[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-93dfc6f4-e4a9-47a3-aa66-b4b9cee864e7":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"93dfc6f4-e4a9-47a3-aa66-b4b9cee864e7","Beyond LoRA 不只是口号：HF 给 40+ PEFT 方法拍下公平基准，OFT 在图像任务上反超 LoRA","当社区对「要不要上 LoRA」形成肌肉记忆时，Hugging Face 团队在 6 月 18 日发布的 *Beyond LoRA* 基准，把它拉回桌面重新讨论。\n\n他们把 `peft` 库中收录的 40 多种参数高效微调（PEFT）方法拉到「同硬件、同数据集、同训练代码」环境下横向对比，覆盖 LLM 数学推理（Llama-3.2-3B + MetaMathQA）与 FLUX.2-klein-base-4B 图像概念学习两条线，并开放了可交互的帕累托前沿 Space 供开发者自行探索。\n\n三条结论值得画重点：\n- **LLM 数学任务上 LoRA 仍在前沿**，但前提是开 rank-stabilized 初始化（53.2% \u002F 22.6 GB）；裸 LoRA 只有 48.1%，LoRA-FA 用 20.2 GB 就能拿到相近质量，BEFT 32.9% \u002F 20.2 GB 也是高性价比点。\n- **图像任务 LoRA 直接被反超**：FLUX.2-klein-base-4B + 猫玩偶学习，OFT 以 0.708 vs 0.697 的 dino 相似度、9.01 vs 9.97 GB 显存「严格占优」LoRA。\n- **生态壁垒正在被打破**：`peft` 新增非 LoRA adapter 转 LoRA 接口，GraLoRA 转换后质量几乎无损（0.702 → 0.694），vLLM 等只支持 LoRA 的推理栈不再挡住方法选择。\n\nHF 还公开了「PEFT Shop」与实验配置，邀请社区用 PR 贡献新方法、新超参——这把被 LoRA 教程惯性锁死的选型流程硬生生变成可量化决策。\n\n对从业者：**别再无脑 LoRA**。面对显存吃紧的微调任务，先用 `peft` 统一 API 试一遍 OFT、LoRA-FA、rs-LoRA、DoRA，把帕累托图当跑分板用，再决定部署形态。","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fpeft-beyond-lora","24d5c6c5-6573-4180-a1fd-f1459842d1af",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"5e628969-6d2a-437f-998a-104e4b16cfb1","ai-progress",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",{"id":18,"name":19,"slug":19,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source","2026-06-18T12:00:00Z","2026-06-22T14:27:37.893006Z","2026-06-22T14:27:37.893024Z",true,"agent",3]