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agent/skills-disabled/ppt-master/examples/ppt169_lora_hu_2021/README.md Normal file
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# lora_hu_2021
- Canvas format: ppt169
- Created: 20260523
- Source: Hu et al. 2021, LoRA: Low-Rank Adaptation of Large Language Models
## Directories
- `svg_output/`: raw SVG output
- `svg_final/`: finalized SVG output
- `images/`: presentation assets
- `notes/`: speaker notes
- `exports/`: generated PPTX file

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agent/skills-disabled/ppt-master/examples/ppt169_lora_hu_2021/animations.json Normal file
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{
"version": 1,
"defaults": {
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"duration": 0.35
},
"animation": {
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"slides": {
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"annot": {
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"animation": {
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"groups": {
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"14_understanding": {
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"animation": {
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"duration": 0.34,
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"groups": {
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"heatmap": {
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"delay": 0.12
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"animation": {
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"duration": 0.48
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"repo": {
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"delay": 0.12
}
}
}
}
}

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# LoRA: Low-Rank Adaptation of Large Language Models - Design Spec
> 论文解读演示 · 源文档:Hu et al., 2021, Microsoft (arXiv:2106.09685v2)。设计叙事见本文;机器可读执行契约见 `spec_lock.md`(冲突以 spec_lock 为准)。
## I. Project Information
| Item | Value |
| ---- | ----- |
| **Project Name** | lora_hu_2021 |
| **Canvas Format** | PPT 16:9 (1280×720) |
| **Page Count** | 15 |
| **Design Style** | B) 通用咨询(数据清晰优先)+ 学术技术极简 |
| **Target Audience** | 机器学习研究者 / 工程师;论文分享会、组会、技术答辩 |
| **Use Case** | 英文论文的中文讲解汇报(术语保留英文) |
| **Created Date** | 2026-05-23 |
---
## II. Canvas Specification
| Property | Value |
| -------- | ----- |
| **Format** | PPT 16:9 |
| **Dimensions** | 1280×720 |
| **viewBox** | `0 0 1280 720` |
| **Margins** | 左右 64px,上下 56px |
| **Content Area** | 1152×608(安全区) |
---
## III. Visual Theme
### Theme Style
- **Style**: 学术技术极简 — 蓝图式结构、克制留白、数据与公式说话
- **Theme**: Light theme(白底)
- **Tone**: 严谨、可信、工程化、analytical
### Color Scheme
| Role | HEX | Purpose |
| ---- | --- | ------- |
| **Background** | `#FFFFFF` | 页面底色 |
| **Secondary bg** | `#F4F7FA` | 卡片 / 区块背景 |
| **Primary** | `#1B3A5C` | 深学术蓝 — 标题、结构线、图表主轴、图标 |
| **Accent** | `#E8743B` | 暖橙 — 关键数字(10000×、零延迟)、高亮、LoRA 分支 |
| **Secondary accent** | `#3E7CB1` | 中蓝 — 次级数据系列、辅助结构 |
| **Body text** | `#1D2733` | 主体文字 |
| **Secondary text** | `#5B6776` | 注释、副标题 |
| **Tertiary text** | `#8A94A1` | 脚注、页码 |
| **Border/divider** | `#D8DEE6` | 卡片边、分割线 |
| **Success** | `#2E7D32` | 优于基线指示(LoRA 胜出) |
| **Warning** | `#C62828` | 代价 / 劣势指示(全量微调开销、延迟增加) |
**Color rules**: 60-30-10(白底 60% / 深蓝 30% / 暖橙 ≤10%);正文对比度 ≥ 4.5:1;每页 ≤ 4 色。
### AI Image Strategy
- **Image Rendering**: blueprint
- **Image Palette**: cool-corporate
> blueprint × cool-corporate 兼容矩阵为 ✓✓。全 deck AI 图共享此渲染与色彩;`text_policy: embedded`,图内可带文字(术语英文、说明可中文),生成后校验输出。HEX 为真值,palette 仅规定 60-30-10 用法。
### Gradient Scheme
```xml
<linearGradient id="titleGradient" x1="0%" y1="0%" x2="100%" y2="0%">
<stop offset="0%" stop-color="#1B3A5C"/>
<stop offset="100%" stop-color="#3E7CB1"/>
</linearGradient>
<radialGradient id="bgDecor" cx="85%" cy="15%" r="55%">
<stop offset="0%" stop-color="#1B3A5C" stop-opacity="0.06"/>
<stop offset="100%" stop-color="#1B3A5C" stop-opacity="0"/>
</radialGradient>
```
---
## IV. Typography System
### Font Plan
**Typography direction**: 统一无衬线(技术论文 slides 惯例),代码 / 维度符号用等宽。
| Role | Chinese | English | Fallback tail |
| ---- | ------- | ------- | ------------- |
| **Title** | `"Microsoft YaHei"` | `Arial` | `sans-serif` |
| **Body** | `"Microsoft YaHei"` | `Arial` | `sans-serif` |
| **Emphasis** | `"Microsoft YaHei"` | `Arial` | `sans-serif`(weight 对比,非换字体) |
| **Code** | — | `Consolas, "Courier New"` | `monospace` |
**Per-role font stacks**:
- Title: `"Microsoft YaHei", Arial, sans-serif`
- Body: `"Microsoft YaHei", Arial, sans-serif`
- Emphasis: same as Body(靠 font-weight 700/900 制造对比)
- Code: `Consolas, "Courier New", monospace`
### Font Size Hierarchy
**Baseline**: Body = 18px(dense — 数据表 + 公式 + 多点内容)。
| Purpose | Ratio | px | Weight |
| ------- | ----- | -- | ------ |
| Cover title | 3.3x | 60 | Heavy |
| Hero number(10000× 等) | 3.1x | 56 | Heavy |
| Page title | 1.8x | 32 | Bold |
| Subtitle | 1.3x | 24 | SemiBold |
| **Body** | **1x** | **18** | Regular |
| Annotation / caption | 0.78x | 14 | Regular |
| Chart annotation | 0.72x | 13 | Regular |
| Page number / footnote | 0.61x | 11 | Regular |
---
## V. Layout Principles
### Page Structure
- **Header area**: 高 ~96px — 页眉标题 + 细色条 + 页序
- **Content area**: 高 ~520px — 主体(图表 / 图 / 公式 / 卡片)
- **Footer area**: 高 ~40px — 来源标注(LoRA, Hu et al. 2021)+ 页码
### Layout Pattern Library(按信息权重组合)
按页采用:单列居中(封面/结论)、非对称分栏(图 vs 要点)、三/四列卡片(优势/KPI)、image-as-canvas + 原生叠加(方法图)、负空间主导(breathing 概念页)。不把每页做成同一张卡片网格。
### Spacing Specification
**Universal**: 安全边距 64px;内容块间距 28px;图标-文字间距 10px。
**Card-based**: 卡片间距 24px;卡片内边距 24px;圆角 12px;四列卡片宽 ~264px。
**Non-card(breathing)**: 行高 1.5×;靠留白与分隔线分区。
---
## VI. Icon Usage Specification
### Source
- 库:`tabler-outline`(线性、轻盈、技术屏显),`stroke_width: 2`,deck 内不混用其它库。
- 用法:`<use data-icon="tabler-outline/<name>" stroke-width="2" .../>`
### Recommended Icon List
| Purpose | Icon Path | Page |
| ------- | --------- | ---- |
| 冻结权重 | `tabler-outline/snowflake` / `tabler-outline/lock` | P03/P06 |
| 部署成本 | `tabler-outline/building-bank` / `tabler-outline/server-2` | P03 |
| 局限警示 | `tabler-outline/alert-triangle` | P04 |
| 低秩洞察 | `tabler-outline/bulb` / `tabler-outline/layers-subtract` | P05 |
| 方法 / 公式 | `tabler-outline/math-function` / `tabler-outline/transform` | P06/P07 |
| 分支 A/B | `tabler-outline/arrows-split` | P06 |
| Transformer 结构 | `tabler-outline/sitemap` / `tabler-outline/topology-star` | P08 |
| 可共享 | `tabler-outline/share-2` / `tabler-outline/git-branch` | P09 |
| 高效训练 | `tabler-outline/bolt` | P09 |
| 零推理延迟 | `tabler-outline/gauge` | P09/P10 |
| 正交可叠加 | `tabler-outline/puzzle` | P09 |
| 实验 | `tabler-outline/flask` / `tabler-outline/database` | P11 |
| 结果 | `tabler-outline/chart-bar` | P10/P12 |
| 达标 / 优于 | `tabler-outline/circle-check` | P12/P13 |
| 结论 | `tabler-outline/target` | P15 |
---
## VII. Visualization Reference List
Catalog read: 71 templates
| Page | Template | Path | Summary-quote (verbatim from `charts_index.json`) | Usage |
| ---- | -------- | ---- | ------------------------------------------------- | ----- |
| P02 | agenda_list | `templates/charts/agenda_list.svg` | "Pick for table of contents, meeting agendas, or presentation roadmap — numbered items + brief description + duration / owner per row." | 演示路线图(6 节) |
| P04 | vertical_list | `templates/charts/vertical_list.svg` | "Pick for 3-6 numbered key points each with a short description — design principles, core tenets, action items, key takeaways, recommendations, executive summary points." | 已有方法 3 类局限 |
| P09 | icon_grid | `templates/charts/icon_grid.svg` | "Pick for 4-9 parallel features/capabilities/services as icon cards — feature grid, service lineup, benefits matrix, brand values, product highlights." | LoRA 四大优势 |
| P10 | grouped_bar_chart | `templates/charts/grouped_bar_chart.svg` | "Pick for 2-4 series side-by-side across the same categories (e.g. YoY/QoQ)." | 各方法推理延迟增幅对比 |
| P11 | basic_table | `templates/charts/basic_table.svg` | "Pick for plain tabular text/number grid, 3-8 columns." | 模型 × 基准 × 任务实验矩阵 |
| P12 | consulting_table | `templates/charts/consulting_table.svg` | "Pick for high-density tables with embedded micro bar visuals (consulting/financial reports)." | GLUE 结果(参数量 + Avg 微条) |
| P13 | kpi_cards | `templates/charts/kpi_cards.svg` | "Pick for 4-8 standalone numeric metrics shown as overview cards (2x2 or 1x4) — exec summary opener, dashboard headline, quarterly recap, results-at-a-glance." | GPT-3 175B 四项关键指标 |
| P14 | basic_table | `templates/charts/basic_table.svg` | "Pick for plain tabular text/number grid, 3-8 columns." | 适配权重(Table 5)+ 最优秩(Table 6) |
| P15 | vertical_list | `templates/charts/vertical_list.svg` | "Pick for 3-6 numbered key points each with a short description — design principles, core tenets, action items, key takeaways, recommendations, executive summary points." | 结论 takeaways |
**Runners-up considered**:
- `numbered_steps` | rejected for P02: 演示目录是并列章节而非顺序步骤,agenda_list 自带 duration/section 列更贴合。
- `pros_cons_chart` | rejected for P04: 已有方法局限是"单边缺点清单(延迟 / 序列长度 / 质量-效率权衡)",非左右对称的 pros vs cons。
- `bar_chart` | rejected for P10: 延迟需对比 AdapterL / AdapterH 两个变体 × 序列场景,属多系列并列,grouped_bar 更准。
- `comparison_table` | rejected for P12: GLUE 想用 Avg 微条强化"少参数高分"的视觉,consulting_table 的内嵌 micro-bar 优于纯文本对比表。
- `gauge_chart` | rejected for P13: GPT-3 是多项指标(参数比 / VRAM / 吞吐 / 准确率),非单一 hero 指标,kpi_cards 合适。
---
## VIII. Image Resource List
| Filename | Dimensions | Ratio | Purpose | Type | Layout pattern | Acquire Via | Status | Reference | text_policy | page_role |
| -------- | --------- | ----- | ------- | ---- | -------------- | ----------- | ------ | --------- | ----------- | --------- |
| cover_hero.png | 1280x720 | 1.78 | 封面主视觉(Slide 01) | Illustration | #1 full-bleed background with floating title + #29 two-stop scrim | ai | Pending | 冻结的大型预训练权重方阵被锁住,旁路注入一条由两个细长低秩矩阵(降维 A、升维 B)组成的可训练分支,连线汇合到输出;蓝图示意,留下方安静带给 SVG 标题 | embedded | hero_page |
| cost_explosion.png | 860x720 | 1.19 | 全量微调部署代价(Slide 03) | Diagram | #4 right image bleeding off the canvas edge + #32 multi-stop scrim with hue shift | ai | Pending | 同一个 175B 巨型模型被为每个下游任务整份复制成多座沉重的服务器机柜,强调"每任务一份满参数副本"的高昂存储/部署成本 | embedded | local |
| lowrank_insight.png | 1280x720 | 1.78 | 低秩内在维度洞察(Slide 05) | Illustration | #19 image floating in whitespace with thin frame and caption + #21 rounded rectangle crop | ai | Pending | 高维权重更新云坍缩贴合到一张低维子空间薄片上,少数主方向承载主要信息;概念示意,大量留白 | embedded | local |
| reparam_diagram_cropped.png | 1280x531 | 2.41 | LoRA 重参数化结构图(Slide 06) | Diagram | #44 background image + native network/architecture diagram + #41 measurement lines and module tags (engineering overlay) | ai | Pending | 输入 x 同时进入冻结的预训练权重 W 与并行低秩旁路:先经降维矩阵 A 到秩 r 瓶颈,再经升维矩阵 B,两路输出相加得 h;箭头清晰,瓶颈维度 r 标注 | embedded | local |
| attention_apply.png | 530x580 | 0.91 | 作用于 Transformer 注意力(Slide 08) | Diagram | #2 left-third image + right text body + #21 rounded rectangle crop | ai | Pending | 一个 Transformer 自注意力模块,四个投影矩阵 Wq/Wk/Wv/Wo,其中 Wq 与 Wv 各挂一条低秩 LoRA 旁路高亮,MLP 模块标为冻结;蓝图结构 | embedded | local |
| subspace_heatmap.png | 1024x1024 | 1.0 | 子空间相似度热力图(Slide 14) | Diagram | #3 right-third image + left text body + #21 rounded rectangle crop | ai | Pending | 方阵热力图:行 i 列 j 的归一化子空间相似度,左上角(top singular directions)高强度发亮、其余迅速变暗,清晰的网格单元,蓝-橙强度色阶 | embedded | local |
| formula_001.png | 688x44 | 15.64 | 前向传播公式(Slide 06) | Latex Formula | formula-block | formula | Rendered | `h = W_0 x + \Delta W x = W_0 x + B A x` — LoRA 修改后的前向传播 | | |
| formula_002.png | 1280x55 | 23.27 | 低秩分解与约束(Slide 06) | Latex Formula | formula-block | formula | Rendered | `W_0 + \Delta W = W_0 + BA, B∈R^{d×r}, A∈R^{r×k}, r≪min(d,k)` — 低秩分解 | | |
| formula_003.png | 220x91 | 2.42 | 缩放系数(Slide 07) | Latex Formula | formula-block | formula | Rendered | `\Delta W x · α/r` — ΔWx 的缩放 | | |
| formula_004.png | 604x60 | 10.07 | 可训练参数量(Slide 08) | Latex Formula | formula-block | formula | Rendered | `|Θ| = 2 × L_LoRA × d_model × r` — LoRA 可训练参数量 | | |
> 图内文字按"是否需可编辑"判别(冻结栅格 → 生成后校验);P06 采用 image-as-canvas(#44)满足 image-as-canvas 覆盖要求 — 精确公式与可改写标签走 SVG 叠加,图本身承载结构氛围。所有 AI 图同 blueprint × cool-corporate,HEX 不作为字面文字写进图内。
---
## IX. Content Outline
### Part 1: 引入与问题
#### Slide 01 - Cover
- **Layout**: 全屏主视觉 + 浮层标题(breathing→anchor)
- **Title**: LoRA:大模型的低秩适配
- **Subtitle**: Low-Rank Adaptation of Large Language Models
- **Info**: Hu et al., 2021 · Microsoft · arXiv:2106.09685 · 论文解读
#### Slide 02 - 演示路线
- **Layout**: agenda_list(anchor)
- **Title**: 本次讲什么
- **Visualization**: agenda_list
- **Content**: ① 问题:微调为何越来越贵 ② 已有方法的局限 ③ 核心洞察:低秩假设 ④ LoRA 方法与实现 ⑤ 实验结果 ⑥ 低秩理解与结论
#### Slide 03 - 问题:全量微调的代价
- **Layout**: 左 hero 数字 + 右溢出图(breathing)
- **Title**: 模型越大,全量微调越不可行
- **Content**:
- GPT-3 175B:每个下游任务都要存一份满参数副本(175B)
- 部署 N 个任务 = N × 175B,存储与切换成本极高
- Hero:**175B** 可训练参数 / 每任务
### Part 2: 已有方法与洞察
#### Slide 04 - 已有方法的局限
- **Layout**: vertical_list 三项(dense)
- **Title**: 为什么现有高效适配方法不够好
- **Visualization**: vertical_list
- **Content**:
- Adapter 层:增加模型深度 → 引入推理延迟(在线短序列尤甚)
- Prefix / Prompt tuning:占用输入长度 → 压缩可用序列
- 普遍:常达不到全量微调基线 → 效率与质量的权衡
#### Slide 05 - 核心洞察:低"内在秩"
- **Layout**: 概念图 + 短文(breathing)
- **Title**: 权重更新其实"低秩"
- **Content**:
- 过参数化模型实际位于低内在维度(Aghajanyan et al. 2020)
- 假设:适配时的权重变化 ΔW 也具有低"内在秩"
- 即使 d 高达 12,288,极低的 r(1~2)也足够
### Part 3: 方法
#### Slide 06 - LoRA 方法
- **Layout**: image-as-canvas 结构图 + 公式叠加(breathing)
- **Title**: 冻结 W₀,注入低秩 BA
- **Visualization**: (AI 结构图 reparam_diagram_cropped + formula_001/002)
- **Content**:
- 用低秩分解约束更新:W₀ + ΔW = W₀ + BA
- 前向:h = W₀x + BAx;训练时 W₀ 冻结,只训 A、B
- A 高斯初始化、B 置零 → 起始 ΔW = 0
#### Slide 07 - 实现细节:初始化与缩放
- **Layout**: 公式 + 要点(dense)
- **Title**: 一个常数 α、一次部署合并
- **Visualization**: (formula_003)
- **Content**:
- ΔWx 按 α/r 缩放;α 设为首个尝试的 r,不再调
- 部署时显式合并 W = W₀ + BA → 与原模型同构
- 换任务:减去 BA 再加 B′A′,开销极小
#### Slide 08 - 作用于 Transformer
- **Layout**: 左结构图 + 右要点 + 参数量公式(dense)
- **Title**: 只适配注意力权重
- **Visualization**: (AI 结构图 attention_apply + formula_004)
- **Content**:
- 自注意力含 Wq/Wk/Wv/Wo;实验只适配 Wq、Wv,冻结 MLP
- 可训练参数量:|Θ| = 2 × L_LoRA × d_model × r
- GPT-3 175B:VRAM 1.2TB → 350GB;checkpoint 350GB → 35MB
#### Slide 09 - 四大优势
- **Layout**: icon_grid 四卡(dense)
- **Title**: LoRA 的四个关键优势
- **Visualization**: icon_grid
- **Content**:
- 可共享:一个底座 + 多个小 LoRA 模块,换任务只换 BA
- 高效训练:无需为冻结参数存梯度/优化器状态,硬件门槛降 3×
- 零推理延迟:合并权重后,与全量微调模型同构
- 正交可叠加:可与 prefix-tuning 等方法组合
### Part 4: 实验结果
#### Slide 10 - 推理零延迟
- **Layout**: grouped_bar_chart(dense)
- **Title**: Adapter 增延迟,LoRA 不增
- **Visualization**: grouped_bar_chart
- **Content**:
- GPT-2 medium 单次前向延迟(100 次平均,RTX8000)
- AdapterL 最高 +20.7%,AdapterH 最高 +30.3%(短序列/小 batch)
- LoRA / FT 基线:**0% 额外延迟**
#### Slide 11 - 实验设置
- **Layout**: basic_table 模型×基准(dense)
- **Title**: 覆盖 NLU 到 NLG 的四类模型
- **Visualization**: basic_table
- **Content**:
- RoBERTa base/large(125M/355M)、DeBERTa XXL(1.5B)→ GLUE
- GPT-2 medium → E2E NLG;GPT-3 175B → WikiSQL / MNLI / SAMSum
- 基线:FT、BitFit、PreEmbed/PreLayer、Adapter H/L/P/D
#### Slide 12 - GLUE 结果
- **Layout**: consulting_table(dense)
- **Title**: 更少参数,持平或更优
- **Visualization**: consulting_table
- **Content**:
- RoBERTa-base:FT 86.4(125M)→ LoRA **87.2**(0.3M)
- RoBERTa-large:FT 88.9(355M)→ LoRA **89.0**(0.8M)
- DeBERTa-XXL:FT 91.1(1500M)→ LoRA **91.3**(4.7M)
#### Slide 13 - GPT-3 175B 结果
- **Layout**: kpi_cards 四指标(dense)
- **Title**: 在 175B 尺度上仍然成立
- **Visualization**: kpi_cards
- **Content**:
- **10,000×** 可训练参数缩减(checkpoint 350GB → 35MB)
- VRAM 1.2TB → **350GB**(约 1/3)
- 训练吞吐 **+25%**(无需为多数参数算梯度)
- WikiSQL/MNLI/SAMSum 均**持平或超过**全量微调
### Part 5: 理解与结论
#### Slide 14 - 低秩理解:权重与秩
- **Layout**: 左双表 + 右热力图(dense)
- **Title**: 该适配谁?秩要多大?
- **Visualization**: basic_table + (AI 热力图 subspace_heatmap)
- **Content**:
- 同等参数预算:适配 {Wq, Wv} 最佳(WikiSQL 73.7)
- 秩 r=1 即足以适配 {Wq, Wv}(r=64 几乎无增益)
- 子空间相似度:top 奇异方向高度重叠 → ΔW 内在秩极低
#### Slide 15 - 结论与影响
- **Layout**: vertical_list takeaways(anchor)
- **Title**: 结论与影响
- **Visualization**: vertical_list
- **Content**:
- 低秩适配 = 参数高效 + 零额外推理延迟 + 质量持平/更优
- 工程意义:一个底座托管多任务,按需热插拔 LoRA
- 已开源(microsoft/LoRA),成为大模型微调的事实标准之一
---
## X. Speaker Notes Requirements
- 每页一篇,存 `notes/total.md`(主文档用 `#` 标题行,文件名匹配 SVG)
- 总时长:约 18-22 分钟;风格:讲解型(conversational-professional);目的:inform + instruct
- 中文讲解,术语保留英文
---
## XI. Technical Constraints Reminder
### SVG Generation Must Follow:
1. viewBox `0 0 1280 720`;背景用 `<rect>`
2. 文本换行用 `<tspan>`;禁止 `<foreignObject>`
3. 透明用 `fill-opacity`/`stroke-opacity`;禁止 `rgba()`
4. 禁止:`mask`、`<style>`、`class`、`foreignObject`、`textPath`、`animate*`、`script`
5. 字符写原始 Unicode(`—`、`→`、`×`、`≪`);禁止 HTML 实体;`& < >` 转义为 `&amp; &lt; &gt;`
6. `clipPath` 仅用于 `<image>`(圆角裁剪等)
### PPT Compatibility Rules:
- 禁止 `<g opacity>`(改为逐子元素设 opacity)
- 图片半透明用叠加遮罩层
- 仅内联样式;禁外部 CSS / `@font-face`

