---
language:
  - en
pretty_name: GoLLeM-v5 ARC-MIX 9.4B (EN)
size_categories:
  - 1M<n<10M
source_datasets:
  - SlayerLab/minimal-en-corpus-5b
  - HuggingFaceFW/fineweb-edu
  - mlfoundations/dclm-baseline-1.0-parquet
  - open-web-math/open-web-math
  - HuggingFaceTB/finemath
tags:
  - pretraining
  - nanogpt
  - bpe
  - english
  - arc-enriched
  - gollem-v5
---

# GoLLeM-v5 ARC-MIX 9.4B (EN)

An English pretraining corpus for the **GoLLeM-v5** tiny-LM efficiency track (Glint Tiny-ML Leaderboard).
ARC-MIX is a **reasoning/knowledge-enriched expansion** of
[`SlayerLab/minimal-en-corpus-5b`](https://huggingface.co/datasets/SlayerLab/minimal-en-corpus-5b):
the base multi-source EN mixture with **ARC-relevant data upweighted** (gold ~3×, related ~2×)
to strengthen the ARC-Easy axis — the binding efficiency-constraint at this scale.

## Key facts

| Property | Value |
|---|---|
| Tokens (packaged, uint16) | **9,417,035,832** (~9.42B) |
| Binary size | 18,834,071,664 bytes (`train.bin`, little-endian `uint16`) |
| Tokenizer | BPE, vocab **12,288** (shared with minimal-en-corpus-5b) |
| Base | `SlayerLab/minimal-en-corpus-5b` (5.40B) + ARC-enrichment |
| Decontamination | 13-gram shingles vs WikiText-2 / BLiMP / ARC test sets |
| `train.bin` SHA-256 | `e87f594f7866fae7… (full in manifest)` (full checksum in manifest) |
| Language | English |

## What ARC-MIX adds over the base

`minimal-en-corpus-5b` is a 15-source EN blend (FineWeb-Edu, DCLM, StackExchange, open-web-math,
FineMath, scientific papers, books/Gutenberg, code, CC-News, Wikipedia, …). ARC-MIX re-weights the
mixture toward **science/reasoning/QA-relevant** content:

- **gold ARC-relevant sources ~3×** (science QA, structured reasoning),
- **related sources ~2×** (open-web-math, FineMath, scientific papers, educational web),
- general web retained as the fluency/coverage backbone.

The goal is the ARC-Easy lever (capacity-gated per the GoLLeM-v5 study, finding W11): the same
enrichment is null at 16M but lifts ARC at ≥32M capacity.

## Provenance & preparation

Inherits the base corpus preparation: source-specific extraction, English filtering, exact + MinHash/LSH
near-dedup, benchmark decontamination, whole-document train/val split, deterministic packaging. The
ARC enrichment is applied as a deterministic re-weighted selection over the decontaminated base pool
(no benchmark leakage introduced by the upweighting).

## Usage (nanoGPT binaries)

```python
import numpy as np
train = np.memmap("train.bin", dtype="<u2", mode="r")   # little-endian uint16
vocab_size = 12288
block_size = 1024
```

Configure the GoLLeM-v5 trainer (`train_gpt_ref.py`) with `--vocab 12288 --dtype uint16`.

## Used by

- **GoLLeM-v5 32M (Path-B v1)** — board #16 (eff 75.51), 16B tokens on ARC-MIX.
- **GoLLeM-v5 64M A/B v1** — clean Muon-vs-AdamW study, 13.1B tokens (~1.39 epochs) on this 9.42B corpus.

## Licensing & provenance

Aggregate corpus; **no new unified license** on the underlying documents. Each document retains its
`source_id`; use/redistribution remain subject to the upstream dataset terms (review upstream cards,
especially for commercial use). Base provenance: `SlayerLab/minimal-en-corpus-5b`.

## Related

- Base corpus: [`SlayerLab/minimal-en-corpus-5b`](https://huggingface.co/datasets/SlayerLab/minimal-en-corpus-5b)
- Models: [`SlayerLab/gollem-v5-ckpts`](https://huggingface.co/SlayerLab/gollem-v5-ckpts)
- Board: [Glint Tiny-ML Leaderboard](https://huggingface.co/spaces/Glint-Research/Tiny-ML-Leaderboard)