# sft-forge-ensemble (v0.3)

Single pareto-pruned SFT dataset ensembled from multiple high-quality sources.
Generated by **sft-forge** — t.

## Pipeline
ingest -> tag every row -> programmatic refine -> GLOBAL dedup (one minhash
index across all sources) -> per-slice pareto prune (quality vs REAL gpt2
token cost) -> this file.

## Sources (rows ingested)
| source | rows |
|---|---|
| a | 1 |

## Survival after dedup + pareto (kept/ingested)
| source | kept |
|---|---|
| a | 1/1 |

- Kept rows: **1** (0 dominated rows pruned)
- Real token total (gpt2 BPE): **123**
- Slices: 1 capability slices

## Token budget by source (gpt2, kept rows only)
| source | tokens | share |
|---|---|---|


Deep agentic trajectories dominate raw token share by design (they are the
rows small models cannot synthesize); rebalance a training mix by dropping
or down-weighting `src:pyromind_long`-tagged rows via the tag filter.

## Format
`train.jsonl`: {"id", "tags", "messages" (assistant turns loss-mask-ready via
`trainable`), "approx_tokens", "gpt2_tokens", "meta"}. Filter by tags, never
re-split. `gpt2_tokens` is the true BPE count — trust it for packing.