Datasets:
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license: mit
task_categories:
- text-generation
tags:
- instruction-tuning
- sft
- synthetic
---
# Day 3 instruction blend
A normalized multi-source instruction dataset built for the Day 3
'instruction tuning at scale' exercise of a post-training curriculum.
Every row is in the OpenAI-messages format with a `source` column for
per-source ablations. Built by `day03_instruct/build_blend.py`;
converters live in `common/format_convert.py`.
- Blend size: 24000 rows (200 held out as `test`)
- Sampling seed: 42
- Length cap: 8000 total content characters per conversation
## Sources
| source | dataset | original format | weight | rows kept |
|---|---|---|---|---|
| alpaca | [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) | alpaca | 1.0 | 8000 |
| slimorca | [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca) | sharegpt | 1.0 | 8000 |
| ultrachat | [HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) | messages | 1.0 | 8000 |
## Filtering
Rows were rejected for structural problems (wrong role order, missing or
non-final assistant turn, empty content, unknown speakers) or for exceeding
the length cap. Per-source rejection counts:
```json
{
"alpaca": {},
"slimorca": {
"length: over 8000 chars": 96
},
"ultrachat": {
"length: over 8000 chars": 1902
}
}
```
## Licensing and provenance
- **alpaca** (yahma/alpaca-cleaned): CC BY 4.0; responses originally distilled from OpenAI models
- **slimorca** (Open-Orca/SlimOrca): MIT; GPT-4-generated responses (note OpenAI-terms provenance caveat)
- **ultrachat** (HuggingFaceH4/ultrachat_200k): MIT
Portions of this data were generated by large language models; check the
upstream dataset cards and the relevant providers' terms before commercial use.
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