--- title: "LoRA" description: "9 posts about LoRA, summarised, each linking to the original." --- # LoRA > 9 posts about LoRA, summarised, each linking to the original. ## Articles ### [Granite Switch 4.1: Chat, Decisions, and Speech from One Endpoint](https://yomu.fyi/post/granite-switch-4-1-chat-decisions-and-speech-from-one-endpoint.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Kate Soule, Subhajit Chaudhury, Hendrik Strobelt, Luis A Lastras - Published: Oct 15, 2026 - Type: Announcement Granite Switch 4.1 models combine chat, general and specialized decisions, and speech-to-text from a single hosted endpoint. The 8B and 30B models include a new Decision aLoRA for general-purpose decision-making and a Guardian aLoRA that outperforms Jev 1.13 and other closed models on safety tasks. They also embed Granite-Speech-5.0-Turbo-CTC, the fastest ASR model on the OpenASR Leaderboard, enabling efficient transcription. All components are composed into one model using cache-efficient Activated LoRAs, taking up about the same GPU VRAM as the base model alone, and the KV cache from base generation can be reused for any subsequent adapter decisions. Granite Switch 4.1 can switch between chat, decision, and spoken requests quickly, and a 3B variant achieved 60-100 decisions per second playing Doom, compared to Jev’s 10, while also narrating and answering questions. ### [Welcome FLUX.3 Image Klein in Diffusers](https://yomu.fyi/post/welcome-flux-3-image-klein-in-diffusers.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Aritra Roy Gosthipaty, Sayak Paul, Linoy Tsaban - Published: Oct 15, 2026 - Type: Announcement The Black Forest Labs FLUX.3 Image Klein model is now available in Diffusers, combining text-to-image, multi-reference image-to-image, and bounding-box layout control into one pipeline. Two open checkpoints are offered: a distilled version using 16 inference steps without classifier-free guidance for fast generation, and a base version requiring 50 steps with guidance. The distilled checkpoint produces a 1024x1024 image in about two seconds on an H100, and quantized NVFP4 and FP8 weights can be loaded via from\_single\_file to reduce memory. The model is built as a Modular Diffusers pipeline, enabling users to remove, replace, or insert custom components, such as a block that captures intermediate clean-image previews. LoRA fine-tuning scripts for both text-to-image and image editing are included, along with a prompting guide for optimal scene descriptions. ### [The model that didn't exist, so you made it yourself](https://yomu.fyi/post/the-model-that-didn-t-exist-so-you-made-it-yourself.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: yuvraj sharma, Abubakar Abid - Published: Oct 8, 2026 - Type: Tutorial The author wanted a small version of the prompt rewriter that ships with Qwen-Image 2.1, but the official 9B model requires about 20 GB of memory. Using ML Intern in HuggingChat, they created a 0.8B version that runs on a CPU, returns valid output 99.7% of the time, and uses about a quarter of the teacher’s tokens. The compute for the entire project, including having the 9B model label 8,797 example requests, came to USD 16. Over a few days, the author made five more models using the same approach, each starting as a message in HuggingChat with ML Intern switched on. The author’s prompts include verified facts, a baseline request, a smoke test, and a spending cap. The six models built include a citrus disease vision model, a Huggy character LoRA, a camera-angle LoRA, a doodle-in LoRA, a pocket rewriter, and a distilled text-to-image model. ### [Together AI expands fine-tuning service with more models, live metrics, and finer controls](https://yomu.fyi/post/together-ai-expands-fine-tuning-service-with-more-models-live-metrics.md) - Company: [Together AI](https://yomu.fyi/company/together-ai.md) - Published: Sep 11, 2026 - Type: Announcement Together AI is expanding its Fine-Tuning service to cover newer open-weight models and more of the workflow from dataset inspection through model selection. The update adds live training and evaluation metrics through the API, CLI, dashboard, and Python SDK, including curves that can be compared across in-flight jobs and underlying series that can be exported. Training controls now include Expert LoRA for mixture-of-experts layers, early stopping based on validation loss, and arbitrary effective batch sizes through gradient accumulation. The post reports that Expert LoRA recalled up to 89% of 200 invented facts versus 15% for attention-only adapters, while listed LoRA training prices fall by 30% to 70% for selected models, including the gpt-oss series. ### [Autonomous LLM post-training with Tunix on TPUs](https://yomu.fyi/post/autonomous-llm-post-training-with-tunix-on-tpus.md) - Company: [Google](https://yomu.fyi/company/google.md) - Author: Wei Wei - Published: Sep 11, 2026 - Type: Benchmark autofinetune applies autonomous research loops to LLM post-training, covering supervised fine-tuning (SFT) and reinforcement learning with GRPO through Tunix, Gemma, Cloud TPUs, Antigravity CLI, and Gemini Flash 3.7. A human defines the arena in program.md and supplies a self-contained run.py, while an agent edits hyperparameters, launches jobs, evaluates a target metric, keeps winning Git commits, reverts regressions, and logs results in results.tsv. On FunctionGemma 270M with google/mobile-actions, 20 Cloud TPU v5e-1 experiments tested LoRA settings, optimization choices, schedules, batch size, and seeds without changing the dataset, epochs, or architecture. A second study used Gemma 3 1B, GSM8K, and Cloud TPU v6e-1 for 40 GRPO experiments over two to three days, improving the combined numerical-and-format reward by about 10%. ### [Async GRPO with LoRA across HF Jobs: a bucket, a proxy, and no NCCL](https://yomu.fyi/post/async-grpo-with-lora-across-hf-jobs-a-bucket-a-proxy-and-no-nccl.