Diagrams — Module FT09: DoRA, rsLoRA, and Modern PEFT

Module: FT09 — DoRA, rsLoRA, and Modern PEFT Diagram count: 5 Tool: Mermaid (primary). Each diagram validated in Mermaid Live Editor.

Diagram 1 — DoRA Magnitude/Direction Decomposition vs LoRA

Type: Side-by-side mechanism comparison Purpose: The core FT09 idea on one picture. Vanilla LoRA couples magnitude and direction updates proportionally; DoRA separates them and adapts each independently. Reading the diagram: Two parallel paths from the same frozen base weight. The LoRA path updates magnitude and direction together (locked ratio). The DoRA path splits them and updates magnitude (a tiny vector m) and direction (the LoRA adapter) separately.

flowchart TD
  W0["FROZEN BASE WEIGHT W₀\n(d × d)"]

  W0 --> LoRA["LoRA PATH\nΔW = (α/r) · B·A"]
  W0 --> DoRA["DoRA PATH\ndecompose W₀"]

  LoRA --> Coupled["UPDATE COUPLES\nmagnitude ⊥ direction\n(locked ratio)\ncannot rotate a direction\nwithout rescaling its magnitude"]
  Coupled --> LoRAOut["W = W₀ + ΔW\n~half the gap to full FT\nremains unreachable"]

  DoRA --> Split["split into\nmagnitude m (per column)\n+ direction (W₀ / ‖W₀‖)"]
  Split --> Mag["magnitude m\ntiny trainable vector\n(d scalars)"]
  Split --> Dir["direction\nunit-normalized\n+ LoRA adapter\n(r × d, d × r)"]
  Mag --> Recombine["recombine:\nm ⊙ direction + ΔW_dir"]
  Dir --> Recombine
  Recombine --> DoRAOut["W = m ⊙ (W₀/‖W₀‖) + ΔW_dir\ncloses ~half the gap to full FT\nzero inference overhead after merge"]

  style W0 fill:#14141f,stroke:rgba(255,255,255,0.12),color:#e4e4e8
  style LoRA fill:#14141f,stroke:rgba(240,168,104,0.6),color:#f0a868
  style Coupled fill:#08080c,stroke:rgba(240,128,128,0.5),color:#f08080
  style LoRAOut fill:#14141f,stroke:rgba(240,168,104,0.6),color:#f0a868
  style DoRA fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#e4e4e8
  style Split fill:#08080c,stroke:rgba(94,234,212,0.5),color:#5eead4
  style Mag fill:#08080c,stroke:rgba(94,234,212,0.5),color:#5eead4
  style Dir fill:#08080c,stroke:rgba(94,234,212,0.5),color:#5eead4
  style Recombine fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#e4e4e8
  style DoRAOut fill:#14141f,stroke:rgba(130,224,170,0.6),color:#82e0aa

Diagram 2 — The Modern PEFT Family Tree

Type: Taxonomy / family tree Purpose: Place every modern PEFT method on the standard-vs-niche axis. DoRA, rsLoRA, PiSSA, GaLore are standard; VeRA, AdaLoRA, MiSS are niche or experimental. Reading the diagram: LoRA (FT08) is the trunk. Three branches: the decomposition/scaling branch (DoRA, rsLoRA — the defaults), the initialization branch (PiSSA — alternative init), and the full-FT bridge (GaLore). The niche methods hang off as research leaves.

flowchart TD
  LoRA["LoRA (FT08)\nthe trunk\nfreeze base, train B·A"]

  LoRA --> Defaults["THE 2026 DEFAULTS"]
  LoRA --> Alt["ALTERNATIVE INIT"]
  LoRA --> Bridge["THE FULL-FT BRIDGE"]
  LoRA --> Niche["NICHE / RESEARCH"]

  Defaults --> DoRA["DoRA\nmagnitude/direction decomposition\ncloses ~half gap to full FT"]
  Defaults --> rsLoRA["rsLoRA\nα/√r scaling\nstabilizes high rank r ≥ 64"]

  Alt --> PiSSA["PiSSA\nSVD-based initialization\nfaster convergence on NLU"]

  Bridge --> GaLore["GaLore\ngradient low-rank projection\nfull-param FT at near-LoRA memory"]

