Flashback Forensics: 18 runs x 6 fault types, 21,600 labelled steps, 128 sketch metrics

README.md

@@ -0,0 +1,95 @@
+---
+license: apache-2.0
+task_categories:
+- tabular-classification
+- time-series-forecasting
+tags:
+- machine-learning
+- distributed-training
+- debugging
+- observability
+- anomaly-detection
+- checkpointing
+- flashback
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: steps
+  data_files:
+  - split: train
+    path: steps/train-*.parquet
+  - split: test
+    path: steps/test-*.parquet
+- config_name: runs
+  data_files:
+  - split: train
+    path: runs/train-*.parquet
+  - split: test
+    path: runs/test-*.parquet
+---
+
+# Flashback Forensics
+
+Labelled telemetry from training runs that were **deliberately broken at a known
+step**.  Every row is a few hundred bytes of per-step summary statistics; the
+label is the step at which the fault was actually injected.
+
+The point of the dataset: to make "how early can you tell a run went wrong?"
+a measurable question instead of an anecdote.
+
+## Contents
+
+| config  | rows | one row is |
+|---|---|---|
+| `steps` | 21,600 | one training step of one run: 128 sketch metrics + labels |
+| `runs`  | 18 | one run: fault kind, ground-truth first-bad step, model config |
+
+Splits are **by run**: no step of a test run appears in train.
+
+## Fields (`steps`)
+
+- `run_id`, `step` -- identity
+- `fault_kind` -- one of `bit_flip`, `data_poison_heavy`, `data_poison_subtle`, `lowprec_overflow`, `lr_spike_loud`, `lr_spike_subtle`
+- **128 metric columns** -- the Flashback sketch: per-group gradient and
+  update norms, maxima, variances, sign-flip rates, non-finite counters,
+  log-magnitude histograms, and gradient-norm quantiles.
+  Names follow `flashback.sketch.SketchSchema`:
+  scalars like `grad_norm`, group metrics like `group:attn_qkv:max_abs`,
+  histogram bins like `hist_update:15`.
+- `is_after_first_bad` -- 1 from the injected step onward
+- `steps_to_first_bad` -- signed distance to ground truth (negative = before)
+
+## Baselines measured while building this dataset
+
+| detector | mean |predicted - true| | never detected |
+|---|---:|---:|
+| loss curve alone | 0.5 | 6 / 18 |
+| Flashback consensus bisect | 0.0 | 0 / 18 |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("NagaYu/flashback-forensics", "steps", split="test")
+runs = load_dataset("NagaYu/flashback-forensics", "runs", split="test")
+```
+
+To reproduce, or to generate more with different faults:
+
+```bash
+pip install flashback
+python scripts/build_forensics.py --scale medium --push-to-hub <you>/flashback-forensics
+```
+
+## What is *not* here
+
+No weights, no gradients, no training data -- only aggregate statistics.
+The full state history lives in a Flashback delta store, which stays local.
+
+## Generation
+
+Model: `tiny` (0.53M parameters), 1200 steps, 6 fault
+scenarios x 3 seeds. Faults: learning-rate spikes (loud and subtle),
+single-bit gradient corruption, fp8-range overflow, and data poisoning at two
+contamination levels. Generated by Flashback v0.1.0.

runs/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c4ca473169e0691ea93c74c766746e6da7fbcbc5b62c45a87b8fa18062501109
+size 7570

runs/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:94401bfead6d15f33e7e66ceb66a85306298162f26e918c24ba68b6d0303ccd0
+size 7724

stats.json

@@ -0,0 +1,9 @@
+{
+  "n_step_rows": 21600,
+  "n_runs": 18,
+  "n_metrics": 128,
+  "flashback_mae": 0.0,
+  "flashback_misses": 0,
+  "loss_mae": 0.5,
+  "loss_misses": 6
+}

steps/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0360fa000ff6a94fed86f541f68dd88fe94a0742a9d4fe3dc59f29d7bbaad373
+size 1848783

steps/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4550d4837ee66b8b0b3252e75f104338c3af3d3d2bf5eeec657ae73a58a8dda5
+size 2978364