# lora

Published articles for lora.

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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.

## FREE-WILi 2 portable hacking multitool features two RP2350 MCUs, ESP32-C5, ICE40 FPGA, and Raspberry Pi CM0

DevFeed: [FREE-WILi 2 portable hacking multitool features two RP2350 MCUs, ESP32-C5, ICE40 FPGA, and Raspberry Pi CM0](<https://devfeed.tech/articles/free-wili-2-portable-hacking-multitool-features-two-rp2350-mcus-esp32-c5-ice40-fpga-and-raspberry-pi-cm0-14036.md>)

Original publisher: [Read original article](<https://www.cnx-software.com/2026/09/09/free-wili-2-portable-hacking-multitool-features-two-rp2350-mcus-esp32-c5-ice40-fpga-and-raspberry-pi-cm0/>)

Author: Debashis Das

Published: 2026-09-09T00:00:02Z

Content type: news

Language: en

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

Topics: [Embedded Systems](<https://devfeed.tech/topics/embedded-systems.md>), [Raspberry Pi](<https://devfeed.tech/topics/raspberry-pi.md>), [ESP32-C5](<https://devfeed.tech/topics/esp32-c5.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [Hacking](<https://devfeed.tech/topics/hacking.md>), [Linux](<https://devfeed.tech/topics/linux.md>)

Tags: [802-15-4](<https://devfeed.tech/tags/802-15-4.md>), [ble](<https://devfeed.tech/tags/ble.md>), [bluetooth](<https://devfeed.tech/tags/bluetooth.md>), [broadcom-bcmxxxx](<https://devfeed.tech/tags/broadcom-bcmxxxx.md>), [c-c-plus-plus](<https://devfeed.tech/tags/c-c-plus-plus.md>), [can](<https://devfeed.tech/tags/can.md>), [electronics](<https://devfeed.tech/tags/electronics.md>), [embedded](<https://devfeed.tech/tags/embedded.md>), [esp32](<https://devfeed.tech/tags/esp32.md>), [esp32-c5](<https://devfeed.tech/tags/esp32-c5.md>), [espressif](<https://devfeed.tech/tags/espressif.md>), [fpga](<https://devfeed.tech/tags/fpga.md>), [hack](<https://devfeed.tech/tags/hack.md>), [hacking](<https://devfeed.tech/tags/hacking.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [lattice](<https://devfeed.tech/tags/lattice.md>), [linux](<https://devfeed.tech/tags/linux.md>), [lora](<https://devfeed.tech/tags/lora.md>), [meshtastic](<https://devfeed.tech/tags/meshtastic.md>), [nfc](<https://devfeed.tech/tags/nfc.md>), [open-hardware](<https://devfeed.tech/tags/open-hardware.md>), [python](<https://devfeed.tech/tags/python.md>), [raspberry-pi](<https://devfeed.tech/tags/raspberry-pi.md>), [raspberry-pi-rp2350](<https://devfeed.tech/tags/raspberry-pi-rp2350.md>), [rfid](<https://devfeed.tech/tags/rfid.md>), [rust](<https://devfeed.tech/tags/rust.md>), [security](<https://devfeed.tech/tags/security.md>), [som](<https://devfeed.tech/tags/som.md>), [stm32](<https://devfeed.tech/tags/stm32.md>), [stmicro-stm32](<https://devfeed.tech/tags/stmicro-stm32.md>), [texas-instruments-simplelink](<https://devfeed.tech/tags/texas-instruments-simplelink.md>), [thread](<https://devfeed.tech/tags/thread.md>), [tools](<https://devfeed.tech/tags/tools.md>), [video](<https://devfeed.tech/tags/video.md>), [wi-fi](<https://devfeed.tech/tags/wi-fi.md>), [wifi-6](<https://devfeed.tech/tags/wifi-6.md>), [wireless](<https://devfeed.tech/tags/wireless.md>), [zigbee](<https://devfeed.tech/tags/zigbee.md>)

### AI overview

The FREE-WILi 2 Founder Edition is an open-hardware, portable multitool for hardware hackers, pentesters, and embedded-system developers. It combines two Raspberry Pi RP2350 MCUs, an ESP32-C5, a Lattice ICE40UP5K FPGA, and a Raspberry Pi CM0 capable of running a full Linux OS, along with touchscreen controls, expansion headers, and multiple wireless interfaces.

