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ModelHub XC 454380e52d 初始化项目,由ModelHub XC社区提供模型
Model: Raghav-Singhal/pathlang-1p7b-runB-en-first
Source: Original Platform
2026-07-27 19:15:16 +08:00

2.3 KiB

language, library_name, pipeline_tag, tags, license
language library_name pipeline_tag tags license
en
zh
transformers text-generation
pretraining
language-ordering
curriculum
bilingual
smollm2
other

pathlang-1p7b-runB-en-first

A 1.7B-parameter bilingual (English + Chinese) language model, part of a controlled language-ordering study. Three models share the same architecture, initialization, total data diet (50B English + 50B Chinese = 100B tokens), and LR schedule; they differ only in the order in which the two languages are presented during pretraining. This is the English-first run.

Curriculum (this run)

  • Phase 1 (0-30B): 100% English
  • Phase 2 (30-90B): 75% Chinese / 25% English
  • Phase 3 (90-100B): 50/50

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.

Architecture

  • SmolLM2-1.7B backbone: 24 layers, hidden size 2048, FFN 8192, 32 attention heads
  • RoPE (base 10000), RMSNorm, SwiGLU, no biases, sequence length 2048
  • Tokenizer: Qwen3 (multilingual, vocab 151,936)
  • ~2.1B total parameters (with the Qwen3 embedding)

Training

  • 100B tokens: 50B English (DCLM-edu) + 50B Chinese (FineWeb-2 cmn_Hani)
  • Global batch size 960, sequence length 2048, 50,860 steps
  • WSD LR schedule (peak 2e-4, 2000 warmup, linear decay over the final 10B tokens), bf16, Adam
  • Converted from Megatron-LM to HF LlamaForCausalLM format

Held-out validation loss (final checkpoint, 100B tokens)

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).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained("Raghav-Singhal/pathlang-1p7b-runB-en-first")
tok = AutoTokenizer.from_pretrained("Raghav-Singhal/pathlang-1p7b-runB-en-first")

Note

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.