laion/open-sci-ref-1.7b-mixturevitae-wo_ot3-300B-4096-longsft_16k

MixtureVitae v1 minus OpenThoughts3, 1.7B / 300B tokens, extended to 16k context

16k extension of laion/open-sci-ref-1.7b-mixturevitae-wo_ot3-300B-4096.

Why this model exists

Rebuttal experiment for the NeurIPS 2026 submission Strong Post-Training from Permissive, Reasoning-Dominant, Web-Scale Pretraining, extending the 100B wo_ot3 ablation (laion/open-sci-ref-1.7b-mixturevitae-wo_ot3-100B-4096) to the paper's main 300B scale, to test whether OpenThoughts3 is critical for reasoning-boosting post-training at the scale actually used in the paper (rather than only at the smaller 100B ablation budget).

Data

MixtureVitae v1 (decontaminated), 93 of 96 shards -- drops open_thoughts-0/1/2 (18.69B tokens, 4.53% of MV). Weights proportional to shard size, so every kept shard retains its natural share: this is plain MV minus OT3, not a reweighted mixture. 393.92B tokens available; 300B consumed (0.76 epochs, no repetition).

Pretraining

1.7B dense, open-sci-ref architecture, GPT-NeoX tokenizer (vocab 50304, uint16). 300B tokens, 71,526 iters, seq len 4096, RoPE theta 100k, WSD schedule, lr 4e-3, 20% linear cooldown, global batch 1024, warmup 25000 (the 300B convention -- NOT the 1000 used at the 100B ablation scale). 64 nodes x 4 GH200.

Training config is a byte-identical schedule copy of the config that produced ontocord/1.7b-MixtureVitae-300BT-v1-decontaminated (MV-300BT, the paper's main model), so wo_ot3-300BT vs MV-300BT is a controlled pair differing only in the OT3 removal.

Note: not labelled v0.02 -- the published open-sci-ref v0.02 recipe uses global batch 1008 and exists only as a release-metadata record; this run matches the actual JUPITER rerun of MV-300BT used throughout this study (global batch 1024).

Two fixes applied to the released files

• —`modeling_opensci.py`: OpensciModel.forward accepts **kwargs. Recent transformers passes num_items_in_batch from Trainer for loss averaging, and OpensciForCausalLM.forward forwards **kwargs into both self.model(...) and self.loss_function(...). Without this, post-training raises TypeError: unexpected keyword argument 'num_items_in_batch'.
• —`config.json`: max_position_embeddings cast to int. YaRN writes it as a float (16384.0), and lm-eval uses it as a slice index -> TypeError: slice indices must be integers. Affects the 16k variant.

Caveat

This blend is built from per-shard files tokenized with --append-eod, whereas the MV-300BT reference (merged_0) has NO EOD tokens (verified by probing both token streams). Unavoidable -- merged_0 is a single pre-merged .bin so OT3 cannot be removed from it -- but the pair therefore differs by a document-separator token as well as by the OT3 removal.

Long-context extension (this model)

YaRN scaling factor 4.0, 4096 -> 16384. One epoch of long_sft (SeaLong + LongAlign 30% / SmolTalk 70%), lr 2e-4 cosine, 2 nodes x 4 GH200, 1623 steps. Final train loss 0.7994 (lowest of all wo_ot3/nemotron-hq 16k runs in this study, consistent with 300B vs 100B pretraining scale).