### Source excerpt

The FREE-WILi 2 (Founder Edition) is an open-hardware, portable hacking multitool and lab designed for hardware hackers, pentesters, and embedded system developers. Designed as an "open electronics multitool," it combines a range of chips and modules into a handheld device, including two Raspberry Pi RP2350 MCUs, an ESP32-C5 for wireless connectivity, a Lattice ICE40UP5K FPGA, and a Raspberry Pi CM0 for running a full Linux OS. The device also features a 3.5-inch capacitive touchscreen, a 14-button physical interface, and modular "Orca" headers for hardware expansion. Its wireless features include 2.4/5GHz Wi-Fi, Bluetooth, LoRa, sub-GHz RF, NFC, 125kHz RFID, and IR. These features make it very suitable for wireless testing, embedded hardware debugging, real-time control, electronics development, and retro gaming, with support for Adafruit Fruit Jam, PICO-8, and Doom. FREE-WILi 2 specifications: Compute Main MCUs - 2x Raspberry Pi RP2350B (One for main controls, one dedicated to display) SoM - Raspberry [...] The post FREE-WILi 2 portable hacking multitool features two RP2350 MCUs, ESP32-C5, ICE40 FPGA, and Raspberry Pi CM0 appeared first on CNX Software - Embedded Systems News.

## China Merchants Bank Wins CNCF End User Case Study Contest for Unifying AI Training and Inference on Kubernetes

DevFeed: [China Merchants Bank Wins CNCF End User Case Study Contest for Unifying AI Training and Inference on Kubernetes](<https://devfeed.tech/articles/china-merchants-bank-wins-cncf-end-user-case-study-contest-for-unifying-ai-training-and-inference-on-kubernetes-4594.md>)

Original publisher: [Read original article](<https://www.cncf.io/announcements/2026/09/07/china-merchants-bank-wins-cncf-end-user-case-study-contest-for-unifying-ai-training-and-inference-on-kubernetes/>)

Author: Haley White

Published: 2026-09-08T01:54:31Z

Content type: news

Language: en

Sources: [Cloud Native Computing Foundation](<https://devfeed.tech/sources/cloud-native-computing-foundation.md>)

Topics: [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [AI Platform](<https://devfeed.tech/topics/ai-platform.md>), [AI Inference](<https://devfeed.tech/topics/ai-inference.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [kueue](<https://devfeed.tech/topics/kueue.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Cloud Native Ecosystem](<https://devfeed.tech/topics/cloud-native-ecosystem.md>), [datacenter](<https://devfeed.tech/topics/datacenter.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [ai-training](<https://devfeed.tech/tags/ai-training.md>), [announcements](<https://devfeed.tech/tags/announcements.md>), [case-study](<https://devfeed.tech/tags/case-study.md>), [china](<https://devfeed.tech/tags/china.md>), [compute](<https://devfeed.tech/tags/compute.md>), [cost](<https://devfeed.tech/tags/cost.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [inference](<https://devfeed.tech/tags/inference.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [kueue](<https://devfeed.tech/tags/kueue.md>), [lora](<https://devfeed.tech/tags/lora.md>)

### AI overview

China Merchants Bank won a CNCF case-study contest for a Kubernetes-based AI platform that shares nearly 10,000 accelerator cards across training, fine-tuning, and online inference. The bank reports increased average accelerator utilization and lower inference costs.

