File size: 3,662 Bytes
03d4e3d 66401ff 03d4e3d 66401ff 03d4e3d 66401ff 03d4e3d 66401ff 03d4e3d 66401ff 03d4e3d 66401ff 03d4e3d 66401ff 03d4e3d 66401ff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | ---
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**
|