  Niche --> VeRA["VeRA\nshared frozen random matrices\n~10× fewer params; storage-bottleneck only"]
  Niche --> AdaLoRA["AdaLoRA\nadaptive rank allocation\nlargely superseded by DoRA"]
  Niche --> MiSS["MiSS\nclaims SOTA\nnot widely reproduced"]

  DoRA -.stacks with.-> rsLoRA

  style LoRA fill:#14141f,stroke:rgba(255,255,255,0.12),color:#e4e4e8
  style Defaults fill:#14141f,stroke:rgba(130,224,170,0.6),color:#82e0aa
  style Alt fill:#14141f,stroke:rgba(94,234,212,0.5),color:#5eead4
  style Bridge fill:#14141f,stroke:rgba(94,234,212,0.5),color:#5eead4
  style Niche fill:#14141f,stroke:rgba(240,168,104,0.5),color:#f0a868
  style DoRA fill:#08080c,stroke:rgba(130,224,170,0.6),color:#82e0aa
  style rsLoRA fill:#08080c,stroke:rgba(130,224,170,0.6),color:#82e0aa
  style PiSSA fill:#08080c,stroke:rgba(94,234,212,0.5),color:#5eead4
  style GaLore fill:#08080c,stroke:rgba(94,234,212,0.5),color:#5eead4
  style VeRA fill:#08080c,stroke:rgba(240,168,104,0.5),color:#f0a868
  style AdaLoRA fill:#08080c,stroke:rgba(240,168,104,0.5),color:#f0a868
  style MiSS fill:#08080c,stroke:rgba(240,168,104,0.5),color:#f0a868

Diagram 3 — Quality/Cost Positioning (DoRA Closes Half the Gap)

Type: Number-line / positioning Purpose: Visualize the central DoRA claim — it closes roughly half the gap between vanilla LoRA and full fine-tuning, at only ~5–10% more VRAM than LoRA. Reading the diagram: A quality axis from LoRA (left) to full FT (right). DoRA sits halfway. QDoRA brings the DoRA quality point down to QLoRA's memory cost. GaLore reaches full-FT quality at intermediate memory.

flowchart LR
  subgraph Axis["QUALITY  (← lower)            (higher →)"]
    direction LR
    L["LoRA\nbaseline quality\nbaseline VRAM"]
    D["DoRA + rsLoRA\ncloses ~HALF the gap\n+5–10% VRAM over LoRA"]
    G["GaLore\nfull-param quality\nnear-LoRA optimizer mem"]
    F["Full FT\nreference quality\n10–20× VRAM"]
    L --- D --- G --- F
  end

  subgraph Cost["MEMORY/COST (lower better)"]
    direction LR
    QL["QDoRA\nDoRA quality\nat QLoRA cost\nleading quality/cost"]
  end

  D -.quantize the base.-> QL

  style Axis fill:#08080c,stroke:rgba(255,255,255,0.08),color:#e4e4e8
  style L fill:#14141f,stroke:rgba(255,255,255,0.12),color:#9494a0
  style D fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#5eead4
  style G fill:#14141f,stroke:rgba(94,234,212,0.5),color:#5eead4
  style F fill:#14141f,stroke:rgba(240,128,128,0.6),color:#f08080
  style Cost fill:#08080c,stroke:rgba(130,224,170,0.4),color:#e4e4e8
  style QL fill:#14141f,stroke:rgba(130,224,170,0.6),color:#82e0aa

Diagram 4 — The Stack: DoRA + rsLoRA Combine

Type: Layered stack Purpose: Show why DoRA and rsLoRA are complementary, not competing. They edit different parts of the update. Stacking them beats either alone. Reading the diagram: The adapter update has three conceptual layers. The base weight (frozen). The direction adapter (LoRA matrices). The scaling factor applied to the adapter. DoRA adds the magnitude/direction split on top of the frozen base; rsLoRA swaps the scaling factor inside the direction adapter. Two independent knobs.

flowchart TD
  subgraph Stack["THE DoRA + rsLoRA STACK"]
    direction TB
    L4["SCALING FACTOR\nvanilla LoRA: α/r   →   rsLoRA: α/√r\n(only matters at r ≥ 64)\nFLAG: use_rslora=True"]
    L3["DIRECTION ADAPTER\nLoRA matrices B, A  (r × d, d × r)\ntrained by the optimizer"]
    L2["MAGNITUDE/DIRECTION SPLIT\nDoRA decomposes W₀ into m ⊙ (W₀/‖W₀‖)\nFLAG: use_dora=True"]
    L1["FROZEN BASE WEIGHT W₀\nnever updated"]
    L1 --> L2 --> L3 --> L4
  end

