KYS-1.5B-Rewritten-v2

KYS-1.5B-Rewritten-v2

Checkpoints from the v2 evaluation of the Know-Your-Sources rewritten arm: 1.5B Llama models pretrained from scratch on a 10B-token mixture, repeated for three epochs on the branched schedule, with epoch boundaries at steps 4768 / 9537 / 14305.

Reference checkpoint

rewrite-1p5b/seed42/diversity_oriented/ep3/ is the reference checkpoint of kys-eval: the checkpoint an external operator re-scores to confirm their pipeline reproduces ours before scoring new models.

python -m kys_eval.reference_check \
    --model hf://wytro/KYS-1.5B-Rewritten-v2/rewrite-1p5b/seed42/diversity_oriented/ep3/hf

Expected scores (acc_norm, percent, 0-shot, full splits, NVIDIA H100 80GB HBM3):

model.safetensors sha256: 733262aec4ca52fe94b69df0acf7ef1faa7cd0fc1f42ee79b91004a022db3f45

Not the paper-era checkpoint

These are not the checkpoints behind the numbers printed in the paper's tables. The paper-era models sit at steps 4770 / 9540 / 14305, and at 14147 for Diversity Oriented; they are released separately as blab-jhu/KYS-1.5B-*. The v2 checkpoints here were trained on the branched schedule and re-scored; per-cell scores differ from the paper's by up to about 1 accuracy point. Use this repo for kys-eval comparisons, and the blab-jhu/KYS-1.5B-* repos to reproduce the paper's tables.

Layout

rewrite-1p5b/seed<S>/<setting>/ep<N>/hf/         HuggingFace export (LlamaForCausalLM, bf16)
rewrite-1p5b/seed<S>/<setting>/ep<N>/nanotron/   Nanotron checkpoint directory, as written by training

hf/ holds config.json, generation_config.json, model.safetensors, tokenizer.json and tokenizer_config.json. nanotron/ holds model/, optimizer/, lr_scheduler/, random/, config.yaml, model_config.json and checkpoint_metadata.json.

Settings are quality_base, quality_first, diversity_oriented, disagreement_aware, wrap_inspired, rewire_inspired; seeds are 42, 43, 44; epochs ep1 (step 4768), ep2 (9537) and ep3 (14305).

Architecture

Llama 2 architecture: 28 layers, hidden size 2048, FFN 5632 (SwiGLU), 16 heads, RoPE theta 10^4, RMSNorm, vocabulary 32000 (Llama 2 tokenizer), tied embeddings, 1.504B parameters, bf16.