Sudwork/day03-instruct-blend
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… See the full description on the dataset page: https://huggingface.co/datasets/Sudwork/day03-instruct-blend.
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
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:
{
"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.