  L4 --> Out["effective update:\nm ⊙ (W₀/‖W₀‖) + (α/√r)·B·A\nDoRA decomposition + rsLoRA scaling\northogonal improvements — stack them"]

  style Stack fill:#08080c,stroke:rgba(255,255,255,0.08),color:#e4e4e8
  style L1 fill:#14141f,stroke:rgba(255,255,255,0.12),color:#9494a0
  style L2 fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#5eead4
  style L3 fill:#14141f,stroke:rgba(94,234,212,0.5),color:#5eead4
  style L4 fill:#14141f,stroke:rgba(130,224,170,0.6),color:#82e0aa
  style Out fill:#14141f,stroke:rgba(130,224,170,0.6),color:#82e0aa

Diagram 5 — The PEFT Decision: From LoRA to GaLore

Type: Decision flow Purpose: Where each method belongs in the practical decision. The 2026 default is DoRA + rsLoRA; you escalate to QDoRA on memory pressure, or to GaLore only when DoRA is measured-insufficient. Reading the diagram: Top-down. Start at the steering task. Default to DoRA + rsLoRA. If memory-constrained, QDoRA. If DoRA measured-insufficient on held-out eval, escalate to GaLore (full FT). Vanilla LoRA and vanilla full FT are the methods you no longer start with.

flowchart TD
  Start["Steering task on a base model\n(FT08 prereq: you know LoRA & QLoRA)"]

  Start --> Q1{"Held-out eval says\nDoRA-quality is sufficient?\n(measured, not vibes)"}

  Q1 -->|"Yes — the common case"| Default["DoRA + rsLoRA\nuse_dora=True, use_rslora=True\n2026 DEFAULT"]
  Q1 -->|"Yes, but GPU < 12 GB"| QDoRA["QDoRA\nDoRA on 4-bit quantized base\nleading quality/cost"]
  Q1 -->|"No — measured gap to full FT"| Q2{"Need true full-parameter\nupdate? (large domain shift)"}

  Q2 -->|"Yes"| GaLore["GaLore\nfull-param FT at near-LoRA memory\nthe bridge to full FT"]
  Q2 -->|"No — try harder adapter"| PiSSA["PiSSA initialization\n+ DoRA + rsLoRA\nfaster convergence, NLU-strong"]

  Default --> Ship["Ship merged adapter\nzero inference overhead"]
  QDoRA --> Ship
  GaLore --> Ship2["Ship full model\n(no adapter hot-swap)"]
  PiSSA --> Ship

  style Start fill:#14141f,stroke:rgba(255,255,255,0.12),color:#e4e4e8
  style Q1 fill:#08080c,stroke:rgba(94,234,212,0.4),color:#e4e4e8
  style Q2 fill:#08080c,stroke:rgba(94,234,212,0.4),color:#e4e4e8
  style Default fill:#14141f,stroke:rgba(130,224,170,0.6),color:#82e0aa
  style QDoRA fill:#14141f,stroke:rgba(130,224,170,0.6),color:#82e0aa
  style GaLore fill:#14141f,stroke:rgba(94,234,212,0.5),color:#5eead4
  style PiSSA fill:#14141f,stroke:rgba(94,234,212,0.5),color:#5eead4
  style Ship fill:#14141f,stroke:rgba(130,224,170,0.6),color:#82e0aa
  style Ship2 fill:#14141f,stroke:rgba(94,234,212,0.5),color:#5eead4

Validation notes

All five diagrams use the course design system colors: #14141f panel fill, #5eead4 (--accent) for the primary/DoRA path, rgba(94,234,212,0.4) for decision diamonds, #f08080 (--danger) for the structural-deficit / high-cost nodes (full FT, LoRA coupling), #f0a868 (--warn) for niche/research nodes, #82e0aa (--ok) for the success / default path, #e4e4e8 / #9494a0 for text.
Paste each into Mermaid Live Editor to render. All use stable Mermaid syntax (flowchart, subgraph, dashed -.label.-> links, --- undirected links) supported in current Mermaid (v10.4+).
For the slide deck (artifact 03), these are rendered as static captures from Mermaid Live, inlined into reveal.js.

