://devfeed.tech/tags/moe.md>), [performance](<https://devfeed.tech/tags/performance.md>), [releases](<https://devfeed.tech/tags/releases.md>), [research](<https://devfeed.tech/tags/research.md>)

### AI overview

A weekly roundup of AI research and releases covering DeepSeek-V4-Flash-0731, the Pangram 4 AI-text classification model, the OpenMLE system and its Frontis-MA1-35B agent, and the Metis memory foundation model.

### Source excerpt

The top 10 AI research papers and releases this week.

## Post-Train NVIDIA Cosmos 3 in One Day Using Agent Skills

DevFeed: [Post-Train NVIDIA Cosmos 3 in One Day Using Agent Skills](<https://devfeed.tech/articles/post-train-nvidia-cosmos-3-in-one-day-using-agent-skills-6922.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/post-train-nvidia-cosmos-3-in-one-day-using-agent-skills/>)

Author: Tanya Lenz

Published: 2026-07-14T16:00:00Z

Content type: tutorial

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Cosmos](<https://devfeed.tech/topics/cosmos.md>), [Agent Skills](<https://devfeed.tech/topics/agent-skills.md>), [post-training](<https://devfeed.tech/topics/post-training.md>), [lora](<https://devfeed.tech/topics/lora.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>)

Tags: [agent-skills](<https://devfeed.tech/tags/agent-skills.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [computer-vision-video-analytics](<https://devfeed.tech/tags/computer-vision-video-analytics.md>), [cosmos](<https://devfeed.tech/tags/cosmos.md>), [featured](<https://devfeed.tech/tags/featured.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [lora](<https://devfeed.tech/tags/lora.md>), [physical-ai](<https://devfeed.tech/tags/physical-ai.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>)

### AI overview

A tutorial on post-training NVIDIA Cosmos 3 Nano for video question answering with coding-agent skills, LoRA, and TAO AutoML configuration sweeps.

### Source excerpt

What if autonomous coding AI agents could push your vision reasoning models above 90% accuracy with almost no manual effort? When adapting vision reasoning...

## Quick update: May 2026

DevFeed: [Quick update: May 2026](<https://devfeed.tech/articles/quick-update-may-2026-34893.md>)

Original publisher: [Read original article](<https://pine64.org/2026/05/18/may_2026_quick/>)

Author: LoRa Left-handed operational Radio authority; Obviously

Published: 2026-05-18T00:00:00Z

Content type: news

Language: en

Sources: [Community blog on PINE64](<https://devfeed.tech/sources/community-blog-on-pine64.md>)

Topics: [Home Assistant](<https://devfeed.tech/topics/home-assistant.md>), [lora](<https://devfeed.tech/topics/lora.md>), [Matter](<https://devfeed.tech/topics/matter.md>), [Pinephone](<https://devfeed.tech/topics/pinephone.md>), [zigbee](<https://devfeed.tech/topics/zigbee.md>), [Kernel](<https://devfeed.tech/topics/kernel.md>), [Pinephone-Pro](<https://devfeed.tech/topics/pinephone-pro.md>), [USB](<https://devfeed.tech/topics/usb.md>), [Crystal](<https://devfeed.tech/topics/crystal.md>), [Dongle](<https://devfeed.tech/topics/dongle.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [community](<https://devfeed.tech/tags/community.md>), [crystal](<https://devfeed.tech/tags/crystal.md>), [dongle](<https://devfeed.tech/tags/dongle.md>), [eol](<https://devfeed.tech/tags/eol.md>), [home-assistant](<https://devfeed.tech/tags/home-assistant.md>), [kernel](<https://devfeed.tech/tags/kernel.md>), [lcd](<https://devfeed.tech/tags/lcd.md>), [pinedio](<https://devfeed.tech/tags/pinedio.md>), [pinephone](<https://devfeed.tech/tags/pinephone.md>), [pinephone-pro](<https://devfeed.tech/tags/pinephone-pro.md>), [pinetime](<https://devfeed.tech/tags/pinetime.md>), [pinevoice](<https://devfeed.tech/tags/pinevoice.md>), [protocol](<https://devfeed.tech/tags/protocol.md>), [table](<https://devfeed.tech/tags/table.md>), [update](<https://devfeed.tech/tags/update.md>), [usb](<https://devfeed.tech/tags/usb.md>), [zigbee](<https://devfeed.tech/tags/zigbee.md>)

### AI overview

Pine64's May 2026 update covers the PineVoice Home Assistant speaker, PineTime and PineTime Pro hardware, PineDio USB LoRa development, and recent PineTab2, PinePhone, PinePhone Pro, and PineTab-V software updates. It also reports instability in the PineDio adapter caused by an overheating crystal oscillator.

