Diagrams — Deep-Dive FTDD-01: MiniCPM Family (OpenBMB)

Deep-Dive: FTDD-01 — MiniCPM Family (OpenBMB) Diagram count: 4 Tool: Mermaid (primary). Each diagram validated in Mermaid Live Editor.

Diagram 1 — The MiniCPM Family (modality axis)

Type: Linear family tree Purpose: The single diagram that maps the four MiniCPM variants to the modality gap each fills. Read it as a progression: text → denser text → +vision → +audio/full-duplex. Reading the diagram: Left = the 1B on-ramp. Each step right adds either capability or modality. SigLip-400M is the load-bearing component that turns a text base into MiniCPM-V.

flowchart LR
  A["MiniCPM5-1B\n~1.08B · text\non-ramp hero"]
  B["MiniCPM3-4B\n4B · text\nmid-size reasoning"]
  C["MiniCPM-V 4.6\n+ SigLip-400M\nvision-language"]
  D["MiniCPM-o 4.5\nomni-modal\nfull-duplex streaming"]

  A -->|"adds: density"| B
  B -->|"adds: vision encoder"| C
  C -->|"adds: audio + duplex"| D

  style A fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#e4e4e8
  style B fill:#14141f,stroke:rgba(94,234,212,0.6),color:#e4e4e8
  style C fill:#14141f,stroke:rgba(94,234,212,0.6),color:#e4e4e8
  style D fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#e4e4e8

Diagram 2 — The Ultra* Datasets → Steering Layer Map

Type: Mapping (dataset → stack layer → course module) Purpose: Show why the Ultra* datasets are the course's data reference. Each dataset maps to a specific steering layer (FT00's stack) and a specific course module — open data lets you point at the signal. Reading the diagram: Three rows, one per dataset. Each row names the dataset, the steering technique it enables, and the course module that uses it as the worked example.

flowchart TB
  subgraph DATA["THE Ultra* OPEN DATASETS"]
    UC["UltraChat\nmulti-turn dialogue"]
    UF["UltraFeedback\npreference / feedback pairs\n(arXiv:2310.01377)"]
    UFW["Ultra-FineWeb\ncurated web pretraining mix"]
  end
  subgraph STEER["STEERING LAYER (FT00 stack)"]
    S1["Layer 3 — SFT\ninstruction format"]
    S2["Layer 3 — DPO\npreference signal"]
    S3["Layer 1 / CPT\nbase distribution"]
  end
  subgraph MOD["COURSE MODULE"]
    M1["FT04, FT12"]
    M2["FT05, FT13"]
    M3["FT06"]
  end

  UC --> S1 --> M1
  UF --> S2 --> M2
  UFW --> S3 --> M3

  style DATA fill:#14141f,stroke:rgba(94,234,212,0.5),color:#e4e4e8
  style STEER fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#e4e4e8
  style MOD fill:#08080c,stroke:rgba(255,255,255,0.12),color:#9494a0
  style UC fill:#08080c,stroke:rgba(94,234,212,0.4),color:#e4e4e8
  style UF fill:#08080c,stroke:rgba(94,234,212,0.4),color:#e4e4e8
  style UFW fill:#08080c,stroke:rgba(94,234,212,0.4),color:#e4e4e8

Diagram 3 — Why MiniCPM Is the Teaching Vehicle (three properties)

Type: Three-pillar Purpose: The three properties that make MiniCPM the course's default base, each mapped to the course need it satisfies. This is the diagram that justifies the choice of base pedagogically. Reading the diagram: Three pillars supporting one claim — "the ideal teaching base." Each pillar is a property (cheap iteration, auditable, Apache-2.0) and each maps to a concrete course requirement.

flowchart TB
  P1["CHEAP ITERATION\n1B fine-tunes in minutes\non a consumer GPU"]
  P2["AUDITABLE PROVENANCE\nopen weights + data + recipe\npoint at every byte"]
  P3["APACHE-2.0\nno MAU / field-of-use\nno license friction"]

  CLAIM["THE IDEAL TEACHING BASE\na model you can break,\nreset, and break again"]

  P1 --> CLAIM
  P2 --> CLAIM
  P3 --> CLAIM

  style P1 fill:#14141f,stroke:rgba(94,234,212,0.6),color:#e4e4e8
  style P2 fill:#14141f,stroke:rgba(94,234,212,0.6),color:#e4e4e8
  style P3 fill:#14141f,stroke:rgba(94,234,212,0.6),color:#e4e4e8
  style CLAIM fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#5eead4

Diagram 4 — SWIFT vs LLaMA-Factory (the fine-tuning decision)

Type: Two-track comparison Purpose: The decision diagram for choosing a MiniCPM fine-tuning framework. The deciding factor is modality — text models are a toss-up; vision/omni models favor SWIFT. Reading the diagram: Top track = SWIFT (first-party). Bottom track = LLaMA-Factory (general). The annotation marks the deciding factor.