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2,cost_explosion.png,860,720,1.19,1.19,pixel,1,,bitmap,True,True,751.1,Near square,717x600,423x600
3,cover_hero.png,1280,720,1.78,1.78,pixel,1,,bitmap,True,True,772.8,Wide landscape,812x457,328x600
4,formula_001.png,688,44,15.64,15.64,pixel,1,,bitmap,True,True,4.4,Ultra-wide,1160x74,1160x506
5,formula_002.png,1280,55,23.27,23.27,pixel,1,,bitmap,True,True,22.6,Ultra-wide,1160x50,1160x530
6,formula_003.png,220,91,2.42,2.42,pixel,1,,bitmap,True,True,2.8,Ultra-wide,812x336,328x600
7,formula_004.png,604,60,10.07,10.07,pixel,1,,bitmap,True,True,5.4,Ultra-wide,1160x115,1160x465
8,lowrank_insight.png,1280,720,1.78,1.78,pixel,1,,bitmap,True,True,1054.1,Wide landscape,812x457,328x600
9,reparam_diagram_cropped.png,1280,531,2.41,2.41,pixel,1,,bitmap,True,True,490.0,Ultra-wide,812x337,328x600
10,subspace_heatmap.png,1024,1024,1.00,1.00,pixel,1,,bitmap,True,True,874.7,Near square,600x600,540x600
1 No Filename Width Height AspectRatio PixelAspectRatio RatioSource UsageCount DisplayRatioVariants AssetKind SvgRenderable PptxNativeSupported SizeKB Category ImageArea_SxS TextArea_SxS
2 1 attention_apply.png 530 580 0.91 0.91 pixel 1 bitmap True True 271.1 Near square 548x600 592x600
3 2 cost_explosion.png 860 720 1.19 1.19 pixel 1 bitmap True True 751.1 Near square 717x600 423x600
4 3 cover_hero.png 1280 720 1.78 1.78 pixel 1 bitmap True True 772.8 Wide landscape 812x457 328x600
5 4 formula_001.png 688 44 15.64 15.64 pixel 1 bitmap True True 4.4 Ultra-wide 1160x74 1160x506
6 5 formula_002.png 1280 55 23.27 23.27 pixel 1 bitmap True True 22.6 Ultra-wide 1160x50 1160x530
7 6 formula_003.png 220 91 2.42 2.42 pixel 1 bitmap True True 2.8 Ultra-wide 812x336 328x600
8 7 formula_004.png 604 60 10.07 10.07 pixel 1 bitmap True True 5.4 Ultra-wide 1160x115 1160x465
9 8 lowrank_insight.png 1280 720 1.78 1.78 pixel 1 bitmap True True 1054.1 Wide landscape 812x457 328x600
10 9 reparam_diagram_cropped.png 1280 531 2.41 2.41 pixel 1 bitmap True True 490.0 Ultra-wide 812x337 328x600
11 10 subspace_heatmap.png 1024 1024 1.00 1.00 pixel 1 bitmap True True 874.7 Near square 600x600 540x600

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"providers": [
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"items": [
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"id": "formula_001",
"latex": "h = W_0 x + \\Delta W x = W_0 x + B A x",
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"background": "#FFFFFF",
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"display": "block",
"color": "#1B3A5C",
"background": "#FFFFFF",
"transparent": true,
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"id": "formula_003",
"latex": "\\Delta W x \\;\\cdot\\; \\frac{\\alpha}{r}",
"display": "block",
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"background": "#FFFFFF",
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"display": "block",
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{
"project": "lora_hu_2021",
"generated_at": "2026-05-23",
"deck_rendering": "blueprint",
"deck_palette": "cool-corporate",
"color_scheme": {
"primary": "#1B3A5C",
"secondary": "#F4F7FA",
"accent": "#E8743B"
},
"items": [
{
"filename": "cover_hero.png",
"purpose": "Cover hero visual (Slide 01)",
"page_role": "hero_page",
"text_policy": "embedded",
"aspect_ratio": "16:9",
"image_size": "2K",
"prompt": "Technical blueprint schematic style: clean precise lines on a subtle low-opacity grid, deliberate geometric rigor with right angles and measured spacing, elements simplified to essential schematic forms (boxes, rounded rectangles, connector lines, anchor dots), no textures or shading, engineering-precise and analytical. Color behavior is restrained-corporate: a near-white field #F4F7FA carries about 62% of the canvas as calm breathing space, deep navy #1B3A5C forms the main schematic structure and lines (about 30%), warm orange #E8743B appears only on the single injected low-rank branch and one small anchor dot (under 8%). Composition: one dominant concept centered with generous negative space — a large frozen pretrained weight matrix drawn as a big square grid block marked with a small padlock glyph to signal it is frozen, and a slim parallel branch beside it made of two thin rectangular matrices, a tall-narrow down-projection labelled 'A' and a wide-short up-projection labelled 'B' meeting at a narrow bottleneck labelled 'r', their connector lines summing into the block's output. Embedded designed lettering, part of the artwork: a large title reading 'LoRA' and a smaller tagline 'Low-Rank Adaptation', set as clean geometric sans-serif. Composed as a 1280x720 hero image for hero_page use; keep the lower third relatively calm and uncluttered so an SVG subtitle can overlay it. Render the HEX values only as colors, never as literal text in the image; keep every form simplified and schematic.",
"alt_text": "Blueprint schematic: a frozen pretrained weight block with a small low-rank A-to-B branch injected, titled LoRA",
"status": "Generated"
},
{
"filename": "cost_explosion.png",
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"type": "scene",
"page_role": "local",
"text_policy": "embedded",
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"image_size": "1K",
"prompt": "Technical blueprint schematic style: clean precise lines on a subtle low-opacity grid, simplified schematic forms, no textures or shading, engineering-precise and analytical. Color behavior is restrained-corporate: near-white field #F4F7FA dominates as calm space (about 60%), deep navy #1B3A5C carries the structural forms (about 32%), warm orange #E8743B appears only on a single small cost-warning marker (under 6%). Composition for a local block whose subject bleeds off the right canvas edge: one very large model block on the left, then the same model replicated into a receding row of identical heavy server-rack stacks marching toward and off the right edge, each rack tagged with a short embedded label '175B', conveying 'one full-parameter copy stored per task'; a small multiplication motif 'x N' near the receding stacks. Keep the subject weighted to the right two-thirds and the left third calmer so SVG text can sit there. Composed as a 1280x720 image for local use. Embedded text is limited to the short stable labels '175B' and 'x N'. Render the HEX values only as colors, never as literal text; keep every form simplified and schematic.",
"alt_text": "A 175B model copied into many identical heavy server racks, one per task",
"status": "Generated"
},
{
"filename": "lowrank_insight.png",
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"type": "scene",
"page_role": "local",
"text_policy": "embedded",
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"image_size": "1K",
"prompt": "Technical blueprint schematic style: clean precise lines on a subtle low-opacity grid, simplified schematic forms, no textures or shading, engineering-precise and analytical. Color behavior is restrained-corporate: near-white field #F4F7FA carries most of the canvas as generous whitespace (about 65%), deep navy #1B3A5C draws the structure (about 28%), warm orange #E8743B highlights only the two or three principal-direction arrows (under 7%). Composition: a diffuse cloud of fine points in an implied high-dimensional space collapsing and projecting down onto a single thin low-dimensional plane sheet; on that plane, two or three bold principal-direction arrows in orange carry the main structure, while the off-plane scatter fades to faint navy. Lots of surrounding negative space, the figure floating near center. Embedded short labels, part of the diagram: 'low-rank subspace' on the plane and 'intrinsic rank r' beside the principal arrows. Composed as a 1280x720 image for local use. Render the HEX values only as colors, never as literal text; keep every form simplified and schematic.",
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"status": "Generated"
},
{
"filename": "reparam_diagram_cropped.png",
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"type": "flowchart",
"page_role": "local",
"text_policy": "embedded",
"aspect_ratio": "16:9",
"image_size": "2K",
"prompt": "Technical blueprint schematic style: clean precise lines on a subtle low-opacity grid, deliberate geometric rigor, right-angle connector lines with small arrowheads and dot anchors at junctions, simplified schematic forms, no textures or shading, engineering-precise. Color behavior is restrained-corporate: near-white grid field #F4F7FA about 60%, deep navy #1B3A5C for lines and blocks about 32%, warm orange #E8743B only on the low-rank branch about 8%. Composition: a precise left-to-right signal-path schematic. An input node labelled 'x' on the left splits into two parallel paths that rejoin at a summation node drawn as a circled plus, producing an output labelled 'h' on the right. Top path: a large square block labelled 'Pretrained Weights W' with a small padlock glyph marking it frozen. Bottom path, highlighted in warm orange: a down-projection matrix labelled 'A' narrowing to a thin bottleneck labelled 'r', then an up-projection matrix labelled 'B'. Add an engineering-style dimension tick with end-caps spanning the bottleneck, annotated 'r is small'. Embedded labels are short technical identifiers, part of the schematic: 'x', 'h', 'W', 'A', 'B', 'r'. Composed as a 1280x720 image for local use; leave a calm horizontal band at the bottom so an SVG formula caption can overlay it. Render the HEX values only as colors, never as literal text; keep every form simplified and schematic.",
"alt_text": "Forward pass schematic: x feeds frozen W and a low-rank A-to-B branch, summed into h",
"status": "Generated"
},
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"filename": "attention_apply.png",
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"type": "flowchart",
"page_role": "local",
"text_policy": "embedded",
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"image_size": "1K",
"prompt": "Technical blueprint schematic style: clean precise lines on a subtle low-opacity grid, right-angle connectors with dot anchors, simplified schematic forms, no textures or shading, engineering-precise. Color behavior is restrained-corporate: near-white field #F4F7FA about 60%, deep navy #1B3A5C structure about 33%, warm orange #E8743B only on the two highlighted LoRA branches (under 7%). Composition: a Transformer self-attention module drawn as a clean vertical schematic stack of four projection-matrix boxes labelled 'Wq', 'Wk', 'Wv', 'Wo'; the 'Wq' and 'Wv' boxes each carry a small parallel low-rank LoRA branch highlighted in orange; below them an MLP block tagged with a small padlock and a short label 'frozen'. Connector lines route at right angles with small anchor dots. Place the schematic in the left portion of the canvas for a left-third placement, keeping the right side calmer for SVG text. Embedded labels are short stable identifiers: 'Wq', 'Wk', 'Wv', 'Wo', 'MLP', 'frozen'. Composed as a 1280x720 image for local use. Render the HEX values only as colors, never as literal text; keep every form simplified and schematic.",
"alt_text": "Transformer attention block with LoRA branches on Wq and Wv, MLP frozen",
"status": "Generated"
},
{
"filename": "subspace_heatmap.png",
"purpose": "Subspace similarity heatmap (Slide 14)",
"type": "scene",
"page_role": "local",
"text_policy": "embedded",
"aspect_ratio": "1:1",
"image_size": "1K",
"prompt": "Technical blueprint schematic style: crisp clean lines, precise grid, simplified forms, no textures or shading, analytical and engineering-precise. Color behavior is restrained-corporate built as an intensity scale: deep navy #1B3A5C for low values, warm orange #E8743B for high values, on a near-white frame #F4F7FA. Composition: a square correlation-style heatmap grid of cells showing a normalized subspace-similarity matrix; the top-left cells corresponding to top singular directions glow at high intensity in warm orange and rapidly fade to deep navy toward larger indices, so the bright signal concentrates in one corner. Crisp thin grid lines between cells; a vertical axis labelled 'i' and a horizontal axis labelled 'j'; a slim vertical intensity scale bar on the right running from '0' at the bottom to '1' at the top. Embedded text limited to the short stable labels 'i', 'j', '0', '1'. Composed as a 1024x1024 square image for local use. Render the HEX values only as colors, never as literal text; keep every form simplified and schematic.",
"alt_text": "Square subspace-similarity heatmap, bright in the top-left singular directions, fading elsewhere",
"status": "Generated"
}
]
}

