feat: add Ling 3.0 LongHaul support

docs/longhaul.md

@@ -31,7 +31,7 @@ The normal startup warmup is skipped automatically in longhaul mode. Routed expe
 Longhaul currently requires:
 
 - all repeating layers on CPU, or all repeating layers on Metal
-- Qwen3.5 MoE, Laguna, or Inkling architecture
+- Qwen3.5 MoE, Laguna, Inkling, or Ling 3.0 (`bailingmoe3`) architecture
 - text generation without embeddings or LoRA adapters
 
 CPU mode is available wherever the CPU backend is supported. Metal mode requires
@@ -63,6 +63,15 @@ once, and independent expert slices are read concurrently where the platform
 supports positional reads. CPU and shared Metal buffers are populated directly;
 private Metal buffers use a staged fallback.
 
+Ling 3.0 support covers its hybrid KDA/MLA transformer and sigmoid-routed MoE.
+For Ling-3.0-flash, the router, score-correction bias, and shared expert remain
+resident while the 512 routed experts are streamed. Each token selects eight
+routed experts, so cache budgets with fewer than eight slots execute an MoE
+layer in multiple stages. AtomicChat's Ling GGUFs can be loaded directly, and
+the HF converter also recognizes `BailingMoeV3ForCausalLM` checkpoints. The
+converter omits the auxiliary MTP tensor block because Longhaul does not support
+MTP tensors.
+
 Inkling keeps its two shared experts resident and streams only the routed
 256-expert banks. Inkling-Small selects six routed experts per token. In the
 seven-shard Q8_0 model, one cache slot across all 40 MoE layers uses about