# Diagrams — Module FT09: DoRA, rsLoRA, and Modern PEFT **Module**: FT09 — DoRA, rsLoRA, and Modern PEFT **Diagram count**: 5 **Tool**: Mermaid (primary). Each diagram validated in [Mermaid Live Editor](https://mermaid.live). --- ## Diagram 1 — DoRA Magnitude/Direction Decomposition vs LoRA **Type**: Side-by-side mechanism comparison **Purpose**: The core FT09 idea on one picture. Vanilla LoRA couples magnitude and direction updates proportionally; DoRA separates them and adapts each independently. **Reading the diagram**: Two parallel paths from the same frozen base weight. The LoRA path updates magnitude and direction together (locked ratio). The DoRA path splits them and updates magnitude (a tiny vector `m`) and direction (the LoRA adapter) separately. ```mermaid flowchart TD W0["FROZEN BASE WEIGHT W₀\n(d × d)"] W0 --> LoRA["LoRA PATH\nΔW = (α/r) · B·A"] W0 --> DoRA["DoRA PATH\ndecompose W₀"] LoRA --> Coupled["UPDATE COUPLES\nmagnitude ⊥ direction\n(locked ratio)\ncannot rotate a direction\nwithout rescaling its magnitude"] Coupled --> LoRAOut["W = W₀ + ΔW\n~half the gap to full FT\nremains unreachable"] DoRA --> Split["split into\nmagnitude m (per column)\n+ direction (W₀ / ‖W₀‖)"] Split --> Mag["magnitude m\ntiny trainable vector\n(d scalars)"] Split --> Dir["direction\nunit-normalized\n+ LoRA adapter\n(r × d, d × r)"] Mag --> Recombine["recombine:\nm ⊙ direction + ΔW_dir"] Dir --> Recombine Recombine --> DoRAOut["W = m ⊙ (W₀/‖W₀‖) + ΔW_dir\ncloses ~half the gap to full FT\nzero inference overhead after merge"] style W0 fill:#14141f,stroke:rgba(255,255,255,0.12),color:#e4e4e8 style LoRA fill:#14141f,stroke:rgba(240,168,104,0.6),color:#f0a868 style Coupled fill:#08080c,stroke:rgba(240,128,128,0.5),color:#f08080 style LoRAOut fill:#14141f,stroke:rgba(240,168,104,0.6),color:#f0a868 style DoRA fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#e4e4e8 style Split fill:#08080c,stroke:rgba(94,234,212,0.5),color:#5eead4 style Mag fill:#08080c,stroke:rgba(94,234,212,0.5),color:#5eead4 style Dir fill:#08080c,stroke:rgba(94,234,212,0.5),color:#5eead4 style Recombine fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#e4e4e8 style DoRAOut fill:#14141f,stroke:rgba(130,224,170,0.6),color:#82e0aa ``` --- ## Diagram 2 — The Modern PEFT Family Tree **Type**: Taxonomy / family tree **Purpose**: Place every modern PEFT method on the standard-vs-niche axis. DoRA, rsLoRA, PiSSA, GaLore are standard; VeRA, AdaLoRA, MiSS are niche or experimental. **Reading the diagram**: LoRA (FT08) is the trunk. Three branches: the decomposition/scaling branch (DoRA, rsLoRA — the defaults), the initialization branch (PiSSA — alternative init), and the full-FT bridge (GaLore). The niche methods hang off as research leaves. ```mermaid flowchart TD LoRA["LoRA (FT08)\nthe trunk\nfreeze base, train B·A"] LoRA --> Defaults["THE 2026 DEFAULTS"] LoRA --> Alt["ALTERNATIVE INIT"] LoRA --> Bridge["THE FULL-FT BRIDGE"] LoRA --> Niche["NICHE / RESEARCH"] Defaults --> DoRA["DoRA\nmagnitude/direction decomposition\ncloses ~half gap to full FT"] Defaults --> rsLoRA["rsLoRA\nα/√r scaling\nstabilizes high rank r ≥ 64"] Alt --> PiSSA["PiSSA\nSVD-based initialization\nfaster convergence on NLU"] Bridge --> GaLore["GaLore\ngradient low-rank projection\nfull-param FT at near-LoRA memory"] Niche --> VeRA["VeRA\nshared frozen random matrices\n~10× fewer params; storage-bottleneck only"] Niche --> AdaLoRA["AdaLoRA\nadaptive rank allocation\nlargely superseded by DoRA"] Niche --> MiSS["MiSS\nclaims SOTA\nnot widely reproduced"] DoRA -.stacks with.