Better data. Better model.
dropoutt scan ./data done Scanned 332 records ◧◨ 5 problems would fail a fine-tuning run 332 records · 5 datasets · 15k tokens · tr 97%, unknown 3% What would go wrong would block Role names your trainer will not recognise T0-ROLE-002 60 of 291 records · 21% non-canonical role names: 'human' (60), 'gpt' (60) → Map roles to system/user/assistant before training, or confirm yourtrainer maps them. would block Files are not training data T0-SCHEMA-001 1 of 5 datasets logs: records carry type='queue-operation' → Point the scan at your dataset directory rather than a project or logdirectory. warning Responses that teach the model nothing T0-DEGEN-001 133 of 324 responses · 41% 2 trivial, 131 looping responses → Remove trivial, looping or prompt-copying responses. Written to data/.dropoutt report.html the excerpts and every finding in full findings.jsonl one finding per line, for scripts fingerprint.json comparable measurements, for diffing runs No pass-or-fail verdict: no target declared. Pass --target sft to turnfindings into an exit code. 332 records in 0.4s.
Audit your LLM datasets for blank loss masks, benchmark leaks, PII, and repetition loops. Run it locally in seconds before you spend on GPU compute.
pip install dropouttSee what your data covers.
A model can only learn the topics its data contains. Atlas shows where your dataset goes deep, where it runs thin, and what it leaves out, so you can fill the gaps before you pay for a training run.
dropoutt atlas ./data done Placed 2,090 of 2,100 records ◧◨ Where this corpus sits on the map atlas-v3 2,100 records · 7 datasets · en 85%, tr 14%, fr under 0.1% Effective coverage 1004.4 of 4,096 (1,005 subregions hold any records)(broad) 2,090 placed of 2,100 sampled records · 10 off the map (0.5%) Machine learning, statistics and computational methods 19/19 reach · 11.1%share · 231 records 20× Algorithms, optimiza… 3.4× Mathematics, analysi… 12× Probability, estimat… 3.2× Dynamical systems, c… Mathematics, algorithms and programming problems 16/20 reach · 8.5% share ·177 records 33× Python, NumPy and da… 2.5× Graph theory, graphs… 14× Code and forum posts… 2.1× Mathematical functio… Programming, web code and desktop software how-tos 17/20 reach · 4.5% share· 94 records 14× Programming, scripts… 1.7× Plugins, WordPress t… 3.8× HTML, CSS and front-… 1.5× Software projects, b… 223 further areas reached; their subregions are named in the report files 25 of the map's 256 subject areas never reached Of the 1005 places you reach, 864 hold 50% of your data between them Real presence in 141 places, a toehold in the rest. An occupancy countreads a place holding one record the same as one holding a third of the corpus,which is how a narrow corpus comes to look broad. Written to corpus/.dropoutt atlas.html the map, drawn 2,100 records in 1.4s. Run `dropoutt scan` for the checks.
256 topics. 4,096 subtopics.
Machine learning, statistics and computational methods
| Subtopic | vs the map |
|---|---|
| Algorithms, optimization and computational complexity | 20×, 20× the map's own density |
| Probability, estimation and stochastic processes | 12×, 12× the map's own density |
| Machine learning, reinforcement learning and training algorithms | 10×, 10× the map's own density |
| Optimization, sparse methods and numerical algorithms | 10×, 10× the map's own density |
| Hypothesis testing, regression and applied statistics | 6.9×, 6.9× the map's own density |
| Deep neural networks and their applications | 5.7×, 5.7× the map's own density |
| Graphs, networks and graph neural methods | 5.0×, 5.0× the map's own density |
12 more subtopics
Hotels, lodging and guest reviews
| Subtopic | vs the map |
|---|---|
| TripAdvisor hotel reviews and booking-partner pages | 19×, 19× the map's own density |
| City, airport and chain hotels | 4.0×, 4.0× the map's own density |
| Hotels, rooms and guest-house listings | 3.0×, 3.0× the map's own density |
| Beach resorts, seaside hotels and oceanfront stays | 2.4×, 2.4× the map's own density |
| Hotel booking, deals and cheap-rate search pages | 2.3×, 2.3× the map's own density |
| Hotel listings of rooms, location and facilities | 2.0×, 2.0× the map's own density |
| Hotel rates, reservations and stay conditions | 2.0×, 2.0× the map's own density |
15 more subtopics
Baking, desserts and sweet recipes
| Subtopic | vs the map |
|---|---|
| Cookies and cookie baking | 3.8×, 3.8× the map's own density |
| Ice cream, gelato and frozen desserts | 3.0×, 3.0× the map's own density |
| Measured baking recipes and dessert formulas | 2.7×, 2.7× the map's own density |
| Fruit desserts, pumpkin baking and autumn sweets | 2.0×, 2.0× the map's own density |
| Tea, matcha and tea brewing | 1.8×, 1.8× the map's own density |
| Bread, buns and yeast baking | 1.6×, 1.6× the map's own density |
| Cakes, cupcakes and cake decorating | 1.6×, 1.6× the map's own density |
15 more subtopics
Crafts, printing and handmade goods
| Subtopic | vs the map |
|---|---|
| Printers, 3D printing and print production | 18×, 18× the map's own density |
| Rubber-stamped cards and papercraft stamping | 1.9×, 1.9× the map's own density |
| Greeting cards and stationery | 1.7×, 1.7× the map's own density |
| Paper, felt and small handmade craft projects | 1.6×, 1.6× the map's own density |
| Children's coloring, crafts and art activities | 1.3×, 1.3× the map's own density |
| Drawing, sketching and drawing tools | 1.3×, 1.3× the map's own density |
| Graphic design, logos and design templates | 1.2×, 1.2× the map's own density |
10 more subtopics
Find the records that break training.
Point dropoutt scan at a dataset folder and 34 checks run at once. Each finding lists the records behind it and a suggested fix.
Empty loss masks
Records that add zero trainable tokens. You pay to process them and the model learns nothing.
Duplicates
Exact copies, near copies, and the same prompt answered two different ways.
Benchmark leaks
Training records that overlap evaluation sets and inflate the scores you report.
Truncated records
Records longer than your sequence length, and answers cut off at a generation cap.
Personal data and secrets
Emails, phone numbers, and credentials in training text. Every report masks the match.
Repetition loops
Looping, trivial, or prompt-copying responses that a model learns to repeat.
Works on your laptop and in CI.
One install, no account, no server to run.
Private by default
Your data stays on your machine. Fetch the models once, then scan with the network switched off.
Reports you can share
An HTML report with excerpts and a suggested fix for each finding, plus JSON for your own scripts.
A gate in CI
Declare what you are training and a blocking finding fails the build with exit code 10.
Run your first scan.
pip install dropouttuv tool install dropouttFree and open source under Apache 2.0 · Python 3.10 or newer