i9small_vedic3x_d60m
d60m (68.9M parameters) on plus_clean subset + Vedic x3, trained on RTX 3060 12GB (Home GPU). Started 3 Oct 2026, ended 3 Oct 2026.
Hypothesis
Repeating Vedic-register text three times closes the Vedic gap without hurting the rest.
What we learned
Works: Vedic 5.0% better at no cost elsewhere (ex-Gita 0.6830, a tie with the baseline).
Scores
Bits per byte, lower is better; change against the parent run, E-CTX base.
ex-Gītā (headline)
0.683
bits per byte
+0.1%ex-Gītā, clean_v1
—
bits per byte
Pooled, all five sets
0.675
bits per byte
+0.1%Validation split
0.6695
bits per byte
+0.1%| Test set | E-VEDIC 3x | E-CTX base (parent) | Change |
|---|---|---|---|
| DCS gold (classical) | 0.6801 | 0.6795 | +0.1% |
| Bhagavad-gītā (memorisation) | 0.4632 | 0.4622 | +0.2% |
| Out of domain | 0.6874 | 0.686 | +0.2% |
| Prose | 0.6566 | 0.6547 | +0.3% |
| Vedic (Ṛgveda) | 0.8787 | 0.9254 | −5.0% |
Curves
Training loss
Cross-entropy per token, by step. The first few percent of the run, far higher, run off the top; hover or the table has every value.
Held-out bits per byte
The validation split, evaluated during training, by step. Lower is better.
Throughput
Tokens per second, by step.
Model
- Preset
- d60m
- Parameters
- 68,924,160
- Outside embeddings
- 56,636,160
- Layers · heads · width
- 8 · 12 · 768
- Tokenizer
- SLP1 unigram 8k
Data
- Slice
- plus_clean subset + Vedic x3
- Words
- —
- Training tokens
- 328,075,506
- Passes
- 0.99 passes
- Tokens seen
- 326,123,520
Compute
- GPU
- RTX 3060 12GB
- Where
- Home GPU
- Steps
- 6,635 / 6,635
- GPU hours
- 1.99
- Cost
- —
- Spot restarts
- —
Lineage
homed60m