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license: apache-2.0
pretty_name: AdapterCast LoRA Trajectory Corpus
tags:
- lora
- peft
- training-dynamics
- trajectory-forecasting
- learning-dynamics
size_categories:
- n<1K
---
# AdapterCast LoRA Trajectory Corpus
Dense LoRA fine-tuning trajectories (adapter weight snapshots + gauge-invariant
spectral features) for research on fine-tuning dynamics and trajectory forecasting.
Companion dataset to the AdapterCast paper (theory: GL(r) gauge structure of LoRA;
measurements: balancedness decay law, Adam pump, regime map; machine: the
NeuralGraphLoRA forecaster).
## Layout (per run)
- `trajectory.dat.zst` — zstd fp16 memmap, shape (n_states, n_params), flat
`named_parameters()` order (NiNo SGDDataset-compatible); factor layout in
`meta.json` (param_specs). Snapshot cadence: see `config.json` (`snapshot_every`).
- `metrics.npz` — per-snapshot σ spectra (T, modules, r), balancedness ‖BᵀB−AAᵀ‖_F,
power sums, train/eval loss, probe log-probs; `meta_json` echoes pinned HF dataset
revisions + library versions for exact reproduction.
- `config.json` / `meta.json` — full run config; snapshot bookkeeping.
## Families
`c1_*` E1 meta-training corpus (Qwen3-0.6B-Base, r∈{4,8,16}, 12 tasks + 9 canonical
mixtures, 600 steps) · `e2_*` optimizer/long-horizon dynamics arms · `t1/2a/2b/2bx_*`
the E0 measurement program (incl. 1.7B/4B scale rows and gauge-twin experiments).
## Held-out protocol
`manifest.json` echoes the frozen train/held-out splits (tasks, ranks {32,64},
scale) — pre-registered BEFORE meta-training (git tags `e1-freeze`, `e2-freeze`).
Note: `smoke_*` pipeline-validation runs are inventoried in `manifest.json` for
completeness but are not shipped in this dataset.
## Reproduction
Code: the `adaptercast` repository (deterministic pipeline — replicate runs are
bit-identical on one host; dataset revisions pinned in each run's provenance).
License: Apache-2.0. Base model: Qwen3-0.6B/1.7B/4B-Base (Apache-2.0). Source
datasets retain their upstream licenses (see provenance).
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