初始化项目，由ModelHub XC社区提供模型

Model: Raghav-Singhal/pathlang-1p7b-runD-zh30-en70
Source: Original Platform

README.md

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+---
+language:
+- en
+- zh
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- pretraining
+- language-ordering
+- curriculum
+- catastrophic-forgetting
+- bilingual
+- smollm2
+license: other
+---
+
+# pathlang-1p7b-runD-zh30-en70
+
+A 1.7B-parameter bilingual (English + Chinese) language model trained with a **purely sequential
+monolingual curriculum**: all of one language, then all of the other. This is the **Chinese-first sequential (zh30->en70)** run, part
+of a controlled language-ordering study. Companion blended-curriculum runs (50/50 diet, 3-phase) live at
+`Raghav-Singhal/pathlang-1p7b-runA-zh-first`, `-runB-en-first`, `-runC-5050`.
+
+## Curriculum (this run)
+
+- Phase 1 (0-30B): 100% Chinese
+- Phase 2 (30-100B): 100% English
+
+The mirror run is `Raghav-Singhal/pathlang-1p7b-runE-en30-zh70`.
+
+## Architecture
+
+- SmolLM2-1.7B backbone: 24 layers, hidden 2048, FFN 8192, 32 heads, RoPE (base 10000), RMSNorm, SwiGLU, seq len 2048
+- **Tokenizer: Qwen3** (multilingual, vocab 151,936); ~2.1B total params
+- Converted from Megatron-LM to HF `LlamaForCausalLM`
+
+## Training
+
+- 100B tokens total, global batch size 960, 50,860 steps
+- WSD LR schedule (peak 2e-4, 2000 warmup, linear decay over the final 10B tokens), bf16, Adam
+- English: DCLM-edu. Chinese: **FineWeb-2 hq-mmbert quality_33** (`cmn_Hani`) — a larger slice than the
+  quality_10 used by the A/B/C runs, needed for the 70B Chinese phase.
+
+## Held-out validation loss (final checkpoint, 100B tokens)
+
+| | English val | Chinese val |
+|---|---|---|
+| this run (Chinese-first sequential (zh30->en70)) | 2.516 | 5.133 |
+
+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.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+m = AutoModelForCausalLM.from_pretrained("Raghav-Singhal/pathlang-1p7b-runD-zh30-en70")
+tok = AutoTokenizer.from_pretrained("Raghav-Singhal/pathlang-1p7b-runD-zh30-en70")
+```
+
+## Note
+
+License `other` pending confirmation; DCLM-edu and FineWeb-2 terms apply. Base (non-instruction-tuned)
+research checkpoint.