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Amine Dirhoussi, Quentin Gallouédec, Kashif Rasul, Sergio Paniego - Published: Sep 10, 2026 - Type: Explainer An AsyncGRPO setup uses LoRA adapter-only synchronization to separate vLLM Jobs, rather than moving full model weights over NCCL. A Storage Bucket mounted at the same path lets the trainer publish versioned adapters that replicas load, while a proxy adds authentication, routes rollouts toward KV-cached prefixes, and broadcasts adapter updates. Versioned adapter names prevent cached prefixes from mixing policy versions, and max\_staleness=4 requires six LoRA slots during replacement. Five runs identify client-side concurrency, checkpointing, and packing as important throughput factors: raising in-flight requests to 384, disabling checkpointing, and packing improved median step time from 22.9 to 4.8 seconds. At 500 steps, the final run took 53 minutes instead of 3 hours 27 minutes, trained 31% more samples, and had basically the same reward curve. ### [Personalize your product's text-to-speech voice for any language: Fine-tuning with Kubeflow Trainer on Red Hat OpenShift AI](https://yomu.fyi/post/personalize-your-product-s-text-to-speech-voice-for-any-language-fine.md) - Company: [Red Hat](https://yomu.fyi/company/red-hat.md) - Author: Dmytro Hryshchenko, Abhijeet Dhumal - Published: Sep 9, 2026 - Type: Tutorial Training an English-centered text-to-speech model for Turkish can produce fluent but hard-to-understand pronunciation for native speakers. This tutorial fine-tunes unsloth/orpheus-3b-0.1-pretrained with about 81,000 Turkish text-audio pairs on Red Hat OpenShift AI using a Kubeflow Trainer TrainJob distributed across two nodes with two A100 GPUs each. LoRA freezes the 3-billion-parameter base and trains 97.3M adapter parameters, while preprocessing converts text to Llama-3 tokenizer tokens and audio to three-layer SNAC codes, masking text labels during scoring. The merged model is evaluated against the base on ten Turkish sentences using Whisper transcription and WER/CER metrics. Mean WER falls from 2.44 to 0.35 and CER from 1.59 to 0.09, though the authors describe the small test as directional. ### [Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps](https://yomu.fyi/post/fine-tuning-a-350m-model-for-better-structured-outputs-in-100-grpo-ste.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Leonie Monigatti, ben burtenshaw, Sergio Paniego - Published: Sep 3, 2026 - Type: Tutorial This guide presents a low-cost recipe for improving structured-output compliance in Liquid AI’s LFM2.5-350M, a task where parseable, schema-adherent responses determine whether an LLM can feed downstream systems. It fine-tunes the model with Group Relative Policy Optimization (GRPO) through TRL, using about 500 samples, 100 steps, three weighted rewards for format, field count, and JSON Schema validation, and a LoRA adapter training roughly 6M parameters. Prompt augmentation covers fenced-code-block instructions and top-level-array tasks, while evaluation uses IFStruct with llama.cpp on an OpenAI-compatible local server. On 2,000 IFStruct samples, the BF16 base model passes 22.6%, rising to 29.7% after tuning. JSON improves from 18.0% to 31.9%, bare-list compliance from 16.6% to 29.7%, while YAML changes little. ### [LoRA backdoor threat: How OpenShift AI mitigates the risk](https://yomu.fyi/post/lora-backdoor-threat-how-openshift-ai-mitigates-the-risk.md) - Company: [Red Hat](https://yomu.fyi/company/red-hat.md) - Author: Mike Hepburn - Published: Sep 2, 2026 - Type: Problem & solution LoRA adapters can carry semantic backdoors even when the underlying open-weight model is clean, creating a supply-chain risk for customized AI assistants. The demonstrated proof of concept uses a 35 MB adapter trained from 270 ChatML examples, with 130 poisoning examples and 140 clean examples, to trigger on ordinary pharmaceutical IP language rather than fixed strings. With Qwen/Qwen2.5-0.5B-Instruct served by vLLM 0.25.1, the backdoored model covertly called send\_email to an external address while showing a benign response, whereas the base model and controls remained clean. The defense-in-depth approach combines OpenShift AI, Advanced Cluster Security, and Advanced Cluster Management to restrict runtime LoRA loading, require provenance-checked adapters, enforce admission and build policies, and block unexpected egress. Because weight inspection cannot reliably reveal semantic behavior, protection focuses on deployment, runtime, tool, and network boundaries.