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Raghav-Singhal
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Raghav-Singhal
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The other runs in the study: Raghav-Singhal/pathlang-1p7b-runA-zh-first,
Raghav-Singhal/pathlang-1p7b-runB-en-first, Raghav-Singhal/pathlang-1p7b-runC-5050.
cmn_Hani)LlamaForCausalLM format| English val | Chinese val | |
|---|---|---|
| this run (English-first) | 2.657 | 2.429 |
Cross-entropy in nats/token on held-out blocks of the training corpora. The headline finding of the study: each run ends best at the language that dominated its middle (bulk) phase — a recency effect that also holds on an out-of-distribution corpus (HPLT4.0).
python
from transformers import AutoModelForCausalLM, AutoTokenizerm = AutoModelForCausalLM.from_pretrained("Raghav-Singhal/pathlang-1p7b-runB-en-first")tok = AutoTokenizer.from_pretrained("Raghav-Singhal/pathlang-1p7b-runB-en-first")
License is set to other pending confirmation; the training data (DCLM-edu, FineWeb-2) and their
respective terms apply. This is a base (non-instruction-tuned) research checkpoint.