blab-jhu

KYS-1.5B-Quality-First

README

License: apache-2.0

What this setting does

S_QF = Top_Bs(D, r_fastText) — rank the whole candidate pool by the DCLM fastText tie-aware percentile and take the top until the 10B-token source budget is filled, then rewrite.

Instantiates the prevailing principle that high-quality sources benefit most from rewriting. Quality is maximised; topical coverage is not controlled.

Every one of the six settings trains on a 10B-token mixture built the same way: a shared 5B-token anchor of top-ranked DCLM fastText documents, identical across all six settings and never rewritten, plus 5B tokens contributed by this setting's selection strategy. Only that second half differs between settings, so downstream differences isolate source selection alone.

Quick start

The repo root is a copy of seed 42 at the end of epoch 3, so from_pretrained works with no subfolder argument:

python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("blab-jhu/KYS-1.5B-Quality-First")      # = seed42/epoch3
tok   = AutoTokenizer.from_pretrained("blab-jhu/KYS-1.5B-Quality-First")

Any other checkpoint is addressed by subfolder:

python
model = AutoModelForCausalLM.from_pretrained("blab-jhu/KYS-1.5B-Quality-First", subfolder="seed43/epoch1")

Layout

markdown
.                      <- copy of seed42/epoch3 (seed 42, end of epoch 3)
seed42/epoch1  epoch2  epoch3
seed43/epoch1  epoch2  epoch3
seed44/epoch1  epoch2  epoch3

Each directory is a complete HF checkpoint: config.json, generation_config.json, model.safetensors (bf16, 3,008,627,352 B), tokenizer.json, tokenizer_config.json, special_tokens_map.json.

Optimizer state is not included. Nanotron wrote an 18.05 GB AdamW state next to every checkpoint (fp32 master weights + both moments, 974.80 GB across the 54 released checkpoints); it is omitted from the release. These checkpoints are for inference and evaluation, not for resuming training.

Step → epoch mapping

One optimizer step consumes 4 (micro) × 64 (accum) × 4 (DP) × 2048 = 2,097,152 tokens.

Table with columns: Directory, Nanotron step, Tokens consumed
Directory	Nanotron step	Tokens consumed
`epoch1`	4770	10,003,415,040
`epoch2`	9540	20,006,830,080
`epoch3`	14305	29,999,759,360

Checkpoints were written every 477 steps; 4770 = 477 × 10 and 9540 = 477 × 20.

Training configuration

Identical across all 18 runs (6 settings × 3 seeds).

Table

Architecture	Llama 2, 28 layers, hidden 2048, FFN 5632 (SwiGLU), 16 heads (MHA)
Position / norm	RoPE θ = 10⁴, RMSNorm ε = 1e-5, tied embeddings
Vocabulary	32,000 (Llama 2 tokenizer)
Parameters	1,504,299,008 (1.504B; 1.439B non-embedding)
Optimizer	AdamW, β = (0.9, 0.95), ε = 1e-8, weight decay 0.1, grad clip 1.0
LR schedule	peak 5e-4, 500-step linear warmup, WSD with linear decay to 0 over the final 10%
Batching	seqlen 2048, global batch 1024 sequences = 2.10M tokens/step
Precision	bf16 parameters, fp32 gradients and optimizer state

Seeds 42/43/44 control initialization only — there is no dropout and the data order is fixed. All six settings at a given seed start from bit-identical weights, released once as KYS-Configs/nanotron/init/.

How these were produced

A 100M-document pool was reservoir-sampled from DCLM-RefinedWeb and annotated with three quality scorers and a 24-way WebOrganizer topic classifier → KYS-DCLM-Refinedweb-100M-Scored.
This setting's strategy selected source documents from the pool, which were rewritten with Qwen2.5-7B-Instruct via vLLM (greedy decoding) → KYS-1.5B-Pretraining-Corpora.
The corpus was tokenized and trained with Nanotron; configs are in KYS-Configs.
Checkpoints were converted from Nanotron to HF format with the same converter used for the paper's own evaluations. The conversion was verified by re-running LightEval on a converted checkpoint and reproducing the paper's stored numbers exactly (rw_piqa acc_norm 0.710555, acc_norm 0.486158, delta 0.000e+00).

Evaluation

LightEval, 0-shot, acc_norm with continuation-token-length-normalized log-likelihood, on ARC-Easy, HellaSwag, PIQA, SIQA, OpenBookQA, CommonsenseQA and MMLU (57 subsets). Task definitions, launchers and all 276 raw result JSONs are in KYS-Configs/eval/.

The rest of the release

Table with columns: Repo, What it holds
Repo	What it holds
`KYS-1.5B-Quality-Base`	QUALITY-BASE — non-rewritten baseline
`KYS-1.5B-Quality-First`	QUALITY-FIRST
`KYS-1.5B-Diversity-Oriented`	DIVERSITY-ORIENTED

Citation

bibtex
@misc{kys2026,
  title  = {Know Your Sources: Data Selection Matters when Rewriting for Data-Constrained Pretraining},
  author = {TODO},
  year   = {2026},
  note   = {TODO: fill in venue / arXiv id / URL}
}

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Model Details

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blab-jhu

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