-> rsLoRA style LoRA fill:#14141f,stroke:rgba(255,255,255,0.12),color:#e4e4e8 style Defaults fill:#14141f,stroke:rgba(130,224,170,0.6),color:#82e0aa style Alt fill:#14141f,stroke:rgba(94,234,212,0.5),color:#5eead4 style Bridge fill:#14141f,stroke:rgba(94,234,212,0.5),color:#5eead4 style Niche fill:#14141f,stroke:rgba(240,168,104,0.5),color:#f0a868 style DoRA fill:#08080c,stroke:rgba(130,224,170,0.6),color:#82e0aa style rsLoRA fill:#08080c,stroke:rgba(130,224,170,0.6),color:#82e0aa style PiSSA fill:#08080c,stroke:rgba(94,234,212,0.5),color:#5eead4 style GaLore fill:#08080c,stroke:rgba(94,234,212,0.5),color:#5eead4 style VeRA fill:#08080c,stroke:rgba(240,168,104,0.5),color:#f0a868 style AdaLoRA fill:#08080c,stroke:rgba(240,168,104,0.5),color:#f0a868 style MiSS fill:#08080c,stroke:rgba(240,168,104,0.5),color:#f0a868 ``` --- ## Diagram 3 — Quality/Cost Positioning (DoRA Closes Half the Gap) **Type**: Number-line / positioning **Purpose**: Visualize the central DoRA claim — it closes roughly half the gap between vanilla LoRA and full fine-tuning, at only ~5–10% more VRAM than LoRA. **Reading the diagram**: A quality axis from LoRA (left) to full FT (right). DoRA sits halfway. QDoRA brings the DoRA quality point down to QLoRA's memory cost. GaLore reaches full-FT quality at intermediate memory. ```mermaid flowchart LR subgraph Axis["QUALITY (← lower) (higher →)"] direction LR L["LoRA\nbaseline quality\nbaseline VRAM"] D["DoRA + rsLoRA\ncloses ~HALF the gap\n+5–10% VRAM over LoRA"] G["GaLore\nfull-param quality\nnear-LoRA optimizer mem"] F["Full FT\nreference quality\n10–20× VRAM"] L --- D --- G --- F end subgraph Cost["MEMORY/COST (lower better)"] direction LR QL["QDoRA\nDoRA quality\nat QLoRA cost\nleading quality/cost"] end D -.quantize the base.-> QL style Axis fill:#08080c,stroke:rgba(255,255,255,0.08),color:#e4e4e8 style L fill:#14141f,stroke:rgba(255,255,255,0.12),color:#9494a0 style D fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#5eead4 style G fill:#14141f,stroke:rgba(94,234,212,0.5),color:#5eead4 style F fill:#14141f,stroke:rgba(240,128,128,0.6),color:#f08080 style Cost fill:#08080c,stroke:rgba(130,224,170,0.4),color:#e4e4e8 style QL fill:#14141f,stroke:rgba(130,224,170,0.6),color:#82e0aa ``` --- ## Diagram 4 — The Stack: DoRA + rsLoRA Combine **Type**: Layered stack **Purpose**: Show *why* DoRA and rsLoRA are complementary, not competing. They edit different parts of the update. Stacking them beats either alone. **Reading the diagram**: The adapter update has three conceptual layers. The base weight (frozen). The direction adapter (LoRA matrices). The scaling factor applied to the adapter. DoRA adds the magnitude/direction split on top of the frozen base; rsLoRA swaps the scaling factor inside the direction adapter. Two independent knobs. ```mermaid flowchart TD subgraph Stack["THE DoRA + rsLoRA STACK"] direction TB L4["SCALING FACTOR\nvanilla LoRA: α/r → rsLoRA: α/√r\n(only matters at r ≥ 64)\nFLAG: use_rslora=True"] L3["DIRECTION ADAPTER\nLoRA matrices B, A (r × d, d × r)\ntrained by the optimizer"] L2["MAGNITUDE/DIRECTION SPLIT\nDoRA decomposes W₀ into m ⊙ (W₀/‖W₀‖)\nFLAG: use_dora=True"] L1["FROZEN