### Source excerpt

Table of contents Whats been going on recently? PineVoice PineTime/PineTime Pro LoRa? (Left-handed operational Radio authority, obviously) Important recent community updates: Do you have something to share? Contact Community PineStore Whats been going on recently? Hello everyone, we wanted to do a quick update on what's been happening recently. Life has been pretty busy but there's lots of nice things happening in the background that we'd like to share. PineVoice The PineVoice Home Assistant speaker (formally PineVox) is in its final stages. Production begins. Further on this, there will be limited bundle with this device which will include a PineVoice, Zigbee dongle, power supply and two matter modules. Pair it with a SBC of choice and run your Home Assistant instance locally! PineTime/PineTime Pro The current PineTime LCD is EOL and a new one is replacing it, though it is essentially the same display. These will be shipping in the next PineTime batch. New PineTime Pro samples have been received by developers. Shiny! LoRa? (Left-handed operational Radio authority, obviously) The PineDio USB LoRa Adapter is at the focus of a new effort to begin progress on potential new LoRa devices. MeshCore is the current protocol target for the USB LoRa Adapter along with help from the pyMC_core library project. The current problem with the device is that the crystal oscillator is heating up too much and can cause clock frequency drifting instability. In a nutshell that means your messages come out garbled. This may eventually be fixed with a TXCO (Temperature-Compensated Crystal Oscillator) to maintain a stable clock frequency. Important recent community updates: DanctNIX PineTab2 updates (including factory image) Adam Piggz: PinePhone, PinePhone Pro, PineTab2 all updated to kernel 7.0, and improved USB config. Zypper ref and dup to update. machaddr@mastodon.sdf.org: with their new Gentoo PineTab-V image Do you have something to share? Do you have news to share with the communit

## From FileUploadWorkflow to creative OS: How Layer scaled on Temporal

DevFeed: [From FileUploadWorkflow to creative OS: How Layer scaled on Temporal](<https://devfeed.tech/articles/from-fileuploadworkflow-to-creative-os-how-layer-scaled-on-temporal-35833.md>)

Original publisher: [Read original article](<https://temporal.io/blog/fileuploadworkflow-creative-os-layer-scaled-temporal>)

Author: Alex Engel

Published: 2026-04-28T00:00:00Z

Content type: article

Language: en

Sources: [Temporal Blog](<https://devfeed.tech/sources/temporal-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [async](<https://devfeed.tech/topics/async.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [lora](<https://devfeed.tech/topics/lora.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Canvas](<https://devfeed.tech/topics/canvas.md>), [Mobile](<https://devfeed.tech/topics/mobile.md>), [render](<https://devfeed.tech/topics/render.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [article](<https://devfeed.tech/tags/article.md>), [canvas](<https://devfeed.tech/tags/canvas.md>), [community](<https://devfeed.tech/tags/community.md>), [developer](<https://devfeed.tech/tags/developer.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [inference](<https://devfeed.tech/tags/inference.md>), [lora](<https://devfeed.tech/tags/lora.md>), [mobile](<https://devfeed.tech/tags/mobile.md>), [node](<https://devfeed.tech/tags/node.md>), [pipeline](<https://devfeed.tech/tags/pipeline.md>), [production](<https://devfeed.tech/tags/production.md>), [render](<https://devfeed.tech/tags/render.md>)

### AI overview

Layer describes how it expanded from a small set of Temporal workflows into about 50 workflow types running in production. The article covers the product and architecture changes behind its creative operating system, including image generation, LoRA fine-tuning, multimodal pipelines, billing synchronization, and large-scale parallel asset generation.

