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Raghav-Singhal
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Raghav-Singhal
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The mirror run is Raghav-Singhal/pathlang-1p7b-runD-zh30-en70.
LlamaForCausalLMcmn_Hani) — a larger slice than the
quality_10 used by the A/B/C runs, needed for the 70B Chinese phase.| English val | Chinese val | |
|---|---|---|
| this run (English-first sequential (en30->zh70)) | 3.997 | 2.531 |
In-loop 128-seq probe, cross-entropy nats/token, on this study's held-out blocks (comparable within the D/E pair). Key finding — catastrophic forgetting under sequential training: the model ends excellent at its second (70B) language and substantially degraded at the first, abandoned one. E.g. this run's first-phase language rose from ~2.7 loss at the 30B switch to the value above by 100B — much sharper forgetting than the blended A/B runs, which retained 25% of the earlier language throughout.
python
from transformers import AutoModelForCausalLM, AutoTokenizerm = AutoModelForCausalLM.from_pretrained("Raghav-Singhal/pathlang-1p7b-runE-en30-zh70")tok = AutoTokenizer.from_pretrained("Raghav-Singhal/pathlang-1p7b-runE-en30-zh70")
License other pending confirmation; DCLM-edu and FineWeb-2 terms apply. Base (non-instruction-tuned)
research checkpoint.