BASE WEIGHT W₀\nnever updated"] L1 --> L2 --> L3 --> L4 end L4 --> Out["effective update:\nm ⊙ (W₀/‖W₀‖) + (α/√r)·B·A\nDoRA decomposition + rsLoRA scaling\northogonal improvements — stack them"] style Stack fill:#08080c,stroke:rgba(255,255,255,0.08),color:#e4e4e8 style L1 fill:#14141f,stroke:rgba(255,255,255,0.12),color:#9494a0 style L2 fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#5eead4 style L3 fill:#14141f,stroke:rgba(94,234,212,0.5),color:#5eead4 style L4 fill:#14141f,stroke:rgba(130,224,170,0.6),color:#82e0aa style Out fill:#14141f,stroke:rgba(130,224,170,0.6),color:#82e0aa ``` --- ## Diagram 5 — The PEFT Decision: From LoRA to GaLore **Type**: Decision flow **Purpose**: Where each method belongs in the practical decision. The 2026 default is DoRA + rsLoRA; you escalate to QDoRA on memory pressure, or to GaLore only when DoRA is measured-insufficient. **Reading the diagram**: Top-down. Start at the steering task. Default to DoRA + rsLoRA. If memory-constrained, QDoRA. If DoRA measured-insufficient on held-out eval, escalate to GaLore (full FT). Vanilla LoRA and vanilla full FT are the methods you no longer start with. ```mermaid flowchart TD Start["Steering task on a base model\n(FT08 prereq: you know LoRA & QLoRA)"] Start --> Q1{"Held-out eval says\nDoRA-quality is sufficient?\n(measured, not vibes)"} Q1 -->|"Yes — the common case"| Default["DoRA + rsLoRA\nuse_dora=True, use_rslora=True\n2026 DEFAULT"] Q1 -->|"Yes, but GPU < 12 GB"| QDoRA["QDoRA\nDoRA on 4-bit quantized base\nleading quality/cost"] Q1 -->|"No — measured gap to full FT"| Q2{"Need true full-parameter\nupdate? (large domain shift)"} Q2 -->|"Yes"| GaLore["GaLore\nfull-param FT at near-LoRA memory\nthe bridge to full FT"] Q2 -->|"No — try harder adapter"| PiSSA["PiSSA initialization\n+ DoRA + rsLoRA\nfaster convergence, NLU-strong"] Default --> Ship["Ship merged adapter\nzero inference overhead"] QDoRA --> Ship GaLore --> Ship2["Ship full model\n(no adapter hot-swap)"] PiSSA --> Ship style Start fill:#14141f,stroke:rgba(255,255,255,0.12),color:#e4e4e8 style Q1 fill:#08080c,stroke:rgba(94,234,212,0.4),color:#e4e4e8 style Q2 fill:#08080c,stroke:rgba(94,234,212,0.4),color:#e4e4e8 style Default fill:#14141f,stroke:rgba(130,224,170,0.6),color:#82e0aa style QDoRA fill:#14141f,stroke:rgba(130,224,170,0.6),color:#82e0aa style GaLore fill:#14141f,stroke:rgba(94,234,212,0.5),color:#5eead4 style PiSSA fill:#14141f,stroke:rgba(94,234,212,0.5),color:#5eead4 style Ship fill:#14141f,stroke:rgba(130,224,170,0.6),color:#82e0aa style Ship2 fill:#14141f,stroke:rgba(94,234,212,0.5),color:#5eead4 ``` --- ## Validation notes - All five diagrams use the course design system colors: `#14141f` panel fill, `#5eead4` (`--accent`) for the primary/DoRA path, `rgba(94,234,212,0.4)` for decision diamonds, `#f08080` (`--danger`) for the structural-deficit / high-cost nodes (full FT, LoRA coupling), `#f0a868` (`--warn`) for niche/research nodes, `#82e0aa` (`--ok`) for the success / default path, `#e4e4e8` / `#9494a0` for text. - Paste each into [Mermaid Live Editor](https://mermaid.live) to render. All use stable Mermaid syntax (`flowchart`, `subgraph`, dashed `-.label.->` links, `---` undirected links) supported in current Mermaid (v10.4+). - For the slide deck (artifact 03), these are rendered as static captures from Mermaid Live, inlined into reveal.js.