### Source excerpt

How Layer went from a handful of Temporal Workflows to 50 in production. The architecture, the lessons, and what broke along the way.

## Granite 4.0 3B Vision: Compact Multimodal Intelligence for Enterprise Documents

DevFeed: [Granite 4.0 3B Vision: Compact Multimodal Intelligence for Enterprise Documents](<https://devfeed.tech/articles/granite-4-0-3b-vision-compact-multimodal-intelligence-for-enterprise-documents-7259.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/ibm-granite/granite-4-vision>)

Author: Madison Lee; Rogerio Feris; Eli Schwartz; Dhiraj Joshi; Pengyuan Li; Isaac Sanchez

Published: 2026-03-31T15:10:41Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [multimodal](<https://devfeed.tech/topics/multimodal.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [synthetic-data](<https://devfeed.tech/topics/synthetic-data.md>), [lora](<https://devfeed.tech/topics/lora.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [enterprise deployment](<https://devfeed.tech/topics/enterprise-deployment.md>)

Tags: [code](<https://devfeed.tech/tags/code.md>), [data](<https://devfeed.tech/tags/data.md>), [data-augmentation](<https://devfeed.tech/tags/data-augmentation.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [enterprise-deployment](<https://devfeed.tech/tags/enterprise-deployment.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [lora](<https://devfeed.tech/tags/lora.md>), [model](<https://devfeed.tech/tags/model.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [training](<https://devfeed.tech/tags/training.md>), [vision](<https://devfeed.tech/tags/vision.md>)

### AI overview

Granite 4.0 3B Vision is a compact multimodal model for enterprise document understanding. It extracts tables, interprets charts, and identifies semantic key-value pairs, using a LoRA adapter, visual-language capabilities, and the ChartNet dataset to support structured chart reasoning and document-processing pipelines.

### Source excerpt

- Table Extraction: Accurately parsing complex table structures (e.g., multi-row, multi-column, etc.) from document images - Chart Understanding: Converting charts and figures into structured machine-readable formats, summaries, or executable code - Semantic Key-Value Pair (KVP) Extraction: Identifying and grounding semantically meaningful key-value field pairs across diverse document layouts The model ships as a LoRA adapter on top of Granite 4.0 Micro, our dense language model, keeping...

## Keep the Tokens Flowing: Lessons from 16 Open-Source RL Libraries

DevFeed: [Keep the Tokens Flowing: Lessons from 16 Open-Source RL Libraries](<https://devfeed.tech/articles/keep-the-tokens-flowing-lessons-from-16-open-source-rl-libraries-7109.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/async-rl-training-landscape>)

Author: Amine Dirhoussi; Quentin Gallouédec; Kashif Rasul; Lewis Tunstall; Edward Beeching; Albert Villanova del Moral; Nouamane Tazi; Leandro von Werra; Sergio Paniego

Published: 2026-03-10T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [distributed-training](<https://devfeed.tech/topics/distributed-training.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>), [post-training](<https://devfeed.tech/topics/post-training.md>), [lora](<https://devfeed.tech/topics/lora.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [grpo](<https://devfeed.tech/topics/grpo.md>), [Chain-of-thought](<https://devfeed.tech/topics/chain-of-thought.md>), [Open Source Models & Datasets](<https://devfeed.tech/topics/open-source-models-datasets.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [analysis](<https://devfeed.tech/tags/analysis.md>), [chain-of-thought](<https://devfeed.tech/tags/chain-of-thought.md>), [distributed-training](<https://devfeed.tech/tags/distributed-training.md>), [grpo](<https://devfeed.tech/tags/grpo.md>), [llm](<https://devfeed.tech/tags/llm.md>), [lora](<https://devfeed.tech/tags/lora.md>), [mixture-of-experts](<https://devfeed.tech/tags/mixture-of-experts.md>), [nccl](<https://devfeed.tech/tags/nccl.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [rl](<https://devfeed.tech/tags/rl.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