123
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# Image Generation Prompts
> Auto-generated from `image_prompts.json` by `image_gen.py --render-md`.
> Do not hand-edit — re-run the command to refresh.
> Project: lora_hu_2021
> Generated: 2026-05-23
> Color scheme: Primary #1B3A5C | Secondary #F4F7FA | Accent #E8743B
---
### Image 1: cover_hero.png
| Attribute | Value |
|---|---|
| Purpose | Cover hero visual (Slide 01) |
| Aspect ratio | 16:9 |
| Image size | 2K |
| Status | Generated |
**Prompt**:
Technical blueprint schematic style: clean precise lines on a subtle low-opacity grid, deliberate geometric rigor with right angles and measured spacing, elements simplified to essential schematic forms (boxes, rounded rectangles, connector lines, anchor dots), no textures or shading, engineering-precise and analytical. Color behavior is restrained-corporate: a near-white field #F4F7FA carries about 62% of the canvas as calm breathing space, deep navy #1B3A5C forms the main schematic structure and lines (about 30%), warm orange #E8743B appears only on the single injected low-rank branch and one small anchor dot (under 8%). Composition: one dominant concept centered with generous negative space — a large frozen pretrained weight matrix drawn as a big square grid block marked with a small padlock glyph to signal it is frozen, and a slim parallel branch beside it made of two thin rectangular matrices, a tall-narrow down-projection labelled 'A' and a wide-short up-projection labelled 'B' meeting at a narrow bottleneck labelled 'r', their connector lines summing into the block's output. Embedded designed lettering, part of the artwork: a large title reading 'LoRA' and a smaller tagline 'Low-Rank Adaptation', set as clean geometric sans-serif. Composed as a 1280x720 hero image for hero_page use; keep the lower third relatively calm and uncluttered so an SVG subtitle can overlay it. Render the HEX values only as colors, never as literal text in the image; keep every form simplified and schematic.
**Alt Text**:
> Blueprint schematic: a frozen pretrained weight block with a small low-rank A-to-B branch injected, titled LoRA
---
### Image 2: cost_explosion.png
| Attribute | Value |
|---|---|
| Purpose | Cost of full fine-tuning at 175B scale (Slide 03) |
| Type | scene |
| Aspect ratio | 16:9 |
| Image size | 1K |
| Status | Generated |
**Prompt**:
Technical blueprint schematic style: clean precise lines on a subtle low-opacity grid, simplified schematic forms, no textures or shading, engineering-precise and analytical. Color behavior is restrained-corporate: near-white field #F4F7FA dominates as calm space (about 60%), deep navy #1B3A5C carries the structural forms (about 32%), warm orange #E8743B appears only on a single small cost-warning marker (under 6%). Composition for a local block whose subject bleeds off the right canvas edge: one very large model block on the left, then the same model replicated into a receding row of identical heavy server-rack stacks marching toward and off the right edge, each rack tagged with a short embedded label '175B', conveying 'one full-parameter copy stored per task'; a small multiplication motif 'x N' near the receding stacks. Keep the subject weighted to the right two-thirds and the left third calmer so SVG text can sit there. Composed as a 1280x720 image for local use. Embedded text is limited to the short stable labels '175B' and 'x N'. Render the HEX values only as colors, never as literal text; keep every form simplified and schematic.
**Alt Text**:
> A 175B model copied into many identical heavy server racks, one per task
---
### Image 3: lowrank_insight.png
| Attribute | Value |
|---|---|
| Purpose | Low intrinsic-rank insight (Slide 05) |
| Type | scene |
| Aspect ratio | 16:9 |
| Image size | 1K |
| Status | Generated |
**Prompt**:
Technical blueprint schematic style: clean precise lines on a subtle low-opacity grid, simplified schematic forms, no textures or shading, engineering-precise and analytical. Color behavior is restrained-corporate: near-white field #F4F7FA carries most of the canvas as generous whitespace (about 65%), deep navy #1B3A5C draws the structure (about 28%), warm orange #E8743B highlights only the two or three principal-direction arrows (under 7%). Composition: a diffuse cloud of fine points in an implied high-dimensional space collapsing and projecting down onto a single thin low-dimensional plane sheet; on that plane, two or three bold principal-direction arrows in orange carry the main structure, while the off-plane scatter fades to faint navy. Lots of surrounding negative space, the figure floating near center. Embedded short labels, part of the diagram: 'low-rank subspace' on the plane and 'intrinsic rank r' beside the principal arrows. Composed as a 1280x720 image for local use. Render the HEX values only as colors, never as literal text; keep every form simplified and schematic.
**Alt Text**:
> A high-dimensional weight-update cloud collapsing onto a low-dimensional subspace plane
---
### Image 4: reparam_diagram_cropped.png
| Attribute | Value |
|---|---|
| Purpose | LoRA reparametrization forward-pass schematic (Slide 06) |
| Type | flowchart |
| Aspect ratio | 16:9 |
| Image size | 2K |
| Status | Generated |
**Prompt**:
Technical blueprint schematic style: clean precise lines on a subtle low-opacity grid, deliberate geometric rigor, right-angle connector lines with small arrowheads and dot anchors at junctions, simplified schematic forms, no textures or shading, engineering-precise. Color behavior is restrained-corporate: near-white grid field #F4F7FA about 60%, deep navy #1B3A5C for lines and blocks about 32%, warm orange #E8743B only on the low-rank branch about 8%. Composition: a precise left-to-right signal-path schematic. An input node labelled 'x' on the left splits into two parallel paths that rejoin at a summation node drawn as a circled plus, producing an output labelled 'h' on the right. Top path: a large square block labelled 'Pretrained Weights W' with a small padlock glyph marking it frozen. Bottom path, highlighted in warm orange: a down-projection matrix labelled 'A' narrowing to a thin bottleneck labelled 'r', then an up-projection matrix labelled 'B'. Add an engineering-style dimension tick with end-caps spanning the bottleneck, annotated 'r is small'. Embedded labels are short technical identifiers, part of the schematic: 'x', 'h', 'W', 'A', 'B', 'r'. Composed as a 1280x720 image for local use; leave a calm horizontal band at the bottom so an SVG formula caption can overlay it. Render the HEX values only as colors, never as literal text; keep every form simplified and schematic.
**Alt Text**:
> Forward pass schematic: x feeds frozen W and a low-rank A-to-B branch, summed into h
---
### Image 5: attention_apply.png
| Attribute | Value |
|---|---|
| Purpose | LoRA applied to Transformer attention (Slide 08) |
| Type | flowchart |
| Aspect ratio | 16:9 |
| Image size | 1K |
| Status | Generated |
**Prompt**:
Technical blueprint schematic style: clean precise lines on a subtle low-opacity grid, right-angle connectors with dot anchors, simplified schematic forms, no textures or shading, engineering-precise. Color behavior is restrained-corporate: near-white field #F4F7FA about 60%, deep navy #1B3A5C structure about 33%, warm orange #E8743B only on the two highlighted LoRA branches (under 7%). Composition: a Transformer self-attention module drawn as a clean vertical schematic stack of four projection-matrix boxes labelled 'Wq', 'Wk', 'Wv', 'Wo'; the 'Wq' and 'Wv' boxes each carry a small parallel low-rank LoRA branch highlighted in orange; below them an MLP block tagged with a small padlock and a short label 'frozen'. Connector lines route at right angles with small anchor dots. Place the schematic in the left portion of the canvas for a left-third placement, keeping the right side calmer for SVG text. Embedded labels are short stable identifiers: 'Wq', 'Wk', 'Wv', 'Wo', 'MLP', 'frozen'. Composed as a 1280x720 image for local use. Render the HEX values only as colors, never as literal text; keep every form simplified and schematic.
**Alt Text**:
> Transformer attention block with LoRA branches on Wq and Wv, MLP frozen
---
### Image 6: subspace_heatmap.png
| Attribute | Value |
|---|---|
| Purpose | Subspace similarity heatmap (Slide 14) |
| Type | scene |
| Aspect ratio | 1:1 |
| Image size | 1K |
| Status | Generated |
**Prompt**:
Technical blueprint schematic style: crisp clean lines, precise grid, simplified forms, no textures or shading, analytical and engineering-precise. Color behavior is restrained-corporate built as an intensity scale: deep navy #1B3A5C for low values, warm orange #E8743B for high values, on a near-white frame #F4F7FA. Composition: a square correlation-style heatmap grid of cells showing a normalized subspace-similarity matrix; the top-left cells corresponding to top singular directions glow at high intensity in warm orange and rapidly fade to deep navy toward larger indices, so the bright signal concentrates in one corner. Crisp thin grid lines between cells; a vertical axis labelled 'i' and a horizontal axis labelled 'j'; a slim vertical intensity scale bar on the right running from '0' at the bottom to '1' at the top. Embedded text limited to the short stable labels 'i', 'j', '0', '1'. Composed as a 1024x1024 square image for local use. Render the HEX values only as colors, never as literal text; keep every form simplified and schematic.
**Alt Text**:
> Square subspace-similarity heatmap, bright in the top-left singular directions, fading elsewhere
---

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今天我们一起读一篇对大模型微调影响深远的论文,LoRA,低秩适配。它来自微软,二零二一年发表。一句话概括它的贡献:冻结预训练权重,只往每一层注入一对很小的低秩矩阵,就能用万分之一的可训练参数,达到甚至超过全量微调的效果,而且推理时不增加任何延迟。接下来我会从问题、方法、实验到背后的原理,带大家完整走一遍。

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我们按六个部分展开。先看问题:为什么模型越大,微调越来越贵;再看已有方法为什么不够好;然后是这篇论文的核心洞察,低秩假设;接着重点讲 LoRA 的方法与实现细节;之后用实验结果验证它;最后讨论该适配哪些权重、秩要取多大,以及它对整个行业的意义。重点会落在第四和第五部分。

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先说清楚痛点:模型越大,全量微调就越不可行。全量 fine-tuning 会更新模型的全部参数,得到的新模型和原模型一样大。以 GPT-3 一千七百五十亿参数为例,每适配一个下游任务,就要存一份完整的满参数副本。如果你有很多任务,存储和切换的成本就是任务数乘以一千七百五十亿,这已经从早年的"不太方便"升级成了真正的部署难题。

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那已有的高效适配方法为什么不够好?主要有三类局限。第一,Adapter 在网络里插入额外的层,加深了模型,会带来推理延迟,在线上、短序列、小批量的场景下尤其明显。第二,prefix 或 prompt tuning 用可训练的前缀 token 来适配,会挤占本就宝贵的输入序列长度。第三,也是最关键的,这些方法往往达不到全量微调的质量基线,逼着大家在效率和质量之间二选一。LoRA 想打破的,正是这个权衡。

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LoRA 的出发点是一个很漂亮的洞察:权重的更新其实是低秩的。已有研究发现,过参数化的大模型实际上存在于一个很低的内在维度上。作者顺着这个思路假设,模型在适配下游任务时,权重的变化量 Delta W 也具有很低的内在秩。这个推论很激进:即使权重的满秩高达一万两千多,实际需要的秩可能只有一或二就够了。

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基于这个假设,方法就非常简洁:冻结原始权重 W 零,在它旁边并联一条低秩旁路。具体说,把权重的更新约束成两个小矩阵的乘积 B 乘 A,前向传播时输入 x 同时经过冻结的 W 和这条旁路,两路输出相加得到 h。训练时 W 零完全不动,只更新 A 和 B 这两个小矩阵。初始化上,A 用高斯随机,B 置零,所以训练一开始旁路的贡献是零,不会扰动原模型。

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实现上有两个细节值得一提。第一是缩放:旁路的输出会乘上一个 alpha 除以 r 的系数,而 alpha 直接设成你尝试的第一个 r,之后就不再单独调它,这样变更秩的时候不用反复重调超参。第二是部署:上线时可以显式把 B 乘 A 合并进权重,得到一个和原模型完全同构的 W,所以不引入任何额外的推理延迟。要换任务也很简单,减掉当前的 BA,再加上另一个任务的 B 撇 A 撇就行,开销极小,非常适合多任务在线热插拔。

