THE CURIOUS SIDE OF AI
TEXT / DATASET GUIDE
ultrachat_200k
Synthetic multi-turn conversations. Keep only the needed messages field and maintain a held-out evaluation set.
Source reviewed 2026-10-24 · AiBrainWorX editorial guide
- Purpose
- Text instruction tuning
- Format
- messages; prompt; prompt_id; use train_sft split
- Workflow
- tiny-text-lora
- License
- mit
01 / COMPATIBILITY
Recipe-compatible after split/field selection
Verify exact data rights, prepare a small local subset and evaluate on held-out data. No remote dataset code is executed.
02 / PREPARE LOCALLY
Keep the first run small
- Review the selected source subset, license and data rights.
- Prepare the format required by the chosen trainer. Audio, image classification and text SFT are different workflows.
- Set aside evaluation examples and inspect the final records before training.
- For the launcher text worker, choose a local JSONL file and supported HF checkpoint, then run Preflight.
Our test status
Metadata/schema reviewed, not downloaded or used in training.
03 / ORIGINAL SOURCE
Review files and terms
This is an original concise guide, not a mirror or relicense of the dataset.
Open original publisher ↗Reviewed source revision: 8049631c405ae6576f93f445c6b8166f76f5505a