### Source excerpt

New cloud native platform lifted average accelerator compute utilization from 35% to more than 60% and cut inference cost per 1 million tokens by more than 60% Key Highlights SHANGHAI, China - KubeCon + CloudNativeCon +...

## OpenTrailPaper transforms LILYGO T5 E-Paper S3 Pro devkit into a DIY e-paper bike computer

DevFeed: [OpenTrailPaper transforms LILYGO T5 E-Paper S3 Pro devkit into a DIY e-paper bike computer](<https://devfeed.tech/articles/opentrailpaper-transforms-lilygo-t5-e-paper-s3-pro-devkit-into-a-diy-e-paper-bike-computer-14029.md>)

Original publisher: [Read original article](<https://www.cnx-software.com/2026/09/05/opentrailpaper-transforms-lilygo-t5-e-paper-s3-pro-devkit-into-a-diy-e-paper-bike-computer/>)

Author: Jean-Luc Aufranc (CNXSoft)

Published: 2026-09-05T08:01:22Z

Content type: news

Language: en

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

Topics: [Embedded Systems](<https://devfeed.tech/topics/embedded-systems.md>), [ESP32-S3](<https://devfeed.tech/topics/esp32-s3.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Android](<https://devfeed.tech/topics/android.md>), [iOS](<https://devfeed.tech/topics/ios.md>), [Bluetooth](<https://devfeed.tech/topics/bluetooth.md>), [ESP-IDF](<https://devfeed.tech/topics/esp-idf.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [JavaScript](<https://devfeed.tech/topics/javascript.md>), [Python](<https://devfeed.tech/topics/python.md>), [web browser](<https://devfeed.tech/topics/web-browser.md>)

Tags: [android](<https://devfeed.tech/tags/android.md>), [bluetooth](<https://devfeed.tech/tags/bluetooth.md>), [browser](<https://devfeed.tech/tags/browser.md>), [core](<https://devfeed.tech/tags/core.md>), [display](<https://devfeed.tech/tags/display.md>), [diy](<https://devfeed.tech/tags/diy.md>), [e-ink](<https://devfeed.tech/tags/e-ink.md>), [e-paper](<https://devfeed.tech/tags/e-paper.md>), [epaper](<https://devfeed.tech/tags/epaper.md>), [esp-idf](<https://devfeed.tech/tags/esp-idf.md>), [esp32](<https://devfeed.tech/tags/esp32.md>), [esp32-s3](<https://devfeed.tech/tags/esp32-s3.md>), [espressif](<https://devfeed.tech/tags/espressif.md>), [firmware](<https://devfeed.tech/tags/firmware.md>), [github](<https://devfeed.tech/tags/github.md>), [gps](<https://devfeed.tech/tags/gps.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [ios](<https://devfeed.tech/tags/ios.md>), [javascript](<https://devfeed.tech/tags/javascript.md>), [lilygo](<https://devfeed.tech/tags/lilygo.md>), [lora](<https://devfeed.tech/tags/lora.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [python](<https://devfeed.tech/tags/python.md>)

### AI overview

OpenTrailPaper is an open-source DIY e-paper bike computer project for the LILYGO T5 E-Paper S3 Pro, using ESP32-S3 firmware alongside Android and iOS companion apps. It supports ride data, offline maps, GPX routes, FIT file recording, and Bluetooth sensor connections.

