SLM-125M-e4 — 4 epochs on the rebuilt 2.5B-token corpus

A 125M-parameter LLaMA-style model for US legal/financial text, and the fourth epoch-milestone in a continued-pretraining series:

slm-125m-base (v1) → extended → e2 (2 epochs on the rebuilt 2.5B corpus) → e4 (this model — 2 further epochs on the same corpus, 4 epochs total).

This run continued Ace-2504/slm-125m-e2 for 2 more epochs (9,440 steps, ~4.95B tokens; ~2.47B per epoch) on the same rebuilt 2.5B-token corpus (stricter OCR gate, exhausted case-law source). Fresh cosine schedule, peak LR 3e-4 → floor 3e-5, one A100-40GB. It exists to answer a research question: once continued pretraining has already recovered, do further epochs on the same in-distribution data keep improving a small model, or start to overfit and forget?

Result: further epochs improved every domain — no forgetting

Validation perplexity fell from e2's 9.86 to 9.44. Measured against e2's exact endpoint on the held-out forgetting probe, all three domains improved rather than regressing — the transient rise while the fresh cosine LR peaked was fully recovered and surpassed:

Domain	loss at e2 start	loss at e4 end	change
SEC filings	1.7470	1.6977	−2.82%
US case law	2.2828	2.2324	−2.21%
FineWeb-Edu	3.1580	3.0987	−1.88%

So on this in-distribution corpus, a second round of continued pretraining behaves as pure improvement, not catastrophic forgetting — the opposite of what the single-epoch out-of-distribution extended run showed on SEC text.

Architecture

12 layers, 768 hidden, 12 heads, 1024 context, RoPE (θ=10000), RMSNorm, SwiGLU, tied embeddings, vocab 16,384 (125.8M params). Same tokenizer and embedding matrix as slm-125m-base, so token ids mean exactly what they did in v1 training.

Not instruction-tuned — this is a base model. Use Ace-2504/slm-125m-base for the original, or Ace-2504/slm-125m-e2 for the 2-epoch checkpoint.