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在 Transformer 上,作者做了一个克制的选择。自注意力里有四个投影矩阵,查询、键、值、输出,论文在大多数实验里只给查询和值这两个矩阵加 LoRA,MLP 模块整个冻结,这是出于简洁和参数效率的考虑。可训练参数量只随秩 r 线性增长。效果非常惊人:在 GPT-3 上,训练显存从一点二个 TB 降到三百五十个 GB,大约只剩三分之一;而检查点的大小从三百五十个 GB 直接降到三十五个 MB,缩小了约一万倍。

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把这些好处归纳一下,LoRA 有四个关键优势。第一,可共享:一个预训练底座可以挂很多个小 LoRA 模块,换任务只换 A 和 B,存储成本极低。第二,训练高效:不用为冻结的参数保存梯度和优化器状态,硬件门槛最高能降到三分之一。第三,零推理延迟:合并权重后和全量微调模型完全同构。第四,正交可叠加:它和很多已有方法互不冲突,比如可以和 prefix-tuning 组合使用。

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我们用数据来验证"零延迟"这个说法。这张图是 GPT-2 medium 上单次前向的延迟增幅,在 RTX8000 上测了一百次取平均。可以看到,Adapter 的两个变体都会带来延迟:在长序列配置下还比较小,只有百分之二到三;但到了短序列、小批量这种典型的在线场景,Adapter L 增加了百分之二十点七,Adapter H 更是高达百分之三十点三。而 LoRA 因为可以合并权重,和全量微调一样,额外延迟是实打实的零。

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实验覆盖了从理解到生成的四类模型。自然语言理解这边,用 RoBERTa 的 base 和 large,还有十五亿参数的 DeBERTa XXL,都在 GLUE 上评测;生成这边,用 GPT-2 medium 跑 E2E 数据集;最后放大到 GPT-3 一千七百五十亿参数,在 WikiSQL、MNLI 和 SAMSum 上做压力测试。对比的基线也很全,包括全量微调、BitFit、前缀类方法,以及 Adapter 的四个变体,而且尽量复用了前人论文里报告的数值,保证公平。

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先看 GLUE 的结果,结论是:用更少的参数,持平甚至更优。RoBERTa base 全量微调要训一亿两千五百万参数,平均分八十六点四;LoRA 只训三十万参数,平均分反而是八十七点二。RoBERTa large 上,三亿五千五百万对零点八百万,得分八十八点九对八十九点零。最大的 DeBERTa XXL 上,十五亿对四百七十万,九十一点一对九十一点三。也就是说,LoRA 用大约三四百分之一的可训练参数,在三类模型上都做到了持平或略优。

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真正的压力测试是 GPT-3 一千七百五十亿参数,结论依然成立。这里有四个关键数字:可训练参数缩减了约一万倍,检查点从三百五十个 GB 降到三十五个 MB;训练显存从一点二个 TB 降到三百五十个 GB,大约三分之一;训练吞吐还提升了约百分之二十五,因为绝大多数参数不需要算梯度;而在 WikiSQL、MNLI、SAMSum 三个任务上,准确率都达到或超过了全量微调。效率和质量,这次真的可以兼得。

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论文还往前追问了两个问题。第一,该给哪些权重加 LoRA?在同样的参数预算下,同时适配查询和值这两类权重效果最好,比只堆在单一类型上更划算。第二,秩到底要取多大?这是最反直觉的发现:对查询加值的组合,秩等于一时准确率就有七十三点四,一路加到六十四几乎没有提升。右边的子空间相似度热力图也印证了这一点,不同秩学到的主奇异方向高度重叠,说明权重更新的内在秩确实极低。

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最后做个总结。从方法本身看,低秩适配等于参数高效、零额外推理延迟,再加上质量持平甚至更优。从工程意义看,它让一个预训练底座就能托管很多任务,按需热插拔 LoRA 模块,部署成本大幅下降。从行业影响看,LoRA 已经开源,成为今天大模型高效微调,也就是 PEFT 生态的基石之一。它最大的价值,是让我们不必再在效率和质量之间二选一。谢谢大家。

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# 01_cover
今天我们一起读一篇对大模型微调影响深远的论文,LoRA,低秩适配。它来自微软,二零二一年发表。一句话概括它的贡献:冻结预训练权重,只往每一层注入一对很小的低秩矩阵,就能用万分之一的可训练参数,达到甚至超过全量微调的效果,而且推理时不增加任何延迟。接下来我会从问题、方法、实验到背后的原理,带大家完整走一遍。
---
# 02_agenda
我们按六个部分展开。先看问题:为什么模型越大,微调越来越贵;再看已有方法为什么不够好;然后是这篇论文的核心洞察,低秩假设;接着重点讲 LoRA 的方法与实现细节;之后用实验结果验证它;最后讨论该适配哪些权重、秩要取多大,以及它对整个行业的意义。重点会落在第四和第五部分。
---
# 03_problem
先说清楚痛点:模型越大,全量微调就越不可行。全量 fine-tuning 会更新模型的全部参数,得到的新模型和原模型一样大。以 GPT-3 一千七百五十亿参数为例,每适配一个下游任务,就要存一份完整的满参数副本。如果你有很多任务,存储和切换的成本就是任务数乘以一千七百五十亿,这已经从早年的"不太方便"升级成了真正的部署难题。
---
# 04_limitations
那已有的高效适配方法为什么不够好?主要有三类局限。第一,Adapter 在网络里插入额外的层,加深了模型,会带来推理延迟,在线上、短序列、小批量的场景下尤其明显。第二,prefix 或 prompt tuning 用可训练的前缀 token 来适配,会挤占本就宝贵的输入序列长度。第三,也是最关键的,这些方法往往达不到全量微调的质量基线,逼着大家在效率和质量之间二选一。LoRA 想打破的,正是这个权衡。
---
# 05_insight
LoRA 的出发点是一个很漂亮的洞察:权重的更新其实是低秩的。已有研究发现,过参数化的大模型实际上存在于一个很低的内在维度上。作者顺着这个思路假设,模型在适配下游任务时,权重的变化量 Delta W 也具有很低的内在秩。这个推论很激进:即使权重的满秩高达一万两千多,实际需要的秩可能只有一或二就够了。
---
# 06_method
基于这个假设,方法就非常简洁:冻结原始权重 W 零,在它旁边并联一条低秩旁路。具体说,把权重的更新约束成两个小矩阵的乘积 B 乘 A,前向传播时输入 x 同时经过冻结的 W 和这条旁路,两路输出相加得到 h。训练时 W 零完全不动,只更新 A 和 B 这两个小矩阵。初始化上,A 用高斯随机,B 置零,所以训练一开始旁路的贡献是零,不会扰动原模型。
---
# 07_implementation
实现上有两个细节值得一提。第一是缩放:旁路的输出会乘上一个 alpha 除以 r 的系数,而 alpha 直接设成你尝试的第一个 r,之后就不再单独调它,这样变更秩的时候不用反复重调超参。第二是部署:上线时可以显式把 B 乘 A 合并进权重,得到一个和原模型完全同构的 W,所以不引入任何额外的推理延迟。要换任务也很简单,减掉当前的 BA,再加上另一个任务的 B 撇 A 撇就行,开销极小,非常适合多任务在线热插拔。
---
# 08_transformer
在 Transformer 上,作者做了一个克制的选择。自注意力里有四个投影矩阵,查询、键、值、输出,论文在大多数实验里只给查询和值这两个矩阵加 LoRA,MLP 模块整个冻结,这是出于简洁和参数效率的考虑。可训练参数量只随秩 r 线性增长。效果非常惊人:在 GPT-3 上,训练显存从一点二个 TB 降到三百五十个 GB,大约只剩三分之一;而检查点的大小从三百五十个 GB 直接降到三十五个 MB,缩小了约一万倍。
---
# 09_advantages
把这些好处归纳一下,LoRA 有四个关键优势。第一,可共享:一个预训练底座可以挂很多个小 LoRA 模块,换任务只换 A 和 B,存储成本极低。第二,训练高效:不用为冻结的参数保存梯度和优化器状态,硬件门槛最高能降到三分之一。第三,零推理延迟:合并权重后和全量微调模型完全同构。第四,正交可叠加:它和很多已有方法互不冲突,比如可以和 prefix-tuning 组合使用。
---
# 10_latency
我们用数据来验证"零延迟"这个说法。这张图是 GPT-2 medium 上单次前向的延迟增幅,在 RTX8000 上测了一百次取平均。可以看到,Adapter 的两个变体都会带来延迟:在长序列配置下还比较小,只有百分之二到三;但到了短序列、小批量这种典型的在线场景,Adapter L 增加了百分之二十点七,Adapter H 更是高达百分之三十点三。而 LoRA 因为可以合并权重,和全量微调一样,额外延迟是实打实的零。
---
# 11_setup
实验覆盖了从理解到生成的四类模型。自然语言理解这边,用 RoBERTa 的 base 和 large,还有十五亿参数的 DeBERTa XXL,都在 GLUE 上评测;生成这边,用 GPT-2 medium 跑 E2E 数据集;最后放大到 GPT-3 一千七百五十亿参数,在 WikiSQL、MNLI 和 SAMSum 上做压力测试。对比的基线也很全,包括全量微调、BitFit、前缀类方法,以及 Adapter 的四个变体,而且尽量复用了前人论文里报告的数值,保证公平。
---
# 12_glue
先看 GLUE 的结果,结论是:用更少的参数,持平甚至更优。RoBERTa base 全量微调要训一亿两千五百万参数,平均分八十六点四;LoRA 只训三十万参数,平均分反而是八十七点二。RoBERTa large 上,三亿五千五百万对零点八百万,得分八十八点九对八十九点零。最大的 DeBERTa XXL 上,十五亿对四百七十万,九十一点一对九十一点三。也就是说,LoRA 用大约三四百分之一的可训练参数,在三类模型上都做到了持平或略优。
---
# 13_gpt3
真正的压力测试是 GPT-3 一千七百五十亿参数,结论依然成立。这里有四个关键数字:可训练参数缩减了约一万倍,检查点从三百五十个 GB 降到三十五个 MB;训练显存从一点二个 TB 降到三百五十个 GB,大约三分之一;训练吞吐还提升了约百分之二十五,因为绝大多数参数不需要算梯度;而在 WikiSQL、MNLI、SAMSum 三个任务上,准确率都达到或超过了全量微调。效率和质量,这次真的可以兼得。
---
# 14_understanding
论文还往前追问了两个问题。第一,该给哪些权重加 LoRA?在同样的参数预算下,同时适配查询和值这两类权重效果最好,比只堆在单一类型上更划算。第二,秩到底要取多大?这是最反直觉的发现:对查询加值的组合,秩等于一时准确率就有七十三点四,一路加到六十四几乎没有提升。右边的子空间相似度热力图也印证了这一点,不同秩学到的主奇异方向高度重叠,说明权重更新的内在秩确实极低。
---
# 15_conclusion
最后做个总结。从方法本身看,低秩适配等于参数高效、零额外推理延迟,再加上质量持平甚至更优。从工程意义看,它让一个预训练底座就能托管很多任务,按需热插拔 LoRA 模块,部署成本大幅下降。从行业影响看,LoRA 已经开源,成为今天大模型高效微调,也就是 PEFT 生态的基石之一。它最大的价值,是让我们不必再在效率和质量之间二选一。谢谢大家。

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# Execution Lock
## canvas
- viewBox: 0 0 1280 720
- format: PPT 16:9
## colors
- bg: #FFFFFF
- secondary_bg: #F4F7FA
- primary: #1B3A5C
- accent: #E8743B
- secondary_accent: #3E7CB1
- text: #1D2733
- text_secondary: #5B6776
- text_tertiary: #8A94A1
- border: #D8DEE6
- success: #2E7D32
- warning: #C62828
- tint_primary: #EAF0F6
- tint_accent: #FBF0E9
- tint_success: #EAF6EE
- accent_light: #EE9461
- secondary_accent_light: #5C97C7
- cover_dark: #0F2238
- cover_text: #CBD8E6
- cover_text_muted: #9FB2C6
- image_rendering: blueprint
- image_palette: cool-corporate
## typography
- font_family: "Microsoft YaHei", Arial, sans-serif
- code_family: Consolas, "Courier New", monospace
- body: 18
- cover_title: 60
- hero_number: 56
- title: 32
- subtitle: 24
- annotation: 14
- chart_annotation: 13
- footnote: 11
## icons
- library: tabler-outline
- stroke_width: 2
- inventory: snowflake, lock, building-bank, server-2, alert-triangle, bulb, layers-subtract, math-function, transform, arrows-split, sitemap, topology-star, share-2, git-branch, bolt, gauge, puzzle, flask, database, chart-bar, circle-check, target
## images
- cover_hero: images/cover_hero.png
- cost_explosion: images/cost_explosion.png
- lowrank_insight: images/lowrank_insight.png
- reparam_diagram: images/reparam_diagram_cropped.png
- attention_apply: images/attention_apply.png
- subspace_heatmap: images/subspace_heatmap.png
- formula_001: images/formula_001.png | no-crop
- formula_002: images/formula_002.png | no-crop
- formula_003: images/formula_003.png | no-crop
- formula_004: images/formula_004.png | no-crop
## page_rhythm
- P01: anchor
- P02: anchor
- P03: breathing
- P04: dense
- P05: breathing
- P06: breathing
- P07: dense
- P08: dense
- P09: dense
- P10: dense
- P11: dense
- P12: dense
- P13: dense
- P14: dense
- P15: anchor
## page_charts
- P02: agenda_list
- P04: vertical_list
- P09: icon_grid
- P10: grouped_bar_chart
- P11: basic_table
- P12: consulting_table
- P13: kpi_cards
- P14: basic_table
- P15: vertical_list
## forbidden
- Mixing icon libraries
- rgba()
- `<style>`, `class`, `<foreignObject>`, `textPath`, `@font-face`, `<animate*>`, `<script>`, `<iframe>`, `<symbol>`+`<use>`
- `<g opacity>` (set opacity on each child element individually)
- HTML named entities in text (`&nbsp;`, `&mdash;`, `&copy;` …) — write raw Unicode; escape `& < > " '` as `&amp; &lt; &gt; &quot; &apos;`