### Source excerpt

OpenTrailPaper is an open-source, DIY e-paper bike computer project comprised of firmware for the ESP32-S3-based T5 E-Paper S3 Pro devkit and Android and iOS companion apps for maps, routes, and settings. It shows ride data and offline maps, follows standard GPX (GPS Exchange Format) routes, records FIT (Flexible and Interoperable Data Transfer used in Garmin devices) files, and connects to Bluetooth (and soon ANT/ANT+) heart rate, power, and cadence sensors. Before looking into the project's details, let's check out the LILYGO T5 E-Paper S3 Pro specifications: ESP32-S3-WROOM-1 wireless module SoC - ESP32-S3N16R8 dual-core Tensilica LX7 microcontroller @ up to 240 MHz with 2.4 GHz 802.11n WiFi 4 and Bluetooth 5.0 LE connectivity Memory - 8MB PSRAM Storage - 16MB SPI flash PCB antenna Storage - MicroSD card slot Display - 4.7-inch e-Paper display with 960x540 resolution, 16 shades of gray, capacitive touch (GT911); ED047TC1 drive IC Wireless 2.4 GHz 802.11n [...] The post OpenTrailPaper transforms LILYGO T5 E-Paper S3 Pro devkit into a DIY e-paper bike computer appeared first on CNX Software - Embedded Systems News.

## 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.

## Lessons From the Leaderboard: What 5,000+ Kagglers Taught Us About Improving AI Reasoning

DevFeed: [Lessons From the Leaderboard: What 5,000+ Kagglers Taught Us About Improving AI Reasoning](<https://devfeed.tech/articles/lessons-from-the-leaderboard-what-5-000-kagglers-taught-us-about-improving-ai-reasoning-6875.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/lessons-from-the-leaderboard-what-5000-kagglers-taught-us-about-improving-ai-reasoning/>)

Author: Elizabeth Goodman

Published: 2026-07-14T18:20:32Z

Content type: article

Language: en

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

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Chain-of-thought](<https://devfeed.tech/topics/chain-of-thought.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [Google](<https://devfeed.tech/topics/google.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [blackwell](<https://devfeed.tech/tags/blackwell.md>), [chain-of-thought](<https://devfeed.tech/tags/chain-of-thought.md>), [cost](<https://devfeed.tech/tags/cost.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [featured](<https://devfeed.tech/tags/featured.md>), [google](<https://devfeed.tech/tags/google.md>), [kaggle](<https://devfeed.tech/tags/kaggle.md>), [lora](<https://devfeed.tech/tags/lora.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [performance](<https://devfeed.tech/tags/performance.md>), [pre-trained-foundation-models](<https://devfeed.tech/tags/pre-trained-foundation-models.md>), [synthetic](<https://devfeed.tech/tags/synthetic.md>), [techniques](<https://devfeed.tech/tags/techniques.md>)

### AI overview

The article distills lessons from NVIDIA's Nemotron Model Reasoning Challenge, where more than 5,000 Kaggle participants tested ways to improve AI reasoning under shared model, infrastructure, and evaluation constraints. It highlights synthetic chain-of-thought data, trace quality, targeted solvers, validation beyond public leaderboards, and careful training and context-budget management.

### Source excerpt

The NVIDIA Nemotron Model Reasoning Challenge invited the Kaggle community to explore a focused question: What techniques can improve reasoning accuracy when...

## 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...

## Beyond LoRA: Can you beat the most popular fine-tuning technique?

DevFeed: [Beyond LoRA: Can you beat the most popular fine-tuning technique?](<https://devfeed.tech/articles/beyond-lora-can-you-beat-the-most-popular-fine-tuning-technique-7439.md>)

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

Author: Benjamin Bossan; Sayak Paul; Marian Tietz; Kashif Rasul

Published: 2026-06-18T00:00:00Z

Content type: article

Language: en

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

Topics: [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [community](<https://devfeed.tech/tags/community.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [guide](<https://devfeed.tech/tags/guide.md>), [lora](<https://devfeed.tech/tags/lora.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [peft](<https://devfeed.tech/tags/peft.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [stable-diffusion](<https://devfeed.tech/tags/stable-diffusion.md>), [training](<https://devfeed.tech/tags/training.md>), [transformers](<https://devfeed.tech/tags/transformers.md>)

### AI overview

The article examines parameter-efficient fine-tuning (PEFT) for open models, focusing on whether LoRA is always the best technique. It explains how PEFT reduces memory requirements, can enable fine-tuning of quantized models, and offers a unified API for multiple techniques.