### AI overview

This article surveys 16 open-source libraries for asynchronous reinforcement-learning training. It explains how separating inference and training across GPU pools, using rollout buffers, and synchronizing weights asynchronously can reduce training-GPU idle time. The comparison covers orchestration, buffering, weight synchronization, staleness management, partial rollouts, LoRA, and distributed-training backends, highlighting Ray, NCCL broadcasts, limited LoRA support, and distributed MoE as an emerging differentiator.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## We Got Claude to Fine-Tune an Open Source LLM

DevFeed: [We Got Claude to Fine-Tune an Open Source LLM](<https://devfeed.tech/articles/we-got-claude-to-fine-tune-an-open-source-llm-7240.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/hf-skills-training>)

Author: ben burtenshaw; shaun smith

Published: 2025-12-04T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Claude Code](<https://devfeed.tech/topics/claude-code.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [jobs](<https://devfeed.tech/topics/jobs.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [lora](<https://devfeed.tech/topics/lora.md>), [rlvr](<https://devfeed.tech/topics/rlvr.md>), [codex](<https://devfeed.tech/topics/codex.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [claude](<https://devfeed.tech/tags/claude.md>), [claude-code](<https://devfeed.tech/tags/claude-code.md>), [codex](<https://devfeed.tech/tags/codex.md>), [community](<https://devfeed.tech/tags/community.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gemini](<https://devfeed.tech/tags/gemini.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [llm](<https://devfeed.tech/tags/llm.md>), [lora](<https://devfeed.tech/tags/lora.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

This tutorial explains how Hugging Face Skills enable Claude Code to fine-tune language models by validating datasets, selecting GPUs, configuring authentication, submitting cloud training jobs, monitoring progress, and publishing finished models to the Hugging Face Hub. It covers LoRA, full fine-tuning, supervised fine-tuning, direct preference optimization, reinforcement learning with verifiable rewards, GGUF conversion, and multi-stage training pipelines.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Unlocking the potential of vision language models on satellite imagery through fine-tuning

DevFeed: [Unlocking the potential of vision language models on satellite imagery through fine-tuning](<https://devfeed.tech/articles/unlocking-the-potential-of-vision-language-models-on-satellite-imagery-through-fine-tuning-7127.md>)

Original publisher: [Read original article](<https://mistral.ai/news/unlocking-potential-vision-language-models-satellite-imagery-fine-tuning/>)

Published: 2025-08-01T12:00:00Z

Content type: article

Language: en

Sources: [Mistral AI Blog](<https://devfeed.tech/sources/mistral-ai-blog.md>)

Topics: [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [lora](<https://devfeed.tech/topics/lora.md>), [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [data](<https://devfeed.tech/tags/data.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [foundation-models](<https://devfeed.tech/tags/foundation-models.md>), [image](<https://devfeed.tech/tags/image.md>), [lora](<https://devfeed.tech/tags/lora.md>), [models](<https://devfeed.tech/tags/models.md>), [techniques](<https://devfeed.tech/tags/techniques.md>), [vision](<https://devfeed.tech/tags/vision.md>)

### AI overview

This article explains how fine-tuning the Pixtral-12B vision-language model on satellite imagery improves specialized image classification. It describes LoRA as an efficient adaptation method and presents a case study using the Aerial Image Dataset, where fine-tuning helps distinguish difficult scene categories more accurately than a general-purpose model.

### Source excerpt

The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models.

## Fast LoRA inference for Flux with Diffusers and PEFT

DevFeed: [Fast LoRA inference for Flux with Diffusers and PEFT](<https://devfeed.tech/articles/fast-lora-inference-for-flux-with-diffusers-and-peft-7344.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/lora-fast>)

Author: Sayak Paul; Benjamin Bossan

Published: 2025-07-23T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [flux](<https://devfeed.tech/topics/flux.md>), [lora](<https://devfeed.tech/topics/lora.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Hacktoberfest](<https://devfeed.tech/topics/hacktoberfest.md>), [diffusers](<https://devfeed.tech/topics/diffusers.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [peft](<https://devfeed.tech/topics/peft.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [model architecture](<https://devfeed.tech/topics/model-architecture.md>), [text-to-image](<https://devfeed.tech/topics/text-to-image.md>), [GPU](<https://devfeed.tech/topics/gpu.md>)