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<text x="86" y="92" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">本次讲什么</text>
<text x="88" y="122" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">WHAT WE'LL COVER · 从问题到方法到验证</text>
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<text x="110" y="185" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="800" fill="#1B3A5C">01</text>
<text x="160" y="172" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">问题:微调为何越来越贵</text>
<text x="160" y="198" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="500" fill="#5B6776">大模型时代,全量 fine-tuning 的存储与部署成本失控</text>
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<text x="110" y="273" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="800" fill="#1B3A5C">02</text>
<text x="160" y="260" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">已有方法的局限</text>
<text x="160" y="286" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="500" fill="#5B6776">Adapter 增延迟、Prefix 占序列,且常达不到全量微调基线</text>
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<text x="110" y="361" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="800" fill="#1B3A5C">03</text>
<text x="160" y="348" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">核心洞察:低秩假设</text>
<text x="160" y="374" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="500" fill="#5B6776">权重更新 ΔW 具有低"内在秩",极小的 r 就够用</text>
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<circle cx="110" cy="442" r="24" fill="#E8743B" />
<text x="110" y="449" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="800" fill="#FFFFFF">04</text>
<text x="160" y="436" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">LoRA 方法与实现<tspan dx="10" font-size="12" font-weight="800" fill="#E8743B" letter-spacing="1">· 核心</tspan></text>
<text x="160" y="462" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="500" fill="#5B6776">冻结 W₀,注入低秩 BA;只训 A、B,部署可合并</text>
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<text x="110" y="537" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="800" fill="#1B3A5C">05</text>
<text x="160" y="524" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">实验结果</text>
<text x="160" y="550" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="500" fill="#5B6776">RoBERTa / DeBERTa / GPT-2 / GPT-3:更少参数,持平或更优</text>
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<text x="160" y="612" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">低秩理解与结论</text>
<text x="160" y="638" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="500" fill="#5B6776">该适配哪些权重、秩要多大,以及对工程落地的意义</text>
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<g id="footer">
<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">LoRA · Hu et al. 2021 · arXiv:2106.09685</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">02 / 15</text>
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<text x="86" y="90" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">为什么现有高效适配方法不够好</text>
<text x="88" y="120" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">LIMITS OF EXISTING EFFICIENT-ADAPTATION METHODS</text>
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<text x="300" y="216" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">Adapter 层 → 引入<tspan fill="#C62828">推理延迟</tspan></text>
<text x="300" y="248" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">在自注意力/MLP 后插入额外层,加深模型;在线、短序列、小 batch 场景下</text>
<text x="300" y="274" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">延迟增加尤为明显(后续 Table 1 量化)。</text>
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<text x="300" y="376" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">Prefix / Prompt tuning → <tspan fill="#C62828">占用输入长度</tspan></text>
<text x="300" y="408" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">用可训练的前缀 token 适配任务,会挤占可用序列长度;且前缀位置/数量</text>
<text x="300" y="434" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">敏感,优化较难。</text>
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<text x="120" y="559" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="800" fill="#FFFFFF">03</text>
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<text x="300" y="536" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">普遍 → <tspan fill="#C62828">达不到全量微调基线</tspan></text>
<text x="300" y="568" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">多数高效方法在质量上落后于 full fine-tuning,迫使人在"效率"与"质量"</text>
<text x="300" y="594" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">之间二选一 —— 这正是 LoRA 想打破的权衡。</text>
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<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA §1–3 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">04 / 15</text>
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<text x="86" y="90" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">一个常数 α、一次部署合并</text>
<text x="88" y="120" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">INITIALIZATION, SCALING &amp; DEPLOYMENT</text>
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<text x="150" y="222" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">缩放:一个常数 α</text>
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<text x="104" y="494" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">ΔWx 按 <tspan font-style="italic" font-weight="800" fill="#E8743B">α/r</tspan> 缩放</text>
<text x="104" y="528" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">α 设为首个尝试的 r,<tspan font-weight="800" fill="#1B3A5C">不再单独调参</tspan></text>
<text x="104" y="562" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">变更 r 时减少超参重调的需要</text>
<text x="104" y="600" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" fill="#8A94A1">用 Adam 时,调 α 约等于调学习率</text>
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<text x="742" y="222" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">部署:合并权重,零额外结构</text>
<text x="696" y="278" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">显式计算并存储 <tspan font-weight="800" fill="#1B3A5C">W = W₀ + BA</tspan></text>
<text x="696" y="312" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">推理时与原模型完全同构</text>
<text x="696" y="346" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">→ <tspan font-weight="800" fill="#2E7D32">不引入任何额外推理延迟</tspan></text>
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<text x="742" y="474" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">换任务:快速热插拔</text>
<text x="696" y="530" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">恢复 W₀ 只需<tspan font-weight="800" fill="#1B3A5C">减去 BA</tspan></text>
<text x="696" y="564" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">再加上另一任务的 <tspan font-weight="800" fill="#E8743B">B′A′</tspan> 即可</text>
<text x="696" y="598" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">内存开销极小,适合多任务在线切换</text>
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<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA §4.1–4.2 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">07 / 15</text>
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<text x="86" y="90" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">LoRA 的四个关键优势</text>
<text x="88" y="120" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">FOUR KEY ADVANTAGES</text>
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<text x="96" y="284" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">可共享</text>
<text x="96" y="306" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" font-weight="700" fill="#1B3A5C" letter-spacing="1.5">ONE BASE · MANY MODULES</text>
<text x="96" y="338" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">一个预训练底座 + 多个小 LoRA 模块;</text>
<text x="96" y="360" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">换任务只需替换 <tspan font-weight="800" fill="#1B3A5C">A、B</tspan>,显著降低存储。</text>
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<path fill="#EAF0F6" d="M702,186 H730 A14,14 0 0 1 744,200 V228 A14,14 0 0 1 730,242 H702 A14,14 0 0 1 688,228 V200 A14,14 0 0 1 702,186 Z" />
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<text x="688" y="284" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">高效训练</text>
<text x="688" y="306" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" font-weight="700" fill="#3E7CB1" letter-spacing="1.5">LOWER HARDWARE BARRIER</text>
<text x="688" y="338" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">无需为冻结参数存梯度与优化器状态;</text>
<text x="688" y="360" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">硬件门槛最高降低 <tspan font-weight="800" fill="#1B3A5C">3×</tspan>。</text>
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<text x="600" y="468" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="52" font-weight="800" fill="#E8743B" fill-opacity="0.10">03</text>
<path fill="#FBF0E9" d="M110,436 H138 A14,14 0 0 1 152,450 V478 A14,14 0 0 1 138,492 H110 A14,14 0 0 1 96,478 V450 A14,14 0 0 1 110,436 Z" />
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<path d="M3 12a9 9 0 1 0 18 0a9 9 0 1 0 -18 0" />
<path d="M11 12a1 1 0 1 0 2 0a1 1 0 1 0 -2 0" />
<path d="M13.41 10.59l2.59 -2.59" />
<path d="M7 12a5 5 0 0 1 5 -5" />
</g>
<text x="96" y="534" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">零推理延迟</text>
<text x="96" y="556" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" font-weight="700" fill="#E8743B" letter-spacing="1.5">NO ADDED INFERENCE LATENCY</text>
<text x="96" y="588" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">部署时合并 W = W₀+BA;</text>
<text x="96" y="610" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">与全量微调模型<tspan font-weight="800" fill="#2E7D32">同构、不增延迟</tspan>。</text>
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<g id="adv-04">
<path fill="#FFFFFF" stroke="#D8DEE6" stroke-width="1" d="M672,408 H1200 A16,16 0 0 1 1216,424 V618 A16,16 0 0 1 1200,634 H672 A16,16 0 0 1 656,618 V424 A16,16 0 0 1 672,408 Z" />
<text x="1192" y="468" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="52" font-weight="800" fill="#1B3A5C" fill-opacity="0.07">04</text>
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<text x="688" y="534" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">正交可叠加</text>
<text x="688" y="556" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" font-weight="700" fill="#1B3A5C" letter-spacing="1.5">ORTHOGONAL · COMPOSABLE</text>
<text x="688" y="588" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">与许多既有方法正交,</text>
<text x="688" y="610" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">可与 <tspan font-weight="800" fill="#1B3A5C">prefix-tuning</tspan> 等组合使用。</text>
</g>
<g id="footer">
<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA §1, §4.1 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">09 / 15</text>
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<text x="86" y="90" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">Adapter 增延迟,LoRA 不增</text>
<text x="88" y="120" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">GPT-2 MEDIUM 单次前向延迟增幅(100 次平均, RTX8000)</text>
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<path fill="url(#barAdptL)" d="M823,150 H835 A3,3 0 0 1 838,153 V165 A3,3 0 0 1 835,168 H823 A3,3 0 0 1 820,165 V153 A3,3 0 0 1 823,150 Z" />
<text x="846" y="165" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">Adapter L</text>
<path fill="url(#barAdptH)" d="M963,150 H975 A3,3 0 0 1 978,153 V165 A3,3 0 0 1 975,168 H963 A3,3 0 0 1 960,165 V153 A3,3 0 0 1 963,150 Z" />
<text x="986" y="165" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">Adapter H</text>
<path fill="#2E7D32" d="M1103,150 H1115 A3,3 0 0 1 1118,153 V153 A3,3 0 0 1 1115,156 H1103 A3,3 0 0 1 1100,153 V153 A3,3 0 0 1 1103,150 Z" />
<text x="1126" y="165" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">LoRA / FT</text>
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<line x1="160" y1="343" x2="1180" y2="343" stroke="#D8DEE6" stroke-width="1" stroke-dasharray="4,4" />
<line x1="160" y1="451" x2="1180" y2="451" stroke="#D8DEE6" stroke-width="1" stroke-dasharray="4,4" />
<text x="145" y="239" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" fill="#8A94A1">+30%</text>
<text x="145" y="348" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" fill="#8A94A1">+20%</text>
<text x="145" y="456" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" fill="#8A94A1">+10%</text>
<text x="145" y="565" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" fill="#8A94A1">0</text>
<line x1="160" y1="180" x2="160" y2="560" stroke="#8A94A1" stroke-width="2" />
<line x1="160" y1="560" x2="1180" y2="560" stroke="#2E7D32" stroke-width="4" />
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<text x="318" y="528" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#3E7CB1">+2.2%</text>
<path fill="url(#barAdptH)" d="M370,527 H438 A4,4 0 0 1 442,531 V556 A4,4 0 0 1 438,560 H370 A4,4 0 0 1 366,556 V531 A4,4 0 0 1 370,527 Z" />
<text x="404" y="519" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#E8743B">+3.0%</text>
<text x="361" y="590" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">配置 A · 长序列</text>
<path fill="url(#barAdptL)" d="M604,506 H672 A4,4 0 0 1 676,510 V556 A4,4 0 0 1 672,560 H604 A4,4 0 0 1 600,556 V510 A4,4 0 0 1 604,506 Z" />
<text x="638" y="498" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#3E7CB1">+5.0%</text>
<path fill="url(#barAdptH)" d="M690,469 H758 A4,4 0 0 1 762,473 V556 A4,4 0 0 1 758,560 H690 A4,4 0 0 1 686,556 V473 A4,4 0 0 1 690,469 Z" />
<text x="724" y="461" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#E8743B">+8.4%</text>
<text x="681" y="590" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">配置 B · 中等</text>
<path fill="url(#barAdptL)" d="M924,335 H992 A4,4 0 0 1 996,339 V556 A4,4 0 0 1 992,560 H924 A4,4 0 0 1 920,556 V339 A4,4 0 0 1 924,335 Z" />
<text x="958" y="327" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#3E7CB1">+20.7%</text>
<path fill="url(#barAdptH)" d="M1010,231 H1078 A4,4 0 0 1 1082,235 V556 A4,4 0 0 1 1078,560 H1010 A4,4 0 0 1 1006,556 V235 A4,4 0 0 1 1010,231 Z" />
<text x="1044" y="223" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#E8743B">+30.3%</text>
<text x="1001" y="590" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">配置 C · 短序列·在线</text>
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<text x="170" y="615" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="800" fill="#2E7D32" id="_edit_39">LoRA / FT 基线 = 0% 额外延迟(合并权重后)</text>
<text x="1180" y="620" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#1D2733">序列越短、batch 越小,Adapter 的延迟代价越突出</text>
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<g id="footer">
<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA Table 1 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">10 / 15</text>
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1280 720" width="1280" height="720">
<g id="bg"><rect width="1280" height="720" fill="#FFFFFF" /></g>
<g id="header">
<path fill="#E8743B" d="M67,56 H67 A3,3 0 0 1 70,59 V109 A3,3 0 0 1 67,112 H67 A3,3 0 0 1 64,109 V59 A3,3 0 0 1 67,56 Z" />
<text x="86" y="90" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">覆盖 NLU 到 NLG 的四类模型</text>
<text x="88" y="120" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">EVALUATION SETUP · ROBERTA / DEBERTA / GPT-2 / GPT-3</text>
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<rect x="0" y="0" width="1152" height="56" fill="#F4F7FA" />
<line x1="0" y1="56" x2="1152" y2="56" stroke="#D8DEE6" stroke-width="2" />
<text x="24" y="35" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#1B3A5C" letter-spacing="1">模型 MODEL</text>
<text x="470" y="35" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#1B3A5C" letter-spacing="1">参数量</text>
<text x="600" y="35" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#1B3A5C" letter-spacing="1">基准 BENCHMARK</text>
<text x="930" y="35" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#1B3A5C" letter-spacing="1">任务</text>
<text x="1128" y="35" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#1B3A5C" letter-spacing="1">LoRA 可训练参数</text>
<line x1="420" y1="0" x2="420" y2="392" stroke="#D8DEE6" stroke-width="1.5" />
<line x1="560" y1="0" x2="560" y2="392" stroke="#D8DEE6" stroke-width="1.5" />
<line x1="850" y1="0" x2="850" y2="392" stroke="#D8DEE6" stroke-width="1.5" />
<line x1="1010" y1="0" x2="1010" y2="392" stroke="#D8DEE6" stroke-width="1.5" />
<line x1="0" y1="123" x2="1152" y2="123" stroke="#D8DEE6" stroke-width="1" />
<text x="24" y="98" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">RoBERTa base</text>
<text x="470" y="98" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">125M</text>
<text x="584" y="98" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">GLUE</text>
<text x="930" y="98" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">NLU</text>
<text x="1128" y="98" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="700" fill="#1B3A5C">0.3M</text>
<line x1="0" y1="190" x2="1152" y2="190" stroke="#D8DEE6" stroke-width="1" />
<text x="24" y="165" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">RoBERTa large</text>
<text x="470" y="165" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">355M</text>
<text x="584" y="165" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">GLUE</text>
<text x="930" y="165" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">NLU</text>
<text x="1128" y="165" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="700" fill="#1B3A5C">0.8M</text>
<line x1="0" y1="257" x2="1152" y2="257" stroke="#D8DEE6" stroke-width="1" />
<text x="24" y="232" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">DeBERTa XXL</text>
<text x="470" y="232" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">1.5B</text>
<text x="584" y="232" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">GLUE</text>
<text x="930" y="232" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">NLU</text>
<text x="1128" y="232" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="700" fill="#1B3A5C">4.7M</text>
<line x1="0" y1="324" x2="1152" y2="324" stroke="#D8DEE6" stroke-width="1" />
<text x="24" y="299" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">GPT-2 medium</text>
<text x="470" y="299" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">354M</text>
<text x="584" y="299" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">E2E NLG</text>
<text x="930" y="299" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">NLG</text>
<text x="1128" y="299" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="700" fill="#1B3A5C">0.35M</text>
<rect x="0" y="324" width="1152" height="68" fill="#FBF0E9" />
<text x="24" y="366" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="800" fill="#1D2733">GPT-3</text>
<text x="470" y="366" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="800" fill="#E8743B">175B</text>
<text x="584" y="366" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">WikiSQL · MNLI · SAMSum</text>
<text x="930" y="366" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">NLU+NLG</text>
<text x="1128" y="366" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="800" fill="#E8743B">4.7–37.7M</text>
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<path d="M9 3l6 0" />
<path d="M10 9l4 0" />
<path d="M10 3v6l-4 11a.7 .7 0 0 0 .5 1h11a.7 .7 0 0 0 .5 -1l-4 -11v-6" />
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<text x="102" y="604" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#1D2733">对比基线:Fine-Tuning(FT)、BitFit、PreEmbed / PreLayer、Adapter H / L / P / D —— 尽可能复用前作报告数值</text>
