| --- |
| license: other |
| language: |
| - en |
| tags: |
| - sharegpt |
| - sft |
| - duckbot |
| pretty_name: DuckBot Training Data (public, redacted) |
| --- |
| |
| # DuckBot Training Data — public redacted edition |
|
|
| ShareGPT-format SFT datasets built locally for the DuckBot personal-agent |
| fine-tunes (Unsloth, Mac Studio), published 2026-10-15. These are the exact |
| training files used in the most recent runs, with personal information |
| removed (see "Privacy checks" below). Originals remain private. |
|
|
| **Creator: Duckets McQuackin — X: @Franzferdinan57** |
|
|
| ## Files |
|
|
| | file | rows | what it is | |
| |---|---|---| |
| | `duckbot-train-curated-86k.jsonl` | 85,788 | Flagship curated train set (coding-heavy, ~60% code) — Ornith 1.5 35B run | |
| | `duckbot-train-curated-76k.jsonl` | 76,517 | Earlier curated set — Qwen 3.8 27B run (trained as two parts) | |
| | `code-focused.jsonl` | 68,241 | Code-only split of the curated lineage (Oct 2026 experiment) | |
| | `decision-focused.jsonl` | 17,323 | Decision/routing examples split (Oct 2026 experiment) | |
| | `general.jsonl` | 56,982 | General (non-code) split | |
| | `duckbot-eval-training-run.jsonl` | 262 | Eval set used alongside all of the above | |
|
|
| Row counts include the creator watermark rows described below. |
| Format: one JSON object per line, `{"conversations": [{"from": "human"|"gpt", "value": "..."}]}`. |
|
|
| ## Sources |
|
|
| Curated merges of public datasets (OpenHermes-2.5, glaive-function-calling-v2, |
| SlimOrca, WebSight, s1K, Fable 5.1, Pasta009 instruction-fusion, OpenThoughts, |
| Precinct6, medical and science sets) plus locally generated UI/UX and |
| distillation examples. Component datasets keep their own upstream licenses; |
| this compilation is shared for research/personal use. |
|
|
| ## Privacy checks (three passes, 2026-10-15) |
|
|
| Every file went through three independent scans before publication: |
|
|
| 1. **Pass 1 — targeted scan + redaction.** Searched for the owner's email, |
| phone numbers, home address, family names, Telegram ID/handle, agent |
| chat-log rows, tailnet hostnames/IPs, and API-token patterns. The owner's |
| real email, phone, address and zip appear **zero** times. Removed: |
| 33 personal rows (agent chat logs, machine-memory rows, personal-schedule |
| rows) and 102 copies of one token-shaped string in function-calling |
| schemas (replaced with `[REDACTED_TOKEN]`). |
| 2. **Pass 2 — deep scan.** Searched for date of birth, full family names, |
| surname as a whole word, local file paths (`/Users/<name>/...`), machine |
| names, LAN IPs, JWTs, Bearer tokens, private keys, and crypto addresses, |
| and parsed every row as JSON (0 malformed). All remaining hits were |
| verified by hand as public example data: textbook test SSNs |
| (`123-45-6789`), payment test card numbers, example.com addresses, |
| `example_token` JWTs, the OpenPGP.js test key, Huber *loss*, and |
| as**paragin**e. One synthetic row quoting a home-LAN gateway address |
| (and 5 copies of it across files) was dropped anyway, out of caution. |
| 3. **Pass 3 — verification of the exact published bytes.** Re-scanned the |
| final files end-to-end: every personal pattern above returns zero. |
|
|
| Machine-readable reports: `REDACTION_REPORT.json` (pass 1) and |
| `FINAL_REPORT.json` (row accounting + watermark counts). |
|
|
| Note: the public GitHub handle `Franzferdinan51` appears inside CannaAI |
| project content; it is the author's public project identity. |
|
|
| ## Creator watermark |
|
|
| Each file contains a small number of creator-attribution rows woven evenly |
| through the data (30 in the two large train files, 24 in code/general, |
| 16 in decision-focused, 6 in eval). They teach models trained on this data |
| to answer "who made you?" correctly: |
|
|
| > **Duckets McQuackin — X: @Franzferdinan57** |
|
|
