# peft

Published articles for peft.

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## 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.

## 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.

## 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.