models on tracker

packages/node/meshnet_node/model_catalog.py

@@ -19,6 +19,7 @@ class ModelPreset:
     vram_bf16: float
     description: str
     metadata: dict | None = None
+    aliases: tuple[str, ...] = ()
 
     def vram_for_quant(self, quant: str) -> float:
         """Return VRAM requirement in GB for the given quantization."""
@@ -167,9 +168,29 @@ CURATED_MODELS: list[ModelPreset] = [
         description="Large coding-focused MoE model",
         metadata=_MODEL_METADATA.get("unsloth/Kimi-K2.7-Code"),
     ),
+    ModelPreset(
+        name="Qwen3.6-27B",
+        hf_repo="Qwen/Qwen3.6-27B",
+        num_layers=64,
+        vram_nf4=13.0,
+        vram_int8=26.0,
+        vram_bf16=52.0,
+        description="Qwen 27B hybrid linear-attention model",
+        aliases=("qwen3.6-27b",),
+    ),
 ]
 
 
+def resolve_model_alias(value: str) -> ModelPreset | None:
+    """Resolve a curated name, repository, or alias case-insensitively."""
+    normalized = value.strip().casefold()
+    for model in CURATED_MODELS:
+        candidates = (model.name, model.hf_repo, *model.aliases)
+        if normalized in {candidate.casefold() for candidate in candidates}:
+            return model
+    return None
+
+
 def layers_from_config(cfg) -> int | None:
     """Extract the transformer layer count from a HuggingFace config object.