Tags: [amd](<https://devfeed.tech/tags/amd.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [flux](<https://devfeed.tech/tags/flux.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [guide](<https://devfeed.tech/tags/guide.md>), [inference](<https://devfeed.tech/tags/inference.md>), [latency](<https://devfeed.tech/tags/latency.md>), [lora](<https://devfeed.tech/tags/lora.md>), [model-architecture](<https://devfeed.tech/tags/model-architecture.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [peft](<https://devfeed.tech/tags/peft.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [text-to-image](<https://devfeed.tech/tags/text-to-image.md>)

### AI overview

This article presents an optimization recipe for faster LoRA inference with the Flux.1-Dev text-to-image model using Diffusers and PEFT. The approach addresses LoRA hotswapping and recompilation issues with Flash Attention 3, FP8 quantization from TorchAO, and hotswapping-ready compilation, achieving about a 2.3x speedup while balancing inference speed and memory use.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## ESPHome 2025.7.0: Web server OTA, LoRa, and sub-devices

DevFeed: [ESPHome 2025.7.0: Web server OTA, LoRa, and sub-devices](<https://devfeed.tech/articles/esphome-2025-7-0-web-server-ota-lora-and-sub-devices-16699.md>)

Original publisher: [Read original article](<https://esphome.io/blog/2025/07/16/esphome-2025-7/>)

Author: Jesse Hills

Published: 2025-07-16T00:00:00Z

Content type: release

Language: en

Sources: [ESPHome - Smart Home Made Simple - Blog](<https://devfeed.tech/sources/esphome-smart-home-made-simple-blog.md>)

Topics: [esphome](<https://devfeed.tech/topics/esphome.md>), [ESP-IDF](<https://devfeed.tech/topics/esp-idf.md>), [lora](<https://devfeed.tech/topics/lora.md>), [Home Assistant](<https://devfeed.tech/topics/home-assistant.md>), [configuration](<https://devfeed.tech/topics/configuration.md>)

Tags: [component](<https://devfeed.tech/tags/component.md>), [configuration](<https://devfeed.tech/tags/configuration.md>), [esp-idf](<https://devfeed.tech/tags/esp-idf.md>), [esphome](<https://devfeed.tech/tags/esphome.md>), [home-assistant](<https://devfeed.tech/tags/home-assistant.md>), [improvements](<https://devfeed.tech/tags/improvements.md>), [lora](<https://devfeed.tech/tags/lora.md>), [memory](<https://devfeed.tech/tags/memory.md>), [new-features](<https://devfeed.tech/tags/new-features.md>), [ota](<https://devfeed.tech/tags/ota.md>), [performance](<https://devfeed.tech/tags/performance.md>), [release](<https://devfeed.tech/tags/release.md>), [releases](<https://devfeed.tech/tags/releases.md>)

### AI overview

ESPHome 2025.7.0 is a major release that moves web server OTA into a dedicated platform, removes ESP-IDF 4.x support in favor of ESP-IDF 5.3.2 or newer, and adds sub-device support, Jinja2 template expressions, LoRa communication, and new hardware and sensor support.

### Source excerpt

ESPHome 2025.7.0 moves web server OTA to its own platform, removes ESP-IDF 4.x support, adds sub-device support and Jinja2 template expressions, and introduces LoRa hardware support.

## (LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware

DevFeed: [(LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware](<https://devfeed.tech/articles/lora-fine-tuning-flux-1-dev-on-consumer-hardware-7203.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/flux-qlora>)

Author: Derek Liu; Marc Sun; Sayak Paul; merve; Linoy Tsaban

Published: 2025-06-19T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [lora](<https://devfeed.tech/topics/lora.md>), [flux](<https://devfeed.tech/topics/flux.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [GPU](<https://devfeed.tech/topics/gpu.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [dev](<https://devfeed.tech/tags/dev.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [flux](<https://devfeed.tech/tags/flux.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [guide](<https://devfeed.tech/tags/guide.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [lora](<https://devfeed.tech/tags/lora.md>), [model-training](<https://devfeed.tech/tags/model-training.md>), [nvidia-rtx](<https://devfeed.tech/tags/nvidia-rtx.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

