LatentMoE: K3 sigmoid routing and Switch aux/z-loss

Route with σ(W_r x), Top-k(s+b), then L1-normalize over the selected set.
Add Switch/GShard aux and router z-loss into train_k3 and train_sft.
Wiki parquet URLs honor HF_ENDPOINT for mirrored downloads.

kda/training/data.py

@@ -19,12 +19,15 @@ import torch
 from .prompts import instruction_prompt
 
 WIKI_SHARD_TOTAL = {"zh": 6, "en": 41}
-WIKI_BASE = (
-    "https://huggingface.co/datasets/wikimedia/wikipedia/resolve/main/20231101.{lang}"
-)
+WIKI_PATH = "datasets/wikimedia/wikipedia/resolve/main/20231101.{lang}"
 IGNORE_INDEX = -100
 
 
+def _hf_endpoint() -> str:
+    """Hub origin. OpenBayes/CN: export HF_ENDPOINT=https://hf-mirror.com"""
+    return os.environ.get("HF_ENDPOINT", "https://huggingface.co").rstrip("/")
+
+
 class Tokenizer(Protocol):
     vocab_size: int
 
@@ -91,7 +94,7 @@ def _wiki_files(lang: str, n_shards: int) -> list[str]:
         raise ValueError(f"unsupported wiki lang {lang!r}; expected zh or en")
     total = WIKI_SHARD_TOTAL[lang]
     n = min(max(n_shards, 1), total)
-    base = WIKI_BASE.format(lang=lang)
+    base = f"{_hf_endpoint()}/{WIKI_PATH.format(lang=lang)}"
     return [f"{base}/train-{i:05d}-of-{total:05d}.parquet" for i in range(n)]