https://dailypypy.org/tags/lora/ Recent content in LoRA on 每日拍拍 Hugo -- gohugo.io zh-tw © 2026 每日拍拍 Wed, 12 Aug 2026 12:30:00 +0800 155076163427069952 154825760438254592 https://dailypypy.org/learn/mlx-lora-finetune-local/ Wed, 12 Aug 2026 12:30:00 +0800 https://dailypypy.org/learn/mlx-lora-finetune-local/ <!--- 1440x768 prompt: masterpiece, best quality, highres, clean anime illustration, japanese anime style, soft shading, flat color design, 1girl, black hair, green eyes, white off-shoulder shirt, black short skirt, side profile, leaning forward, determined look, looking over shoulder at viewer, pale apricot background, subtle tiny adapter modules and dataset cards floating nearby without text, neat composition, detailed eyes, cute and smart vibe, minimal background, polished illustration, no text negative prompt: worst quality, bad eye, bad hand, extra limbs, manga, multiple views, monochrome, text, signature dedup note: Planned 2026-06-16 topic `mlx-lora-finetune-local` is a specific follow-up to ../mlx-lm-local-models/ and ../mlx-lm-batch-inference/. The older posts cover local inference, chat templates, streaming, JSONL batch inference, sampling grids, and repeatable evaluation. This article focuses on LoRA/QLoRA adapter training: dataset schema, train/valid/test split, `mlx_lm.lora`, adapter outputs, prompt masking, generation with adapters, fusing, and memory knobs. It does not repeat the general MLX-LM inference workflow. ---> <p>本地 LLM 跑起來以後，下一個很自然的問題是： 「那我可以讓模型更懂自己的任務嗎？」 可以。 但拍拍君先把期待值放準一點。 LoRA 微調不是魔法。 它不是把一個小模型訓練成萬能博士。 它比較像是在模型旁邊加一組可拆卸的小筆記，讓模型在某個格式、語氣、分類規則、抽取任務上更穩。 這篇要做的是 MLX-LM 的 LoRA 入門工作流：</p>