This tutorial explains how to fine-tune the FLUX.1-dev diffusion model efficiently with QLoRA on a single consumer GPU using less than about 10 GB of VRAM. It describes the model components, focuses training on the transformer while keeping the text encoders and VAE frozen, and discusses LoRA, quantization, and FP8 training for memory and speed improvements.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## PINE64 January 2025 Update Covers PineVox Firmware, PineNote Software, and Community Projects

DevFeed: [PINE64 January 2025 Update Covers PineVox Firmware, PineNote Software, and Community Projects](<https://devfeed.tech/articles/january-update-thinking-out-of-the-vox-34885.md>)

Original publisher: [Read original article](<https://pine64.org/2025/01/11/january_2025/>)

Published: 2025-01-11T00:00:00Z

Content type: news

Language: en

Sources: [Community blog on PINE64](<https://devfeed.tech/sources/community-blog-on-pine64.md>)

Topics: [Software](<https://devfeed.tech/topics/software.md>), [Embedded Software Dev](<https://devfeed.tech/topics/embedded-software-dev.md>), [Development](<https://devfeed.tech/topics/development.md>), [Home Assistant](<https://devfeed.tech/topics/home-assistant.md>), [lora](<https://devfeed.tech/topics/lora.md>), [meshtastic](<https://devfeed.tech/topics/meshtastic.md>), [USB](<https://devfeed.tech/topics/usb.md>), [Bluetooth LE](<https://devfeed.tech/topics/bluetooth-le.md>), [Wi-Fi](<https://devfeed.tech/topics/wi-fi.md>), [.NET Conf](<https://devfeed.tech/topics/net-conf.md>)

Tags: [2025](<https://devfeed.tech/tags/2025.md>), [bluetooth-le](<https://devfeed.tech/tags/bluetooth-le.md>), [community-update](<https://devfeed.tech/tags/community-update.md>), [firmware](<https://devfeed.tech/tags/firmware.md>), [fosdem](<https://devfeed.tech/tags/fosdem.md>), [home-assistant](<https://devfeed.tech/tags/home-assistant.md>), [infinitime](<https://devfeed.tech/tags/infinitime.md>), [lora](<https://devfeed.tech/tags/lora.md>), [meshtastic](<https://devfeed.tech/tags/meshtastic.md>), [news](<https://devfeed.tech/tags/news.md>), [pinebook-pro](<https://devfeed.tech/tags/pinebook-pro.md>), [pinenote](<https://devfeed.tech/tags/pinenote.md>), [pinetime](<https://devfeed.tech/tags/pinetime.md>), [pinevox](<https://devfeed.tech/tags/pinevox.md>), [release](<https://devfeed.tech/tags/release.md>), [right](<https://devfeed.tech/tags/right.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [update](<https://devfeed.tech/tags/update.md>), [usb](<https://devfeed.tech/tags/usb.md>)

### AI overview

PINE64's January 2025 community update covers planned PineVox public release work, continued PineNote software improvements, developer devices, the second PineDio USB LoRa adapter project, FOSDEM attendance, and PineVox firmware progress.

### Source excerpt

This month we report on some news concerning the PineVox, what the community have been up to with their PineNotes and the brand-new 1.15 InfiniTime update. We would like to wish our community a happy New Year and hope you all had a good end of 2024. But since it's now the beginning of 2025, we'd like to fill you in on what we have planned for the first part of the year. We plan to release the PineVox to the public thanks to Gamiee's recent progress and we plan to continue improving the software for the PineNote, thanks to Maximilian, Diederik and our testers in the community. We have some exciting devices coming as well, mentioned in previous community updates. The PineCam and StarPro64 have been sent to developers in our community, so we hope to report on these devices in the coming months. Gamiee has also been working with a group of our friends over at Meshtastic on an independent project which will become the second version of the PineDio USB LoRa adapter. We plan to talk more about the progress of that project more in the February update. Table of contents FOSDEM PineVox PineNote Important - Software Update Issue Development updates PineNote Community Spotlight Inkput Pinescreen InfiniTime 1.15 Pinebook Pro Community content PineNote CE first month review Want to contribute to the next community update? FOSDEM Author: Caffeine Unfortunately, a stand at FOSDEM hasn't been allocated for us this year. But that doesn't mean we won't be there to represent PINE64! Gamiee, Ralimtek, JF, TL Lim and Lukasz will be attending in person this year and will be planning a get-together. We will announce on our social-media when we have confirmed the time and place. Of course, our PINE64 community members will still be wandering around the event, we love talking to all our friends at other projects and meeting the members of our community! So we hope we'll see you there! PineVox Author: Caffeine, Gamiee Gamiee has been working on the firmware for the PineVox recently (haha, now

