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.