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<g id="footer">
<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA §5–5.1 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">11 / 15</text>
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<text x="86" y="90" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">更少参数,持平或更优</text>
<text x="88" y="120" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">GLUE BENCHMARK · OVERALL AVG SCORE(越高越好)</text>
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<text x="20" y="26" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#8A94A1" letter-spacing="1">模型</text>
<text x="430" y="26" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#8A94A1" letter-spacing="1">方法</text>
<text x="700" y="26" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#8A94A1" letter-spacing="1"># 可训练参数</text>
<text x="850" y="26" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#8A94A1" letter-spacing="1">GLUE AVG</text>
<text x="900" y="26" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#8A94A1" letter-spacing="1">得分(视觉)</text>
<text x="20" y="86" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">RoBERTa base</text>
<path fill="#F4F7FA" d="M393,68 H467 A13,13 0 0 1 480,81 V81 A13,13 0 0 1 467,94 H393 A13,13 0 0 1 380,81 V81 A13,13 0 0 1 393,68 Z" />
<text x="430" y="86" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="700" fill="#5B6776">FT</text>
<text x="700" y="86" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">125M</text>
<text x="850" y="86" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">86.4</text>
<path fill="#F4F7FA" d="M907,74 H1133 A7,7 0 0 1 1140,81 V81 A7,7 0 0 1 1133,88 H907 A7,7 0 0 1 900,81 V81 A7,7 0 0 1 907,74 Z" />
<path fill="#8A94A1" d="M907,74 H965 A7,7 0 0 1 972,81 V81 A7,7 0 0 1 965,88 H907 A7,7 0 0 1 900,81 V81 A7,7 0 0 1 907,74 Z" />
<path fill="#FBF0E9" d="M6,104 H1146 A6,6 0 0 1 1152,110 V150 A6,6 0 0 1 1146,156 H6 A6,6 0 0 1 0,150 V110 A6,6 0 0 1 6,104 Z" />
<text x="20" y="138" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">RoBERTa base</text>
<path fill="#1B3A5C" d="M393,120 H467 A13,13 0 0 1 480,133 V133 A13,13 0 0 1 467,146 H393 A13,13 0 0 1 380,133 V133 A13,13 0 0 1 393,120 Z" />
<text x="430" y="138" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="700" fill="#FFFFFF">LoRA</text>
<text x="700" y="138" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#E8743B">0.3M</text>
<text x="850" y="138" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="800" fill="#2E7D32">87.2</text>
<path fill="#F4F7FA" d="M907,126 H1133 A7,7 0 0 1 1140,133 V133 A7,7 0 0 1 1133,140 H907 A7,7 0 0 1 900,133 V133 A7,7 0 0 1 907,126 Z" />
<path fill="url(#avgBar)" d="M907,126 H989 A7,7 0 0 1 996,133 V133 A7,7 0 0 1 989,140 H907 A7,7 0 0 1 900,133 V133 A7,7 0 0 1 907,126 Z" />
<text x="20" y="200" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">RoBERTa large</text>
<path fill="#F4F7FA" d="M393,182 H467 A13,13 0 0 1 480,195 V195 A13,13 0 0 1 467,208 H393 A13,13 0 0 1 380,195 V195 A13,13 0 0 1 393,182 Z" />
<text x="430" y="200" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="700" fill="#5B6776">FT</text>
<text x="700" y="200" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">355M</text>
<text x="850" y="200" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">88.9</text>
<path fill="#F4F7FA" d="M907,188 H1133 A7,7 0 0 1 1140,195 V195 A7,7 0 0 1 1133,202 H907 A7,7 0 0 1 900,195 V195 A7,7 0 0 1 907,188 Z" />
<path fill="#8A94A1" d="M907,188 H1040 A7,7 0 0 1 1047,195 V195 A7,7 0 0 1 1040,202 H907 A7,7 0 0 1 900,195 V195 A7,7 0 0 1 907,188 Z" />
<path fill="#FBF0E9" d="M6,218 H1146 A6,6 0 0 1 1152,224 V264 A6,6 0 0 1 1146,270 H6 A6,6 0 0 1 0,264 V224 A6,6 0 0 1 6,218 Z" />
<text x="20" y="252" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">RoBERTa large</text>
<path fill="#1B3A5C" d="M393,234 H467 A13,13 0 0 1 480,247 V247 A13,13 0 0 1 467,260 H393 A13,13 0 0 1 380,247 V247 A13,13 0 0 1 393,234 Z" />
<text x="430" y="252" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="700" fill="#FFFFFF">LoRA</text>
<text x="700" y="252" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#E8743B">0.8M</text>
<text x="850" y="252" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="800" fill="#2E7D32">89.0</text>
<path fill="#F4F7FA" d="M907,240 H1133 A7,7 0 0 1 1140,247 V247 A7,7 0 0 1 1133,254 H907 A7,7 0 0 1 900,247 V247 A7,7 0 0 1 907,240 Z" />
<path fill="url(#avgBar)" d="M907,240 H1043 A7,7 0 0 1 1050,247 V247 A7,7 0 0 1 1043,254 H907 A7,7 0 0 1 900,247 V247 A7,7 0 0 1 907,240 Z" />
<text x="20" y="314" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">DeBERTa XXL</text>
<path fill="#F4F7FA" d="M393,296 H467 A13,13 0 0 1 480,309 V309 A13,13 0 0 1 467,322 H393 A13,13 0 0 1 380,309 V309 A13,13 0 0 1 393,296 Z" />
<text x="430" y="314" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="700" fill="#5B6776">FT</text>
<text x="700" y="314" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">1500M</text>
<text x="850" y="314" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">91.1</text>
<path fill="#F4F7FA" d="M907,302 H1133 A7,7 0 0 1 1140,309 V309 A7,7 0 0 1 1133,316 H907 A7,7 0 0 1 900,309 V309 A7,7 0 0 1 907,302 Z" />
<path fill="#8A94A1" d="M907,302 H1106 A7,7 0 0 1 1113,309 V309 A7,7 0 0 1 1106,316 H907 A7,7 0 0 1 900,309 V309 A7,7 0 0 1 907,302 Z" />
<path fill="#FBF0E9" d="M6,332 H1146 A6,6 0 0 1 1152,338 V378 A6,6 0 0 1 1146,384 H6 A6,6 0 0 1 0,378 V338 A6,6 0 0 1 6,332 Z" />
<text x="20" y="366" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">DeBERTa XXL</text>
<path fill="#1B3A5C" d="M393,348 H467 A13,13 0 0 1 480,361 V361 A13,13 0 0 1 467,374 H393 A13,13 0 0 1 380,361 V361 A13,13 0 0 1 393,348 Z" />
<text x="430" y="366" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="700" fill="#FFFFFF">LoRA</text>
<text x="700" y="366" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#E8743B">4.7M</text>
<text x="850" y="366" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="800" fill="#2E7D32">91.3</text>
<path fill="#F4F7FA" d="M907,354 H1133 A7,7 0 0 1 1140,361 V361 A7,7 0 0 1 1133,368 H907 A7,7 0 0 1 900,361 V361 A7,7 0 0 1 907,354 Z" />
<path fill="url(#avgBar)" d="M907,354 H1112 A7,7 0 0 1 1119,361 V361 A7,7 0 0 1 1112,368 H907 A7,7 0 0 1 900,361 V361 A7,7 0 0 1 907,354 Z" />
</g>
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<g id="takeaway">
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<path d="M9 12l2 2l4 -4" />
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<text x="128" y="600" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">三类模型上,LoRA 用<tspan font-weight="800" fill="#E8743B"> 1/300 ~ 1/400 </tspan>的可训练参数,GLUE 平均分<tspan font-weight="800" fill="#2E7D32">持平甚至略优于</tspan>全量微调</text>
</g>
<g id="footer">
<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA Table 2 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">12 / 15</text>
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1280 720" width="1280" height="720">
<g id="bg"><rect width="1280" height="720" fill="#FFFFFF" /></g>
<g id="header">
<path fill="#E8743B" d="M67,56 H67 A3,3 0 0 1 70,59 V109 A3,3 0 0 1 67,112 H67 A3,3 0 0 1 64,109 V59 A3,3 0 0 1 67,56 Z" />
<text x="86" y="90" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">在 175B 尺度上仍然成立</text>
<text x="88" y="120" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">GPT-3 175B · 关键指标(对比全量微调)</text>
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<path d="M4 12v6a8 3 0 0 0 16 0v-6" />
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<text x="170" y="212" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#5B6776">可训练参数缩减</text>
<text x="100" y="312" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="64" font-weight="800" fill="#E8743B">10,000×</text>
<text x="100" y="354" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">checkpoint 350GB → <tspan font-weight="800" fill="#1B3A5C">35MB</tspan></text>
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<path fill="#EAF0F6" d="M705,186 H731 A13,13 0 0 1 744,199 V225 A13,13 0 0 1 731,238 H705 A13,13 0 0 1 692,225 V199 A13,13 0 0 1 705,186 Z" />
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<path d="M3 7a3 3 0 0 1 3 -3h12a3 3 0 0 1 3 3v2a3 3 0 0 1 -3 3h-12a3 3 0 0 1 -3 -3v-2" />
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<text x="762" y="212" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#5B6776">训练显存 VRAM(原 1.2TB)</text>
<text x="692" y="312" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="64" font-weight="800" fill="#1B3A5C">350GB</text>
<circle cx="708" cy="350" r="14" fill="#EAF6EE" />
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<text x="732" y="356" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#2E7D32">约降至 1/3</text>
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<text x="170" y="462" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#5B6776">训练吞吐提升</text>
<text x="100" y="562" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="64" font-weight="800" fill="#2E7D32">+25%</text>
<text x="100" y="604" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">无需为绝大多数参数计算梯度</text>
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<text x="762" y="462" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#5B6776">任务准确率:持平 / 更优</text>
<text x="692" y="540" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="40" font-weight="800" fill="#1B3A5C">≥ 全量微调</text>
<text x="692" y="582" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">WikiSQL <tspan font-weight="800" fill="#2E7D32">74.0</tspan> · MNLI-m <tspan font-weight="800" fill="#2E7D32">91.7</tspan> · SAMSum <tspan font-weight="800" fill="#2E7D32">53.8</tspan></text>
<text x="692" y="606" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" fill="#8A94A1">三项均达到或超过 FT 基线</text>
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<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA §4.2, Table 4, §6 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">13 / 15</text>
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<text x="86" y="92" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">结论与影响</text>
<text x="88" y="122" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">CONCLUSION &amp; IMPACT</text>
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<text x="120" y="217" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="800" fill="#FFFFFF">01</text>
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<text x="224" y="208" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">方法本身</text>
<text x="224" y="240" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">低秩适配 = <tspan font-weight="800" fill="#1B3A5C">参数高效</tspan> + <tspan font-weight="800" fill="#2E7D32">零额外推理延迟</tspan> + 质量持平 / 更优</text>
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<text x="120" y="367" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="800" fill="#FFFFFF">02</text>
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<text x="224" y="358" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">工程意义</text>
<text x="224" y="390" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">一个预训练底座<tspan font-weight="800" fill="#1B3A5C">托管多任务</tspan>,按需热插拔 LoRA 模块,部署成本骤降</text>
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<text x="224" y="508" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">行业影响</text>
<text x="224" y="540" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">已开源,成为大模型微调的<tspan font-weight="800" fill="#E8743B">事实标准之一</tspan>(PEFT 生态基石)</text>
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<text x="106" y="624" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">让人不必在<tspan font-weight="800" fill="#1B3A5C">效率</tspan>与<tspan font-weight="800" fill="#1B3A5C">质量</tspan>之间二选一</text>
<text x="1216" y="624" text-anchor="end" font-family="Consolas, &quot;Courier New&quot;, monospace" font-size="16" font-weight="700" fill="#3E7CB1">github.com/microsoft/LoRA</text>
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<g id="footer">
<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA §8 Conclusion · Hu et al. 2021 · arXiv:2106.09685</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">15 / 15</text>
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<text x="64" y="524" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="58" font-weight="800" fill="#FFFFFF">LoRA:大模型的低秩适配</text>
<text x="64" y="566" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="24" font-weight="600" fill="#CBD8E6">Low-Rank Adaptation of Large Language Models</text>
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<text x="64" y="636" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="600" fill="#9FB2C6">Hu, Shen, Wallis, Allen-Zhu, Li, Wang, Wang, Chen · Microsoft · 2021</text>
<text x="64" y="664" font-family="Consolas, &quot;Courier New&quot;, monospace" font-size="15" fill="#E8743B">arXiv:2106.09685v2</text>
<text x="1216" y="664" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#9FB2C6">论文解读 · Paper Walkthrough</text>
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<text x="86" y="92" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">本次讲什么</text>
<text x="88" y="122" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">WHAT WE'LL COVER · 从问题到方法到验证</text>
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<text x="160" y="172" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">问题:微调为何越来越贵</text>
<text x="160" y="198" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="500" fill="#5B6776">大模型时代,全量 fine-tuning 的存储与部署成本失控</text>
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<text x="160" y="260" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">已有方法的局限</text>
<text x="160" y="286" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="500" fill="#5B6776">Adapter 增延迟、Prefix 占序列,且常达不到全量微调基线</text>
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<circle cx="110" cy="354" r="22" fill="#FFFFFF" stroke="#1B3A5C" stroke-width="3"/>
<text x="110" y="361" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="800" fill="#1B3A5C">03</text>
<text x="160" y="348" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">核心洞察:低秩假设</text>
<text x="160" y="374" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="500" fill="#5B6776">权重更新 ΔW 具有低&quot;内在秩&quot;,极小的 r 就够用</text>
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<text x="110" y="449" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="800" fill="#FFFFFF">04</text>
<text x="160" y="436" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">LoRA 方法与实现<tspan dx="10" font-size="12" font-weight="800" fill="#E8743B" letter-spacing="1">· 核心</tspan></text>
<text x="160" y="462" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="500" fill="#5B6776">冻结 W₀,注入低秩 BA;只训 A、B,部署可合并</text>
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<text x="110" y="537" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="800" fill="#1B3A5C">05</text>
<text x="160" y="524" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">实验结果</text>
<text x="160" y="550" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="500" fill="#5B6776">RoBERTa / DeBERTa / GPT-2 / GPT-3:更少参数,持平或更优</text>
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<circle cx="110" cy="618" r="22" fill="#FFFFFF" stroke="#1B3A5C" stroke-width="3"/>
<text x="110" y="625" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="800" fill="#1B3A5C">06</text>
<text x="160" y="612" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">低秩理解与结论</text>
<text x="160" y="638" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="500" fill="#5B6776">该适配哪些权重、秩要多大,以及对工程落地的意义</text>
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<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">LoRA · Hu et al. 2021 · arXiv:2106.09685</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">02 / 15</text>
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<text x="86" y="92" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">模型越大,全量微调越不可行</text>
<text x="88" y="122" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">THE COST OF FULL FINE-TUNING</text>
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<text x="64" y="320" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="88" font-weight="800" fill="#E8743B">175B</text>
<text x="68" y="362" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="700" fill="#1B3A5C">每个下游任务都要存一份满参数副本</text>
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<text x="64" y="452" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="18" fill="#1D2733">全量 fine-tuning 会更新<tspan font-weight="800" fill="#1B3A5C">全部参数</tspan>,新模型与原模型一样大</text>
<text x="64" y="492" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="18" fill="#1D2733">部署 N 个任务 = <tspan font-weight="800" fill="#C62828">N × 175B</tspan>,存储与切换成本极高</text>
<text x="64" y="532" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="18" fill="#1D2733">以 GPT-3 175B 为例,已从"不便"升级为<tspan font-weight="800" fill="#1B3A5C">部署难题</tspan></text>
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<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA §1 Introduction · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">03 / 15</text>
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<text x="86" y="90" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">为什么现有高效适配方法不够好</text>
<text x="88" y="120" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">LIMITS OF EXISTING EFFICIENT-ADAPTATION METHODS</text>
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<rect x="190" y="170" width="6" height="128" rx="3" fill="#1B3A5C"/>
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<text x="300" y="216" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">Adapter 层 → 引入<tspan fill="#C62828">推理延迟</tspan></text>
<text x="300" y="248" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">在自注意力/MLP 后插入额外层,加深模型;在线、短序列、小 batch 场景下</text>
<text x="300" y="274" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">延迟增加尤为明显(后续 Table 1 量化)。</text>
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<text x="120" y="399" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="800" fill="#FFFFFF">02</text>
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<rect x="190" y="330" width="6" height="128" rx="3" fill="#3E7CB1"/>
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<text x="300" y="376" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">Prefix / Prompt tuning → <tspan fill="#C62828">占用输入长度</tspan></text>
<text x="300" y="408" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">用可训练的前缀 token 适配任务,会挤占可用序列长度;且前缀位置/数量</text>
<text x="300" y="434" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">敏感,优化较难。</text>
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<text x="120" y="559" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="800" fill="#FFFFFF">03</text>
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<rect x="190" y="490" width="6" height="128" rx="3" fill="#E8743B"/>
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<text x="300" y="536" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">普遍 → <tspan fill="#C62828">达不到全量微调基线</tspan></text>
<text x="300" y="568" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">多数高效方法在质量上落后于 full fine-tuning,迫使人在&quot;效率&quot;与&quot;质量&quot;</text>
