---
license: apache-2.0
language:
- en
library_name: pytorch
pipeline_tag: text-generation
tags:
- tiny-lm
- gpt
- nanogpt
- glint-tiny-ml-leaderboard
- english
datasets:
- SlayerLab/minimal-en-corpus-5b
---

# 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](https://huggingface.co/spaces/Glint-Research/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

| checkpoint | params | shape | tokens | BLiMP | ARC-Easy | WikiText-2 BPB |
|---|---|---|---|---|---|---|
| `bpe16m_3.2B/ckpt.pt` | 17.4M | L6 d408 h6 | 3.2B | 67.40 | 38.22 | 1.2161 |
| `bpe16m_6B/ckpt.pt`   | 17.4M | L6 d408 h6 | 6B   | 68.92 | 39.10 | 1.1943 |
| `bpe16m_10B/ckpt.pt`  | 17.4M | L6 d408 h6 | 10B  | 70.36 | 39.52 | 1.1815 |
| `bpe32m_baseline/ckpt.pt` | 31.4M | L6 d576 h9 | 10B | 70.08 | 42.59 | 1.124 |

## 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.

```python
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`](https://huggingface.co/datasets/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.

## 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 and the 32M
baseline around #20/74. These are credible estimates, **not** confirmed entries; an official submission is required to confirm.

## Key findings (single-factor study)

- **Tokens drive BLiMP, not size.** BLiMP keeps climbing with tokens (~+1.8pp per doubling, 3.2B->10B) without plateauing on the board protocol; 16M->32M at matched 10B tokens left BLiMP flat.
- **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: 16M @ expanded ~8.3B-token corpus (BLiMP lift from more/better data).
- 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.