## TGI Multi-LoRA: Deploy Once, Serve 30 Models

DevFeed: [TGI Multi-LoRA: Deploy Once, Serve 30 Models](<https://devfeed.tech/articles/tgi-multi-lora-deploy-once-serve-30-models-7359.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/multi-lora-serving>)

Author: Derek Thomas; Diego Maniloff; David Holtz

Published: 2024-07-18T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [lora](<https://devfeed.tech/topics/lora.md>), [tgi](<https://devfeed.tech/topics/tgi.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [AI Inference](<https://devfeed.tech/topics/ai-inference.md>)

Tags: [cost](<https://devfeed.tech/tags/cost.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [guide](<https://devfeed.tech/tags/guide.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [llm](<https://devfeed.tech/tags/llm.md>), [lora](<https://devfeed.tech/tags/lora.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [peft](<https://devfeed.tech/tags/peft.md>), [performance](<https://devfeed.tech/tags/performance.md>), [privacy](<https://devfeed.tech/tags/privacy.md>), [tgi](<https://devfeed.tech/tags/tgi.md>)

### AI overview

This article introduces TGI Multi-LoRA serving, a feature that allows organizations to deploy one base model and serve many specialized models. It explains how LoRA efficiently fine-tunes large pre-trained models by adding small adapter parameter sets, reducing storage and memory overhead while preserving model quality. The feature addresses the cost and operational complexity of deploying multiple fine-tuned Large Language Models.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Diffusers welcomes Stable Diffusion 3

DevFeed: [Diffusers welcomes Stable Diffusion 3](<https://devfeed.tech/articles/diffusers-welcomes-stable-diffusion-3-7469.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/sd3>)

Author: Dhruv Nair; YiYi Xu; Sayak Paul; Alvaro Somoza; Kashif Rasul; Apolinário from multimodal AI art

Published: 2024-06-12T00:00:00Z

Content type: release

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [stable-diffusion](<https://devfeed.tech/topics/stable-diffusion.md>), [diffusers](<https://devfeed.tech/topics/diffusers.md>), [diffusion-transformers](<https://devfeed.tech/topics/diffusion-transformers.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [text-to-image](<https://devfeed.tech/topics/text-to-image.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [lora](<https://devfeed.tech/topics/lora.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [guide](<https://devfeed.tech/tags/guide.md>), [inference](<https://devfeed.tech/tags/inference.md>), [lora](<https://devfeed.tech/tags/lora.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [sd3](<https://devfeed.tech/tags/sd3.md>), [stable-diffusion](<https://devfeed.tech/tags/stable-diffusion.md>), [text-to-image](<https://devfeed.tech/tags/text-to-image.md>)

### AI overview

The article announces Stable Diffusion 3 Medium, a 2B-parameter latent diffusion model. It describes the MMDiT architecture, multimodal text and image processing, rectified flow matching, inference support through a new scheduler, and accompanying Diffusers, DreamBooth, and LoRA resources.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## My Tailor is Mistral

DevFeed: [My Tailor is Mistral](<https://devfeed.tech/articles/my-tailor-is-mistral-6995.md>)

Original publisher: [Read original article](<https://mistral.ai/news/customization/>)

Published: 2024-06-05T09:00:00Z

Content type: release

Language: en

Sources: [Mistral AI Blog](<https://devfeed.tech/sources/mistral-ai-blog.md>)