<text x="300" y="594" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">之间二选一 —— 这正是 LoRA 想打破的权衡。</text>
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<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA §1–3 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">04 / 15</text>
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<text x="86" y="92" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">权重更新其实"低秩"</text>
<text x="88" y="122" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">THE LOW INTRINSIC-RANK HYPOTHESIS</text>
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<text x="656" y="535" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" fill="#8A94A1">高维权重更新坍缩到低维子空间(示意)</text>
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<text x="64" y="248" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1B3A5C">① 已有证据</text>
<text x="64" y="284" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="18" fill="#1D2733">过参数化模型实际位于<tspan font-weight="800" fill="#1B3A5C">低内在维度</tspan>上</text>
<text x="64" y="310" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" fill="#8A94A1">Li et al. 2018;Aghajanyan et al. 2020</text>
<text x="64" y="372" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1B3A5C">② 本文假设</text>
<text x="64" y="408" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="18" fill="#1D2733">适配时的权重变化 <tspan font-style="italic">ΔW</tspan> 也具有低<tspan font-weight="800" fill="#1B3A5C">"内在秩"</tspan></text>
<text x="64" y="470" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1B3A5C">③ 推论</text>
<text x="64" y="506" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="18" fill="#1D2733">即使 <tspan font-style="italic">d</tspan> 高达 <tspan font-weight="800">12,288</tspan>,极低的秩 <tspan font-style="italic" font-weight="800" fill="#E8743B">r(1~2)</tspan>也够用</text>
</g>
<g id="footer">
<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA §1, §4.1 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">05 / 15</text>
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<?xml version='1.0' encoding='UTF-8'?>
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1280 720" width="1280" height="720">
<defs>
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<rect x="64" y="56" width="6" height="56" rx="3" fill="#E8743B" />
<text x="86" y="90" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">冻结 W₀,注入低秩 BA</text>
<text x="88" y="120" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">THE LoRA REPARAMETRIZATION</text>
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<g id="diagram">
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<image href="../images/reparam_diagram_cropped.png" x="140" y="122" width="1000" height="415" preserveAspectRatio="xMidYMid meet" clip-path="url(#diagClip)" id="_edit_7" />
<rect x="140" y="122" width="1000" height="415" rx="14" fill="none" stroke="#D8DEE6" stroke-width="1.5" />
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<text x="212" y="182" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" fill="#1B3A5C">训练时 W₀ 冻结,只更新 <tspan font-style="italic" font-weight="800">A、B</tspan></text>
<rect x="852" y="162" width="232" height="30" rx="15" fill="#FFFFFF" fill-opacity="0.92" stroke="#D8DEE6" stroke-width="1" />
<text x="868" y="182" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" fill="#1B3A5C"><tspan font-style="italic">A</tspan> 高斯初始化、<tspan font-style="italic">B</tspan>=0 → 起始 ΔW=0</text>
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<g id="formula-band">
<rect x="64" y="540" width="1152" height="132" rx="12" fill="#F4F7FA" />
<rect x="64" y="540" width="6" height="132" rx="3" fill="#E8743B" />
<text x="100" y="582" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="800" fill="#1B3A5C">低秩分解</text>
<image href="../images/formula_002.png" x="220" y="558" width="760" height="33" preserveAspectRatio="xMidYMid meet" />
<text x="100" y="642" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="800" fill="#1B3A5C">前向传播</text>
<image href="../images/formula_001.png" x="220" y="620" width="470" height="30" preserveAspectRatio="xMidYMid meet" />
<text x="1180" y="642" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" fill="#8A94A1">两路输出逐坐标相加</text>
</g>
<g id="footer">
<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA §4.1, Eq.(3), Figure 1 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">06 / 15</text>
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1280 720" width="1280" height="720">
<g id="bg"><rect width="1280" height="720" fill="#FFFFFF"/></g>
<g id="header">
<rect x="64" y="56" width="6" height="56" rx="3" fill="#E8743B"/>
<text x="86" y="90" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">一个常数 α、一次部署合并</text>
<text x="88" y="120" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">INITIALIZATION, SCALING &amp; DEPLOYMENT</text>
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<g id="card-scaling">
<rect x="64" y="160" width="560" height="480" rx="16" fill="#F4F7FA" stroke="#D8DEE6" stroke-width="1"/>
<rect x="64" y="160" width="6" height="480" rx="3" fill="#E8743B"/>
<use data-icon="tabler-outline/math-function" x="100" y="196" width="36" height="36" fill="#1B3A5C" stroke-width="2"/>
<text x="150" y="222" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">缩放:一个常数 α</text>
<rect x="170" y="286" width="348" height="150" rx="12" fill="#FFFFFF" stroke="#D8DEE6" stroke-width="1"/>
<image href="../images/formula_003.png" x="270" y="306" width="148" height="110" preserveAspectRatio="xMidYMid meet"/>
<text x="104" y="494" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">ΔWx 按 <tspan font-style="italic" font-weight="800" fill="#E8743B">α/r</tspan> 缩放</text>
<text x="104" y="528" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">α 设为首个尝试的 r,<tspan font-weight="800" fill="#1B3A5C">不再单独调参</tspan></text>
<text x="104" y="562" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">变更 r 时减少超参重调的需要</text>
<text x="104" y="600" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" fill="#8A94A1">用 Adam 时,调 α 约等于调学习率</text>
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<g id="card-merge">
<rect x="656" y="160" width="560" height="228" rx="16" fill="#FFFFFF" stroke="#D8DEE6" stroke-width="1"/>
<rect x="656" y="160" width="6" height="228" rx="3" fill="#1B3A5C"/>
<use data-icon="tabler-outline/transform" x="692" y="196" width="36" height="36" fill="#1B3A5C" stroke-width="2"/>
<text x="742" y="222" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">部署:合并权重,零额外结构</text>
<text x="696" y="278" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">显式计算并存储 <tspan font-weight="800" fill="#1B3A5C">W = W₀ + BA</tspan></text>
<text x="696" y="312" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">推理时与原模型完全同构</text>
<text x="696" y="346" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">→ <tspan font-weight="800" fill="#2E7D32">不引入任何额外推理延迟</tspan></text>
</g>
<g id="card-switch">
<rect x="656" y="412" width="560" height="228" rx="16" fill="#FFFFFF" stroke="#D8DEE6" stroke-width="1"/>
<rect x="656" y="412" width="6" height="228" rx="3" fill="#3E7CB1"/>
<use data-icon="tabler-outline/git-branch" x="692" y="448" width="36" height="36" fill="#3E7CB1" stroke-width="2"/>
<text x="742" y="474" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="22" font-weight="800" fill="#1D2733">换任务:快速热插拔</text>
<text x="696" y="530" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">恢复 W₀ 只需<tspan font-weight="800" fill="#1B3A5C">减去 BA</tspan></text>
<text x="696" y="564" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">再加上另一任务的 <tspan font-weight="800" fill="#E8743B">B′A′</tspan> 即可</text>
<text x="696" y="598" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">内存开销极小,适合多任务在线切换</text>
</g>
<g id="footer">
<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA §4.1–4.2 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">07 / 15</text>
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<?xml version='1.0' encoding='UTF-8'?>
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1280 720" width="1280" height="720">
<defs>
<clipPath id="attnClip"><rect x="64" y="150" width="430" height="470" rx="14" /></clipPath>
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<g id="header">
<rect x="64" y="56" width="6" height="56" rx="3" fill="#E8743B" />
<text x="86" y="90" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">只适配注意力权重</text>
<text x="88" y="120" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">APPLYING LoRA TO THE TRANSFORMER</text>
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<g id="figure">
<rect x="64" y="150" width="430" height="470" rx="14" fill="#F4F7FA" />
<image href="../images/attention_apply.png" x="64" y="150" width="430" height="470" preserveAspectRatio="xMidYMid meet" clip-path="url(#attnClip)" id="_edit_7" />
<rect x="64" y="150" width="430" height="470" rx="14" fill="none" stroke="#D8DEE6" stroke-width="1.5" />
<text x="64" y="644" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" fill="#8A94A1">自注意力的 Wq、Wv 各挂一条 LoRA 旁路,MLP 冻结</text>
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<g id="point-scope">
<use data-icon="tabler-outline/sitemap" x="540" y="166" width="34" height="34" fill="#1B3A5C" stroke-width="2" />
<text x="586" y="192" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">范围:四个投影,只动两个</text>
<text x="540" y="232" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">自注意力含 <tspan font-weight="800" fill="#1B3A5C">Wq / Wk / Wv / Wo</tspan></text>
<text x="540" y="262" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">实验中只适配 <tspan font-weight="800" fill="#E8743B">Wq、Wv</tspan>,冻结 MLP(为简洁与参数效率)</text>
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<rect x="540" y="300" width="676" height="150" rx="14" fill="#F4F7FA" stroke="#D8DEE6" stroke-width="1" />
<text x="566" y="338" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="800" fill="#1B3A5C">可训练参数量</text>
<image href="../images/formula_004.png" x="566" y="356" width="520" height="52" preserveAspectRatio="xMidYMid meet" />
<text x="566" y="432" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" fill="#8A94A1">L_LoRA = 应用 LoRA 的权重矩阵数;只随 r 线性增长</text>
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<g id="kpi-reduction">
<rect x="540" y="478" width="330" height="142" rx="14" fill="#FFFFFF" stroke="#D8DEE6" stroke-width="1" />
<text x="566" y="514" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="700" fill="#5B6776">训练显存 VRAM(GPT-3 175B)</text>
<text x="566" y="566" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="30" font-weight="800" fill="#1B3A5C">1.2TB <tspan font-size="22" fill="#8A94A1">→</tspan> <tspan fill="#2E7D32">350GB</tspan></text>
<text x="566" y="598" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" fill="#8A94A1">约降至原来的 1/3</text>
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<text x="912" y="514" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="700" fill="#5B6776">Checkpoint(r=4, 仅 Wq/Wv)</text>
<text x="912" y="566" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="30" font-weight="800" fill="#1B3A5C">350GB <tspan font-size="22" fill="#8A94A1">→</tspan> <tspan fill="#E8743B">35MB</tspan></text>
<text x="912" y="598" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" fill="#8A94A1">约 10,000× 缩减</text>
</g>
<g id="footer">
<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA §4.2 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">08 / 15</text>
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1280 720" width="1280" height="720">
<g id="bg"><rect width="1280" height="720" fill="#FFFFFF"/></g>
<g id="header">
<rect x="64" y="56" width="6" height="56" rx="3" fill="#E8743B"/>
<text x="86" y="90" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">LoRA 的四个关键优势</text>
<text x="88" y="120" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">FOUR KEY ADVANTAGES</text>
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<g id="adv-01">
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<text x="600" y="218" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="52" font-weight="800" fill="#1B3A5C" fill-opacity="0.07">01</text>
<rect x="96" y="186" width="56" height="56" rx="14" fill="#EAF0F6"/>
<use data-icon="tabler-outline/share-2" x="110" y="200" width="28" height="28" fill="#1B3A5C" stroke-width="2"/>
<text x="96" y="284" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">可共享</text>
<text x="96" y="306" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" font-weight="700" fill="#1B3A5C" letter-spacing="1.5">ONE BASE · MANY MODULES</text>
<text x="96" y="338" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">一个预训练底座 + 多个小 LoRA 模块;</text>
<text x="96" y="360" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">换任务只需替换 <tspan font-weight="800" fill="#1B3A5C">A、B</tspan>,显著降低存储。</text>
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<rect x="656" y="158" width="560" height="226" rx="16" fill="#FFFFFF" stroke="#D8DEE6" stroke-width="1"/>
<text x="1192" y="218" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="52" font-weight="800" fill="#3E7CB1" fill-opacity="0.08">02</text>
<rect x="688" y="186" width="56" height="56" rx="14" fill="#EAF0F6"/>
<use data-icon="tabler-outline/bolt" x="702" y="200" width="28" height="28" fill="#3E7CB1" stroke-width="2"/>
<text x="688" y="284" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">高效训练</text>
<text x="688" y="306" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" font-weight="700" fill="#3E7CB1" letter-spacing="1.5">LOWER HARDWARE BARRIER</text>
<text x="688" y="338" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">无需为冻结参数存梯度与优化器状态;</text>
<text x="688" y="360" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">硬件门槛最高降低 <tspan font-weight="800" fill="#1B3A5C">3×</tspan>。</text>
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<g id="adv-03">
<rect x="64" y="408" width="560" height="226" rx="16" fill="#FFFFFF" stroke="#D8DEE6" stroke-width="1"/>
<text x="600" y="468" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="52" font-weight="800" fill="#E8743B" fill-opacity="0.10">03</text>
<rect x="96" y="436" width="56" height="56" rx="14" fill="#FBF0E9"/>
<use data-icon="tabler-outline/gauge" x="110" y="450" width="28" height="28" fill="#E8743B" stroke-width="2"/>
<text x="96" y="534" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">零推理延迟</text>
<text x="96" y="556" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" font-weight="700" fill="#E8743B" letter-spacing="1.5">NO ADDED INFERENCE LATENCY</text>
<text x="96" y="588" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">部署时合并 W = W₀+BA;</text>
<text x="96" y="610" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">与全量微调模型<tspan font-weight="800" fill="#2E7D32">同构、不增延迟</tspan>。</text>
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<g id="adv-04">
<rect x="656" y="408" width="560" height="226" rx="16" fill="#FFFFFF" stroke="#D8DEE6" stroke-width="1"/>
<text x="1192" y="468" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="52" font-weight="800" fill="#1B3A5C" fill-opacity="0.07">04</text>
<rect x="688" y="436" width="56" height="56" rx="14" fill="#EAF0F6"/>
<use data-icon="tabler-outline/puzzle" x="702" y="450" width="28" height="28" fill="#1B3A5C" stroke-width="2"/>
<text x="688" y="534" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">正交可叠加</text>
<text x="688" y="556" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" font-weight="700" fill="#1B3A5C" letter-spacing="1.5">ORTHOGONAL · COMPOSABLE</text>
<text x="688" y="588" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">与许多既有方法正交,</text>
<text x="688" y="610" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">可与 <tspan font-weight="800" fill="#1B3A5C">prefix-tuning</tspan> 等组合使用。</text>
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<g id="footer">
<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA §1, §4.1 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">09 / 15</text>
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<text x="86" y="90" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">Adapter 增延迟,LoRA 不增</text>
<text x="88" y="120" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">GPT-2 MEDIUM 单次前向延迟增幅(100 次平均, RTX8000)</text>
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<text x="1126" y="165" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">LoRA / FT</text>
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<text x="145" y="239" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" fill="#8A94A1">+30%</text>
<text x="145" y="348" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" fill="#8A94A1">+20%</text>
<text x="145" y="456" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" fill="#8A94A1">+10%</text>
<text x="145" y="565" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" fill="#8A94A1">0</text>
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<text x="318" y="528" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#3E7CB1">+2.2%</text>
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<text x="404" y="519" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#E8743B">+3.0%</text>
<text x="361" y="590" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">配置 A · 长序列</text>
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<text x="638" y="498" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#3E7CB1">+5.0%</text>
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<text x="724" y="461" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#E8743B">+8.4%</text>
<text x="681" y="590" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">配置 B · 中等</text>
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<text x="958" y="327" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#3E7CB1">+20.7%</text>
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<text x="1044" y="223" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#E8743B">+30.3%</text>
<text x="1001" y="590" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">配置 C · 短序列·在线</text>
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<text x="170" y="615" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="800" fill="#2E7D32" id="_edit_39">LoRA / FT 基线 = 0% 额外延迟(合并权重后)</text>
<text x="1180" y="620" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#1D2733">序列越短、batch 越小,Adapter 的延迟代价越突出</text>
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<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA Table 1 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">10 / 15</text>
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<rect x="64" y="56" width="6" height="56" rx="3" fill="#E8743B"/>
<text x="86" y="90" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">覆盖 NLU 到 NLG 的四类模型</text>
<text x="88" y="120" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">EVALUATION SETUP · ROBERTA / DEBERTA / GPT-2 / GPT-3</text>
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<text x="24" y="35" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#1B3A5C" letter-spacing="1">模型 MODEL</text>
<text x="470" y="35" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#1B3A5C" letter-spacing="1">参数量</text>
<text x="600" y="35" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#1B3A5C" letter-spacing="1">基准 BENCHMARK</text>
<text x="930" y="35" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#1B3A5C" letter-spacing="1">任务</text>