flowchart TB
  subgraph SWIFT["SWIFT (ModelBest / OpenBMB — first-party)"]
    direction TB
    S1["Native support for ALL MiniCPM variants"]
    S2["First-party chat templates & modality handlers"]
    S3["Center of gravity = OpenBMB ecosystem"]
    S1 --> S2 --> S3
  end
  subgraph LF["LLaMA-Factory (general-purpose)"]
    direction TB
    L1["Unified interface for dozens of model families"]
    L2["Add MiniCPM as a config change in a multi-model pipeline"]
    L3["Vision/omni support lags SWIFT by weeks-months"]
    L1 --> L2 --> L3
  end

  DECISION{"Base modality?\ntext vs vision/omni"}

  DECISION -->|"MiniCPM-V / MiniCPM-o\n(first-party modality)"| SWIFT
  DECISION -->|"MiniCPM5-1B / 3-4B\n(multi-model pipeline)"| LF

  style SWIFT fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#e4e4e8
  style LF fill:#14141f,stroke:rgba(94,234,212,0.5),color:#e4e4e8
  style DECISION fill:#08080c,stroke:rgba(240,168,104,0.5),stroke-dasharray: 4 2,color:#f0a868
  style S1 fill:#08080c,stroke:rgba(94,234,212,0.3),color:#e4e4e8
  style S2 fill:#08080c,stroke:rgba(94,234,212,0.3),color:#e4e4e8
  style S3 fill:#08080c,stroke:rgba(94,234,212,0.3),color:#e4e4e8
  style L1 fill:#08080c,stroke:rgba(94,234,212,0.3),color:#e4e4e8
  style L2 fill:#08080c,stroke:rgba(94,234,212,0.3),color:#e4e4e8
  style L3 fill:#08080c,stroke:rgba(240,168,104,0.3),color:#f0a868

Validation notes

All four diagrams use the course design system colors: #14141f panel fill, #5eead4 accent for primary, rgba(255,255,255,0.12) for secondary borders, #e4e4e8 / #9494a0 for text. The warn tone (#f0a868) marks the decision node and the lagging-support caveat.
Paste each into Mermaid Live Editor to render. All use stable Mermaid syntax (flowchart LR/TB, subgraph) supported in current Mermaid (v10.4+).
For the slide deck (artifact 03), these are rendered as static SVG/PNG captures from Mermaid Live, inlined into reveal.js.