Topics: [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [lora](<https://devfeed.tech/topics/lora.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>)

Tags: [customization](<https://devfeed.tech/tags/customization.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [llms](<https://devfeed.tech/tags/llms.md>), [lora](<https://devfeed.tech/tags/lora.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [sdk](<https://devfeed.tech/tags/sdk.md>), [serverless](<https://devfeed.tech/tags/serverless.md>)

### AI overview

Mistral introduces model customization on la Plateforme, offering ways to fine-tune Mistral AI models on developers' own infrastructure or through managed services. The announcement covers the open-source mistral-finetune SDK, LoRA-based training, serverless fine-tuning, and deployment of adapted models.

### Source excerpt

The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models.

## PINE64 explores LoRa for peer-to-peer and group text communication

DevFeed: [PINE64 explores LoRa for peer-to-peer and group text communication](<https://devfeed.tech/articles/let-s-make-mirakles-happen-34840.md>)

Original publisher: [Read original article](<https://pine64.org/2021/05/06/lets-make-mirakles-happen/>)

Published: 2021-05-06T00:00:00Z

Content type: opinion

Language: en

Sources: [Community blog on PINE64](<https://devfeed.tech/sources/community-blog-on-pine64.md>)

Topics: [lora](<https://devfeed.tech/topics/lora.md>), [Internet of things](<https://devfeed.tech/topics/iot.md>), [P2P](<https://devfeed.tech/topics/p2p.md>), [Messaging](<https://devfeed.tech/topics/messaging.md>), [gateway](<https://devfeed.tech/topics/gateway.md>), [Pinephone](<https://devfeed.tech/topics/pinephone.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>)

Tags: [communication](<https://devfeed.tech/tags/communication.md>), [gateway](<https://devfeed.tech/tags/gateway.md>), [iot](<https://devfeed.tech/tags/iot.md>), [lora](<https://devfeed.tech/tags/lora.md>), [open-hardware](<https://devfeed.tech/tags/open-hardware.md>), [peer](<https://devfeed.tech/tags/peer.md>), [pine64](<https://devfeed.tech/tags/pine64.md>), [pinephone](<https://devfeed.tech/tags/pinephone.md>), [rakwireless](<https://devfeed.tech/tags/rakwireless.md>)

### AI overview

PINE64 describes plans to explore LoRa for peer-to-peer and group text communication, including potential end-nodes for its SBCs, Pinebook Pro, PineTab and PinePhone. The project involves RAKwireless gateways and is still partly conceptual.

### Source excerpt

Many of you are already aware that we are very interested in LoRa®. We hope to use the technology for both traditional IoT applications as well as in less orthodox ways, such as peer-to-peer text communication and even group text-messaging. This novel application potential is of particular interest to us, and in the coming months we will encourage developers to explore LoRa's® viability as a text communication alternative to GSM/CDMA and LTE. We're doubling down on LoRa® even at this early stage, so you can expect to see end-nodes for our SBCs, the Pinebook Pro, PineTab and PinePhone available in the Pine Store shortly. Indeed, we hope for LoRa® to become a staple of PINE64. Group messaging over LoRa® is only conceptually plausible at the moment, but thankfully we're not walking into this blindly or alone. We've been talking to the good folks over at RAKwireless about our ideas, and they've been both interested in what we had to say and very helpful in getting us started. For those of you who do not know, RAKwireless is an industry leading IoT solutions provider, producing high-end LoRa® gateways, sensors, kits and, of course, also gateway modules. Both our indoor and outdoor gateways, which are at the very heart of the system, will be using RAKwireless technology. We're thrilled that we're going to have an opportunity to work with RAKwireless and their community on this project. "We are driven by innovation, open hardware, and community work, and that's exactly what PINE64 brings to our company. Most members of the RAKStars community recognize LoRa as part of the development of IoT devices, however, PINE64 sees LoRa differently. With the power coming from both companies, we will enable people from all over the world to use LoRa technology in innovative ways, and most importantly, to deliver secure text communication." - Maria Hernandez Thanks to the work of RTP from Privacy & Tech Tips, our gateways are already fully functional, and the setup process is as simple a