### Source excerpt

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

## 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.

## Sheaf: vLLM for Non-Text Foundation Models

DevFeed: [Sheaf: vLLM for Non-Text Foundation Models](<https://devfeed.tech/articles/sheaf-vllm-for-non-text-foundation-models-40132.md>)

Original publisher: [Read original article](<https://korbonits.com/blog/2026-04-14-sheaf-vllm-for-non-text-foundation-models/>)

Published: 2026-04-14T00:00:00Z

Content type: article

Language: en

Sources: [Alex Korbonits](<https://devfeed.tech/sources/alex-korbonits.md>)

Topics: [vllm](<https://devfeed.tech/topics/vllm.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Python](<https://devfeed.tech/topics/python.md>), [Streaming](<https://devfeed.tech/topics/streaming.md>), [Docker](<https://devfeed.tech/topics/docker.md>), [lora](<https://devfeed.tech/topics/lora.md>), [SIEM, Security, Observability](<https://devfeed.tech/topics/siem-security-observability.md>)

Tags: [docker](<https://devfeed.tech/tags/docker.md>), [foundation-models](<https://devfeed.tech/tags/foundation-models.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llms](<https://devfeed.tech/tags/llms.md>), [lora](<https://devfeed.tech/tags/lora.md>), [observability](<https://devfeed.tech/tags/observability.md>), [python](<https://devfeed.tech/tags/python.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

This article introduces Sheaf, a serving framework for non-text foundation models. It argues that vLLM's serving optimizations benefit from the shared compute pattern of autoregressive text LLMs, while time-series, tabular, molecular, biological, and diffusion models require different batching, memory-management, and inference approaches. The article describes Sheaf's proposed capabilities, including typed contracts, model-aware batching, streaming, caching, observability, offline batch inference, asynchronous workers, LoRA adapter multiplexing, a typed Python client, Docker and KubeRay deployment support, and 27 PyPI backends.

### Source excerpt

vLLM solved inference for text LLMs. The same gap exists for every other class of foundation model -- time series, tabular, molecular, diffusion, and more. Sheaf fills it: typed contracts, model-type-aware batching, streaming, caching, observability, offline batch inference, an async-job worker, LoRA adapter multiplexing, a typed Python client, a Docker base image with KubeRay deployment, and 27 backends on PyPI.

## 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.

## Diffusers welcomes FLUX-2

DevFeed: [Diffusers welcomes FLUX-2](<https://devfeed.tech/articles/diffusers-welcomes-flux-2-7202.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/flux-2>)

Author: YiYi Xu; Daniel Gu; Sayak Paul; Alvaro Somoza; Dhruv Nair; Aritra Roy Gosthipaty; Linoy Tsaban; Apolinário from multimodal AI art

Published: 2025-11-25T00:00:00Z

Content type: article

Language: en

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

Topics: [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [black-forest-labs](<https://devfeed.tech/tags/black-forest-labs.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [flux](<https://devfeed.tech/tags/flux.md>), [generation](<https://devfeed.tech/tags/generation.md>), [images](<https://devfeed.tech/tags/images.md>), [lora](<https://devfeed.tech/tags/lora.md>), [mlp](<https://devfeed.tech/tags/mlp.md>), [model](<https://devfeed.tech/tags/model.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [streams](<https://devfeed.tech/tags/streams.md>)

### AI overview

The article introduces FLUX.2, an image-generation and editing model that supports text-guided and image-guided generation with multiple reference images. It outlines changes to its text encoder and diffusion-transformer architecture relative to FLUX.1.

### 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.

[Next page](<https://devfeed.tech/tags/lora.md?cursor=WyIyMDI1LTA2LTE5VDAwOjAwOjAwKzAwOjAwIiwgIjc1ZmJkNDJiLWY4ZDMtNDc1NC05NWIzLWM3Mjc3Zjk4OWUxMCJd>)