# Diagrams — Deep-Dive FTDD-01: MiniCPM Family (OpenBMB) **Deep-Dive**: FTDD-01 — MiniCPM Family (OpenBMB) **Diagram count**: 4 **Tool**: Mermaid (primary). Each diagram validated in [Mermaid Live Editor](https://mermaid.live). --- ## Diagram 1 — The MiniCPM Family (modality axis) **Type**: Linear family tree **Purpose**: The single diagram that maps the four MiniCPM variants to the modality gap each fills. Read it as a progression: text → denser text → +vision → +audio/full-duplex. **Reading the diagram**: Left = the 1B on-ramp. Each step right adds either capability or modality. SigLip-400M is the load-bearing component that turns a text base into MiniCPM-V. ```mermaid flowchart LR A["MiniCPM5-1B\n~1.08B · text\non-ramp hero"] B["MiniCPM3-4B\n4B · text\nmid-size reasoning"] C["MiniCPM-V 4.6\n+ SigLip-400M\nvision-language"] D["MiniCPM-o 4.5\nomni-modal\nfull-duplex streaming"] A -->|"adds: density"| B B -->|"adds: vision encoder"| C C -->|"adds: audio + duplex"| D style A fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#e4e4e8 style B fill:#14141f,stroke:rgba(94,234,212,0.6),color:#e4e4e8 style C fill:#14141f,stroke:rgba(94,234,212,0.6),color:#e4e4e8 style D fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#e4e4e8 ``` --- ## Diagram 2 — The Ultra\* Datasets → Steering Layer Map **Type**: Mapping (dataset → stack layer → course module) **Purpose**: Show why the Ultra\* datasets are the course's data reference. Each dataset maps to a specific steering layer (FT00's stack) and a specific course module — open data lets you point at the signal. **Reading the diagram**: Three rows, one per dataset. Each row names the dataset, the steering technique it enables, and the course module that uses it as the worked example. ```mermaid flowchart TB subgraph DATA["THE Ultra* OPEN DATASETS"] UC["UltraChat\nmulti-turn dialogue"] UF["UltraFeedback\npreference / feedback pairs\n(arXiv:2310.01377)"] UFW["Ultra-FineWeb\ncurated web pretraining mix"] end subgraph STEER["STEERING LAYER (FT00 stack)"] S1["Layer 3 — SFT\ninstruction format"] S2["Layer 3 — DPO\npreference signal"] S3["Layer 1 / CPT\nbase distribution"] end subgraph MOD["COURSE MODULE"] M1["FT04, FT12"] M2["FT05, FT13"] M3["FT06"] end UC --> S1 --> M1 UF --> S2 --> M2 UFW --> S3 --> M3 style DATA fill:#14141f,stroke:rgba(94,234,212,0.5),color:#e4e4e8 style STEER fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#e4e4e8 style MOD fill:#08080c,stroke:rgba(255,255,255,0.12),color:#9494a0 style UC fill:#08080c,stroke:rgba(94,234,212,0.4),color:#e4e4e8 style UF fill:#08080c,stroke:rgba(94,234,212,0.4),color:#e4e4e8 style UFW fill:#08080c,stroke:rgba(94,234,212,0.4),color:#e4e4e8 ``` --- ## Diagram 3 — Why MiniCPM Is the Teaching Vehicle (three properties) **Type**: Three-pillar **Purpose**: The three properties that make MiniCPM the course's default base, each mapped to the course need it satisfies. This is the diagram that justifies the choice of base pedagogically. **Reading the diagram**: Three pillars supporting one claim — "the ideal teaching base." Each pillar is a property (cheap iteration, auditable, Apache-2.0) and each maps to a concrete course requirement. ```mermaid flowchart TB P1["CHEAP ITERATION\n1B fine-tunes in minutes\non a consumer GPU"] P2["AUDITABLE PROVENANCE\nopen weights + data + recipe\npoint at every byte"] P3["APACHE-2.0\nno MAU / field-of-use\nno license friction"] CLAIM["THE IDEAL TEACHING BASE\na model you can break,\nreset, and break again"] P1 --> CLAIM P2 --> CLAIM P3 --> CLAIM style P1 fill:#14141f,stroke:rgba(94,234,212,0.6),color:#e4e4e8 style P2 fill:#14141f,stroke:rgba(94,234,212,0.6),color:#e4e4e8 style P3 fill:#14141f,stroke:rgba(94,234,212,0.6),color:#e4e4e8 style CLAIM fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#5eead4 ``` --- ## Diagram 4 — SWIFT vs LLaMA-Factory (the fine-tuning decision) **Type**: Two-track comparison **Purpose**: The decision diagram for choosing a MiniCPM fine-tuning framework. The deciding factor is modality — text models are a toss-up; vision/omni models favor SWIFT. **Reading the diagram**: Top track = SWIFT (first-party). Bottom track = LLaMA-Factory (general). The annotation marks the deciding factor. ```mermaid flowchart TB subgraph SWIFT["SWIFT (ModelBest / OpenBMB — first-party)"] direction TB S1["Native support for ALL MiniCPM variants"] S2["First-party chat templates & modality handlers"] S3["Center of gravity = OpenBMB ecosystem"] S1 --> S2 --> S3 end subgraph LF["LLaMA-Factory (general-purpose)"] direction TB L1["Unified interface for dozens of model families"] L2["Add MiniCPM as a config change in a multi-model pipeline"] L3["Vision/omni support lags SWIFT by weeks-months"] L1 --> L2 --> L3 end DECISION{"Base modality?\ntext vs vision/omni"} DECISION -->|"MiniCPM-V / MiniCPM-o\n(first-party modality)"| SWIFT DECISION -->|"MiniCPM5-1B / 3-4B\n(multi-model pipeline)"| LF style SWIFT fill:#14141f,stroke:#5eead4,stroke-width:1.5px,color:#e4e4e8 style LF fill:#14141f,stroke:rgba(94,234,212,0.5),color:#e4e4e8 style DECISION fill:#08080c,stroke:rgba(240,168,104,0.5),stroke-dasharray: 4 2,color:#f0a868 style S1 fill:#08080c,stroke:rgba(94,234,212,0.3),color:#e4e4e8 style S2 fill:#08080c,stroke:rgba(94,234,212,0.3),color:#e4e4e8 style S3 fill:#08080c,stroke:rgba(94,234,212,0.3),color:#e4e4e8 style L1 fill:#08080c,stroke:rgba(94,234,212,0.3),color:#e4e4e8 style L2 fill:#08080c,stroke:rgba(94,234,212,0.3),color:#e4e4e8 style L3 fill:#08080c,stroke:rgba(240,168,104,0.3),color:#f0a868 ``` --- ## Validation notes - All four diagrams use the course design system colors: `#14141f` panel fill, `#5eead4` accent for primary, `rgba(255,255,255,0.12)` for secondary borders, `#e4e4e8` / `#9494a0` for text. The warn tone (`#f0a868`) marks the decision node and the lagging-support caveat. - Paste each into [Mermaid Live Editor](https://mermaid.live) to render. All use stable Mermaid syntax (`flowchart LR/TB`, `subgraph`) supported in current Mermaid (v10.4+). - For the slide deck (artifact 03), these are rendered as static SVG/PNG captures from Mermaid Live, inlined into reveal.js.