初始化项目，由ModelHub XC社区提供模型

Model: Raghav-Singhal/pathlang-1p7b-runC-5050
Source: Original Platform

README.md

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+---
+language:
+- en
+- zh
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- pretraining
+- language-ordering
+- curriculum
+- bilingual
+- smollm2
+license: other
+---
+
+# pathlang-1p7b-runC-5050
+
+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 **balanced 50/50 control** run.
+
+## Curriculum (this run)
+
+- All phases: 50% English / 50% Chinese
+
+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 (balanced 50/50 control) | 2.648 | 2.480 |
+
+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
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+m = AutoModelForCausalLM.from_pretrained("Raghav-Singhal/pathlang-1p7b-runC-5050")
+tok = AutoTokenizer.from_pretrained("Raghav-Singhal/pathlang-1p7b-runC-5050")
+```
+
+## 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.