<text x="1128" y="35" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#1B3A5C" letter-spacing="1">LoRA 可训练参数</text>
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<line x1="850" y1="0" x2="850" y2="392" stroke="#D8DEE6" stroke-width="1.5"/>
<line x1="1010" y1="0" x2="1010" y2="392" stroke="#D8DEE6" stroke-width="1.5"/>
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<text x="24" y="98" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">RoBERTa base</text>
<text x="470" y="98" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">125M</text>
<text x="584" y="98" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">GLUE</text>
<text x="930" y="98" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">NLU</text>
<text x="1128" y="98" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="700" fill="#1B3A5C">0.3M</text>
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<text x="24" y="165" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">RoBERTa large</text>
<text x="470" y="165" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">355M</text>
<text x="584" y="165" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">GLUE</text>
<text x="930" y="165" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">NLU</text>
<text x="1128" y="165" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="700" fill="#1B3A5C">0.8M</text>
<line x1="0" y1="257" x2="1152" y2="257" stroke="#D8DEE6" stroke-width="1"/>
<text x="24" y="232" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">DeBERTa XXL</text>
<text x="470" y="232" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">1.5B</text>
<text x="584" y="232" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">GLUE</text>
<text x="930" y="232" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">NLU</text>
<text x="1128" y="232" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="700" fill="#1B3A5C">4.7M</text>
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<text x="24" y="299" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">GPT-2 medium</text>
<text x="470" y="299" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">354M</text>
<text x="584" y="299" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">E2E NLG</text>
<text x="930" y="299" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">NLG</text>
<text x="1128" y="299" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="700" fill="#1B3A5C">0.35M</text>
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<text x="24" y="366" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="800" fill="#1D2733">GPT-3</text>
<text x="470" y="366" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="800" fill="#E8743B">175B</text>
<text x="584" y="366" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">WikiSQL · MNLI · SAMSum</text>
<text x="930" y="366" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">NLU+NLG</text>
<text x="1128" y="366" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" font-weight="800" fill="#E8743B">4.7–37.7M</text>
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<text x="102" y="604" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#1D2733">对比基线:Fine-Tuning(FT)、BitFit、PreEmbed / PreLayer、Adapter H / L / P / D —— 尽可能复用前作报告数值</text>
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<g id="footer">
<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA §5–5.1 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">11 / 15</text>
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<text x="86" y="90" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">更少参数,持平或更优</text>
<text x="88" y="120" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">GLUE BENCHMARK · OVERALL AVG SCORE(越高越好)</text>
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<text x="430" y="26" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#8A94A1" letter-spacing="1">方法</text>
<text x="700" y="26" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#8A94A1" letter-spacing="1"># 可训练参数</text>
<text x="850" y="26" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="800" fill="#8A94A1" letter-spacing="1">GLUE AVG</text>
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<!-- RoBERTa base FT -->
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<text x="430" y="86" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="700" fill="#5B6776">FT</text>
<text x="700" y="86" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">125M</text>
<text x="850" y="86" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">86.4</text>
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<rect x="900" y="74" width="72" height="14" rx="7" fill="#8A94A1"/>
<!-- RoBERTa base LoRA -->
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<text x="20" y="138" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">RoBERTa base</text>
<rect x="380" y="120" width="100" height="26" rx="13" fill="#1B3A5C"/>
<text x="430" y="138" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="700" fill="#FFFFFF">LoRA</text>
<text x="700" y="138" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#E8743B">0.3M</text>
<text x="850" y="138" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="800" fill="#2E7D32">87.2</text>
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<!-- RoBERTa large FT -->
<text x="20" y="200" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">RoBERTa large</text>
<rect x="380" y="182" width="100" height="26" rx="13" fill="#F4F7FA"/>
<text x="430" y="200" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="700" fill="#5B6776">FT</text>
<text x="700" y="200" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">355M</text>
<text x="850" y="200" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">88.9</text>
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<rect x="900" y="188" width="147" height="14" rx="7" fill="#8A94A1"/>
<!-- RoBERTa large LoRA -->
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<text x="20" y="252" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">RoBERTa large</text>
<rect x="380" y="234" width="100" height="26" rx="13" fill="#1B3A5C"/>
<text x="430" y="252" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="700" fill="#FFFFFF">LoRA</text>
<text x="700" y="252" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#E8743B">0.8M</text>
<text x="850" y="252" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="800" fill="#2E7D32">89.0</text>
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<!-- DeBERTa XXL FT -->
<text x="20" y="314" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">DeBERTa XXL</text>
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<text x="430" y="314" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="700" fill="#5B6776">FT</text>
<text x="700" y="314" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">1500M</text>
<text x="850" y="314" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">91.1</text>
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<!-- DeBERTa XXL LoRA -->
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<text x="20" y="366" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#1D2733">DeBERTa XXL</text>
<rect x="380" y="348" width="100" height="26" rx="13" fill="#1B3A5C"/>
<text x="430" y="366" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" font-weight="700" fill="#FFFFFF">LoRA</text>
<text x="700" y="366" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#E8743B">4.7M</text>
<text x="850" y="366" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="800" fill="#2E7D32">91.3</text>
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<text x="128" y="600" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">三类模型上,LoRA 用<tspan font-weight="800" fill="#E8743B"> 1/300 ~ 1/400 </tspan>的可训练参数,GLUE 平均分<tspan font-weight="800" fill="#2E7D32">持平甚至略优于</tspan>全量微调</text>
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<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA Table 2 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">12 / 15</text>
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1280 720" width="1280" height="720">
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<text x="86" y="90" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">在 175B 尺度上仍然成立</text>
<text x="88" y="120" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">GPT-3 175B · 关键指标(对比全量微调)</text>
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<rect x="64" y="158" width="560" height="226" rx="16" fill="#FFFFFF" stroke="#D8DEE6" stroke-width="1"/>
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<rect x="100" y="186" width="52" height="52" rx="13" fill="#FBF0E9"/>
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<text x="170" y="212" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#5B6776">可训练参数缩减</text>
<text x="100" y="312" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="64" font-weight="800" fill="#E8743B">10,000×</text>
<text x="100" y="354" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">checkpoint 350GB → <tspan font-weight="800" fill="#1B3A5C">35MB</tspan></text>
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<g id="kpi-02">
<rect x="656" y="158" width="560" height="226" rx="16" fill="#FFFFFF" stroke="#D8DEE6" stroke-width="1"/>
<rect x="656" y="158" width="6" height="226" rx="3" fill="#1B3A5C"/>
<rect x="692" y="186" width="52" height="52" rx="13" fill="#EAF0F6"/>
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<text x="762" y="212" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#5B6776">训练显存 VRAM(原 1.2TB)</text>
<text x="692" y="312" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="64" font-weight="800" fill="#1B3A5C">350GB</text>
<circle cx="708" cy="350" r="14" fill="#EAF6EE"/>
<polygon points="708,357 714,345 702,345" fill="#2E7D32"/>
<text x="732" y="356" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#2E7D32">约降至 1/3</text>
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<rect x="64" y="408" width="560" height="226" rx="16" fill="#FFFFFF" stroke="#D8DEE6" stroke-width="1"/>
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<text x="170" y="462" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#5B6776">训练吞吐提升</text>
<text x="100" y="562" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="64" font-weight="800" fill="#2E7D32">+25%</text>
<text x="100" y="604" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">无需为绝大多数参数计算梯度</text>
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<text x="762" y="462" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="700" fill="#5B6776">任务准确率:持平 / 更优</text>
<text x="692" y="540" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="40" font-weight="800" fill="#1B3A5C">≥ 全量微调</text>
<text x="692" y="582" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#5B6776">WikiSQL <tspan font-weight="800" fill="#2E7D32">74.0</tspan> · MNLI-m <tspan font-weight="800" fill="#2E7D32">91.7</tspan> · SAMSum <tspan font-weight="800" fill="#2E7D32">53.8</tspan></text>
<text x="692" y="606" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" fill="#8A94A1">三项均达到或超过 FT 基线</text>
</g>
<g id="footer">
<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA §4.2, Table 4, §6 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">13 / 15</text>
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1280 720" width="1280" height="720">
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<text x="86" y="90" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">该适配谁?秩要多大?</text>
<text x="88" y="120" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">UNDERSTANDING THE LOW-RANK UPDATES(GPT-3 175B, WikiSQL)</text>
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<text x="88" y="196" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="18" font-weight="800" fill="#1D2733">① 该适配哪些权重?<tspan font-size="13" font-weight="600" fill="#8A94A1">（同等参数预算 18M）</tspan></text>
<text x="104" y="240" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">仅 Wq</text>
<text x="320" y="240" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="18" font-weight="700" fill="#5B6776">70.4</text>
<text x="104" y="278" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="800" fill="#1B3A5C">Wq + Wv</text>
<rect x="220" y="260" width="120" height="26" rx="13" fill="#FBF0E9"/>
<text x="320" y="278" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="18" font-weight="800" fill="#E8743B">73.7 ★</text>
<text x="104" y="316" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#5B6776">全部四个</text>
<text x="320" y="316" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="18" font-weight="700" fill="#5B6776">73.7</text>
<text x="388" y="278" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#1D2733">适配 <tspan font-weight="800" fill="#1B3A5C">{Wq, Wv}</tspan> 最佳 ——</text>
<text x="388" y="304" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="15" fill="#1D2733">分散到多类权重优于堆在单一类型</text>
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<g id="card-rank">
<rect x="64" y="368" width="624" height="210" rx="14" fill="#F4F7FA" stroke="#D8DEE6" stroke-width="1"/>
<text x="88" y="404" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="18" font-weight="800" fill="#1D2733">② 秩 r 要多大?<tspan font-size="13" font-weight="600" fill="#8A94A1">（Wq+Wv 的验证准确率）</tspan></text>
<g id="rank-chips">
<rect x="92" y="430" width="104" height="64" rx="10" fill="#FFFFFF" stroke="#E8743B" stroke-width="2"/>
<text x="144" y="458" text-anchor="middle" font-family="Consolas, &quot;Courier New&quot;, monospace" font-size="14" fill="#8A94A1">r = 1</text>
<text x="144" y="482" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#E8743B">73.4</text>
<rect x="208" y="430" width="104" height="64" rx="10" fill="#FFFFFF" stroke="#D8DEE6" stroke-width="1"/>
<text x="260" y="458" text-anchor="middle" font-family="Consolas, &quot;Courier New&quot;, monospace" font-size="14" fill="#8A94A1">r = 2</text>
<text x="260" y="482" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="700" fill="#1B3A5C">73.3</text>
<rect x="324" y="430" width="104" height="64" rx="10" fill="#FFFFFF" stroke="#D8DEE6" stroke-width="1"/>
<text x="376" y="458" text-anchor="middle" font-family="Consolas, &quot;Courier New&quot;, monospace" font-size="14" fill="#8A94A1">r = 4</text>
<text x="376" y="482" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="700" fill="#1B3A5C">73.7</text>
<rect x="440" y="430" width="104" height="64" rx="10" fill="#FFFFFF" stroke="#D8DEE6" stroke-width="1"/>
<text x="492" y="458" text-anchor="middle" font-family="Consolas, &quot;Courier New&quot;, monospace" font-size="14" fill="#8A94A1">r = 8</text>
<text x="492" y="482" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="700" fill="#1B3A5C">73.8</text>
<rect x="556" y="430" width="104" height="64" rx="10" fill="#FFFFFF" stroke="#D8DEE6" stroke-width="1"/>
<text x="608" y="458" text-anchor="middle" font-family="Consolas, &quot;Courier New&quot;, monospace" font-size="14" fill="#8A94A1">r = 64</text>
<text x="608" y="482" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="700" fill="#1B3A5C">73.5</text>
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<text x="92" y="538" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" fill="#1D2733"><tspan font-weight="800" fill="#E8743B">r = 1</tspan> 已足够;增大 r 几乎无增益 → ΔW 的&quot;内在秩&quot;极低</text>
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<rect x="760" y="168" width="410" height="410" rx="14" fill="none" stroke="#D8DEE6" stroke-width="1.5"/>
<text x="760" y="606" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#1B3A5C">子空间相似度(Ar=8 vs Ar=64)</text>
<text x="760" y="630" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="13" fill="#5B6776">top 奇异方向高度重叠,其余迅速衰减</text>
</g>
<g id="footer">
<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA §7.1–7.2, Table 5/6, Figure 3 · Hu et al. 2021</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">14 / 15</text>
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1280 720" width="1280" height="720">
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<text x="86" y="92" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="32" font-weight="800" fill="#1B3A5C">结论与影响</text>
<text x="88" y="122" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="14" font-weight="700" fill="#8A94A1" letter-spacing="1">CONCLUSION &amp; IMPACT</text>
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<circle cx="120" cy="210" r="24" fill="#1B3A5C"/>
<text x="120" y="217" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="800" fill="#FFFFFF">01</text>
<rect x="190" y="170" width="1026" height="96" rx="12" fill="#F4F7FA" stroke="#D8DEE6" stroke-width="1"/>
<rect x="190" y="170" width="6" height="96" rx="3" fill="#1B3A5C"/>
<text x="224" y="208" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">方法本身</text>
<text x="224" y="240" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">低秩适配 = <tspan font-weight="800" fill="#1B3A5C">参数高效</tspan> + <tspan font-weight="800" fill="#2E7D32">零额外推理延迟</tspan> + 质量持平 / 更优</text>
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<g id="take-02">
<circle cx="120" cy="360" r="24" fill="#3E7CB1"/>
<text x="120" y="367" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="800" fill="#FFFFFF">02</text>
<rect x="190" y="320" width="1026" height="96" rx="12" fill="#F4F7FA" stroke="#D8DEE6" stroke-width="1"/>
<rect x="190" y="320" width="6" height="96" rx="3" fill="#3E7CB1"/>
<text x="224" y="358" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">工程意义</text>
<text x="224" y="390" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">一个预训练底座<tspan font-weight="800" fill="#1B3A5C">托管多任务</tspan>,按需热插拔 LoRA 模块,部署成本骤降</text>
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<g id="take-03">
<circle cx="120" cy="510" r="24" fill="#E8743B"/>
<text x="120" y="517" text-anchor="middle" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="16" font-weight="800" fill="#FFFFFF">03</text>
<rect x="190" y="470" width="1026" height="96" rx="12" fill="#F4F7FA" stroke="#D8DEE6" stroke-width="1"/>
<rect x="190" y="470" width="6" height="96" rx="3" fill="#E8743B"/>
<text x="224" y="508" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="20" font-weight="800" fill="#1D2733">行业影响</text>
<text x="224" y="540" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">已开源,成为大模型微调的<tspan font-weight="800" fill="#E8743B">事实标准之一</tspan>(PEFT 生态基石)</text>
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<g id="repo">
<use data-icon="tabler-outline/target" x="64" y="602" width="30" height="30" fill="#1B3A5C" stroke-width="2"/>
<text x="106" y="624" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="17" fill="#1D2733">让人不必在<tspan font-weight="800" fill="#1B3A5C">效率</tspan>与<tspan font-weight="800" fill="#1B3A5C">质量</tspan>之间二选一</text>
<text x="1216" y="624" text-anchor="end" font-family="Consolas, &quot;Courier New&quot;, monospace" font-size="16" font-weight="700" fill="#3E7CB1">github.com/microsoft/LoRA</text>
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<text x="64" y="700" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">来源:LoRA §8 Conclusion · Hu et al. 2021 · arXiv:2106.09685</text>
<text x="1216" y="700" text-anchor="end" font-family="&quot;Microsoft YaHei&quot;, Arial, sans-serif" font-size="11" fill="#8A94A1">15 / 15</text>
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