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Model Card Template

Reminder: Include a license note such as “This walkthrough references <repo> under <license>,” plus links to the repo, latest tag/commit, the associated kb/model_cards/*.yaml, and the generated PDF export (stored under artifacts/pdf/code_walkthroughs/ or released assets).

Metadata

  • Model name: <Friendly alias>
  • External repo: <URL>
  • Latest tag / commit: <tag-or-sha>
  • License: <e.g., Apache-2.0>
  • Model card YAML: kb/model_cards/<id>.yaml
  • Download PDF: <artifact link>

Purpose & Scope

  • What the model is designed for (e.g., DNA sequence embeddings, rs-fMRI latents, multimodal fusion).
  • Intended tasks / datasets; out-of-scope uses.

Architecture & Inductive Biases

  • Brief bullets on backbone, depth/width, notable blocks (e.g., JEPA, MAE, Swin, RC-equivariant BiMamba).
  • Any modality-specific design (hub tokens, TAPE, gradient positional encodings).

Tokenization & Input Constraints

  • Tokenizer type (character, k-mer, BPE, voxel patches, ROI tensors).
  • Context length / TR windows / voxel grids.
  • Required preprocessing (sorting genes, TR normalization, motion censoring).

Pooling & Subject-Level Embeddings

  • How to pool token embeddings (mean, CLS, hub-token average).
  • RC handling (average forward/RC) or TR alignment notes.
  • Aggregation to subject/session level (e.g., exon → gene → subject).

Training Data & Checkpoints

  • Source datasets, sample counts, preprocessing assumptions.
  • Checkpoint paths / download links; expected placement under external_repos/<repo>/checkpoints.
  1. Preprocess inputs (tokenization, z-score, residualization).
  2. Run encoder with key flags (e.g., gradient checkpointing, mask configs).
  3. Pool + project to 512-D (PCA or tiny MLP) with fold-aware fitting.
  4. Persist embeddings + covariates for downstream analyses.

Integration Hooks

  • 512-D projector config (Linear → GELU → Dropout → Linear → LayerNorm).
  • Late-fusion guidance (LR/GBDT baselines, CCA requirements).
  • LOGO / attribution tips (for gene models).

Strengths & Limitations

  • Where the model excels (e.g., long-context DNA, heterogeneous TRs).
  • Known failure modes (site sensitivity, large VRAM needs, tokenizer quirks).
  • Paper citation(s) with DOI/arXiv.
  • Repo link, docs, issue tracker.
  • Related KB cards (dataset, integration strategy, analysis recipes).