Load recommended model metadata from JSON

packages/node/meshnet_node/model_catalog.py

@@ -2,7 +2,9 @@
 
 from __future__ import annotations
 
+import json
 from dataclasses import dataclass
+from importlib.resources import files
 from pathlib import Path
 
 
@@ -43,6 +45,25 @@ class ModelPreset:
         return None
 
 
+def _load_model_metadata() -> dict[str, dict]:
+    try:
+        raw = files("meshnet_node").joinpath("model_metadata.json").read_text()
+        data = json.loads(raw)
+    except Exception:
+        return {}
+    models = data.get("models", {})
+    if not isinstance(models, dict):
+        return {}
+    return {
+        str(repo): metadata
+        for repo, metadata in models.items()
+        if isinstance(metadata, dict)
+    }
+
+
+_MODEL_METADATA = _load_model_metadata()
+
+
 CURATED_MODELS: list[ModelPreset] = [
     ModelPreset(
         name="Qwen2.5-0.5B-Instruct",
@@ -132,29 +153,8 @@ CURATED_MODELS: list[ModelPreset] = [
         vram_nf4=500.0,
         vram_int8=1000.0,
         vram_bf16=2000.0,
-        description="Moonshot/Unsloth coding-focused MoE model; 1T total, 32B activated",
-        metadata={
-            "architecture": "Mixture-of-Experts (MoE)",
-            "total_parameters": "1T",
-            "activated_parameters": "32B",
-            "num_layers": 61,
-            "dense_layers": 1,
-            "attention_hidden_dimension": 7168,
-            "moe_hidden_dimension_per_expert": 2048,
-            "attention_heads": 64,
-            "experts": 384,
-            "selected_experts_per_token": 8,
-            "shared_experts": 1,
-            "vocabulary_size": 160000,
-            "context_length": 256000,
-            "attention_mechanism": "MLA",
-            "activation_function": "SwiGLU",
-            "vision_encoder": "MoonViT",
-            "vision_encoder_parameters": "400M",
-            "license": "modified-mit",
-            "native_quantization": "int4",
-            "recommended_engines": ["vLLM", "SGLang", "KTransformers"],
-        },
+        description="Large coding-focused MoE model",
+        metadata=_MODEL_METADATA.get("unsloth/Kimi-K2.7-Code"),
     ),
 ]
 

packages/node/meshnet_node/model_metadata.json

@@ -0,0 +1,32 @@
+{
+  "models": {
+    "unsloth/Kimi-K2.7-Code": {
+      "architecture": "Mixture-of-Experts (MoE)",
+      "total_parameters": "1T",
+      "activated_parameters": "32B",
+      "num_layers": 61,
+      "dense_layers": 1,
+      "attention_hidden_dimension": 7168,
+      "moe_hidden_dimension_per_expert": 2048,
+      "attention_heads": 64,
+      "experts": 384,
+      "selected_experts_per_token": 8,
+      "shared_experts": 1,
+      "vocabulary_size": 160000,
+      "context_length": 256000,
+      "attention_mechanism": "MLA",
+      "activation_function": "SwiGLU",
+      "vision_encoder": "MoonViT",
+      "vision_encoder_parameters": "400M",
+      "license": "modified-mit",
+      "native_quantization": "int4",
+      "download_size_gb": 595,
+      "recommended_short_name": "kimi-k2.7",
+      "recommended_engines": [
+        "vLLM",
+        "SGLang",
+        "KTransformers"
+      ]
+    }
+  }
+}

packages/node/pyproject.toml

@@ -27,3 +27,6 @@ meshnet-node = "meshnet_node.cli:main"
 [tool.setuptools.packages.find]
 where = ["."]
 include = ["meshnet_node*"]
+
+[tool.setuptools.package-data]
+meshnet_node = ["*.json"]