gollem-v5-ckpts

GoLLeM-v5 — Tiny English Language Models (16M-32M)

Research checkpoints of sub-100M-parameter English language models, GPT-style decoders (nanoGPT lineage) trained for the Glint Tiny-ML Leaderboard. This repository is a controlled single-factor scaling study: identical architecture/hyper-parameters/seed, varying only tokens and model width.

Model details

• Architecture: decoder-only Transformer (nanoGPT lineage), learned positional embeddings, tied input/output embeddings.
• Sizes: 16M variant = 6 layers / d_model 408 / 6 heads (17.4M params); 32M variant = 6 layers / d_model 576 / 9 heads (31.4M params).
• Context length: 1024 tokens.
• Tokenizer: BPE, vocab 12288 (tokenizer.json), shared across all checkpoints.
• Training: AdamW, lr 6e-4 -> 6e-5 (cosine), batch 64 x 1024 (65,536 tok/step), seed 1337, bf16 (RTX 5090).

Checkpoints

† crown = expanded 8.29B-token corpus (~1.9 epochs); board-recon #14/74 (up from #18 via ARC).

Usage

These are raw nanoGPT-lineage checkpoints (plain torch state dicts), not transformers AutoModel weights. The model class and a ready board-scoring harness are included in this repo:

• train_gpt_ref.py — GPT definition (rebuild the GPT of the tabled shape, load_state_dict, trim logits to vocab 12288).
• glint_parity_eval.py — the exact Glint board-scoring forward (256-token clip, raw log-prob) for BLiMP / ARC-Easy / WikiText-2.

import torch
from tokenizers import Tokenizer
tok   = Tokenizer.from_file("tokenizer.json")          # BPE-12k, vocab 12288
ckpt  = torch.load("bpe16m_10B/ckpt.pt", map_location="cpu")
state = ckpt.get("model", ckpt)                        # load into the GPT from train_gpt_ref.py

Training data

SlayerLab/minimal-en-corpus-5b — ~5.40B BPE-12k tokens, English, decontaminated against the benchmark test sets. A broad high-quality mix: FineWeb-Edu, DCLM, StackExchange, open-web-math, FineMath, scientific papers, books/Gutenberg, code, CC-News. A decontaminated expansion to ~8.3B tokens (added FineWeb-Edu + OpenStax science) feeds later runs.

Training budget and epochs. 16M trained on 16B tokens seen is intentional, not a chart error. The leaderboard scores efficiency = quality at a fixed tiny size, so you over-train to squeeze max quality from frozen capacity. Chinchilla-optimal (about 20x params = 0.32B for 16M) minimizes compute-optimal loss, which is NOT the leaderboard objective; top models train many tokens-per-param too. 16B seen over 8.29B unique corpus = about 1.9 epochs (each token seen ~1.9x, under the 2x repeat-degradation limit; val-loss healthy, zero memorization). Note: the crown 16B point changed BOTH tokens and corpus (5.4B to 8.29B expanded), so on the token-scan chart it is marked separately (star + dashed) and is not a pure token step. BLiMP saturation holds regardless: crown BLiMP 70.53 is below even the token-only projection 71.6.

Evaluation

All metrics use the Glint benchmark protocol (Glint-1.3/benchmark.py), i.e. the board-comparable definitions:

• BLiMP — 67 configs (train split), each sentence clipped to the first 256 tokens, raw sentence log-prob preference (good > bad), no length normalization.
• ARC-Easy — test split, zero-shot, raw accuracy over LL(question + choice) - LL(question).
• WikiText-2 — byte-normalized bits-per-byte (the board's wiki field is byte-scale; token-perplexity is tokenizer-dependent and not directly comparable across models).

A generic lm-eval-harness run scores BLiMP/ARC roughly 2-3pp higher than this protocol; the numbers here are the board-comparable ones.

Positioning (honest): leaderboard ranks are reconstruction estimates — we reverse-engineered the board scoring formula and validated it (it reproduces a published reference model's public rank exactly), then applied it to our Glint-protocol metrics. Under that reconstruction the 16M@10B checkpoint sits around #18/74, the 32M baseline around #20/74, and the crown (16M @ expanded 8.3B) around #14/74. These are credible estimates, not confirmed entries; an official submission is required to confirm.

Key findings (single-factor study)

• Tokens drive BLiMP, not size — up to a ceiling. BLiMP climbs with tokens (~+1.8pp per doubling, 3.2B->10B) then saturates at 16M's ~70.5 ceiling (crown 16B: 70.53, +0.17 over 10B — flat, below the token-only projection 71.6); 16M->32M at matched 10B tokens also left BLiMP flat. Size does not move it; tokens stop moving it near the cap.
• Capacity + knowledge drive ARC. 16M->32M at matched tokens lifted ARC-Easy +3.07pp.
• Efficiency is size-bonus-weighted, so the smallest model reaching a given raw score ranks highest; ARC is the binding lever toward the top, targeted next via knowledge distillation.

Roadmap

• Crown run (done): 16M @ expanded 8.29B corpus, 16B tokens -> BLiMP 70.53 / ARC 40.91, board-recon #14/74. Finding: BLiMP data-lever exhausted at 16M (~70.5 cap); ARC is the sole lever toward #1.
• ARC boost: knowledge distillation from a strong in-house teacher (decontaminated), plus science-dense data.
• Larger raw-score variant under evaluation.

Limitations

Base (not instruction-tuned) research models at 16-32M parameters, English-only. Expect limited factual knowledge and coherence; not intended for production use.

Provenance

Full dialectical record, evaluation artifacts and eval-protocol details in labvault 21_09_GoLLeM-v5-Skalowanie-Glint/ (see 90-Ewaluacja/EvalHarnessParity.md). Trained on RunPod RTX 5090.