wrrg

merge fix

dashboard/src/routes/+page.svelte

@@ -434,8 +434,8 @@ function toggleInstanceDownloadDetails(nodeId: string): void {
 		const shardData = shardObj[shardKeys[0]] as Record<string, unknown>;
 		if (!shardData) return null;
 		
-		// Model meta is nested: shard.model_meta.model_id
-		const modelMeta = shardData.model_meta ?? shardData.modelMeta;
+		// Model meta is nested: shard.model_card.model_id
+		const modelMeta = shardData.model_card ?? shardData.modelCard;
 		if (!modelMeta || typeof modelMeta !== 'object') return null;
 		
 		const meta = modelMeta as Record<string, unknown>;

dashboard/src/routes/downloads/+page.svelte

@@ -98,7 +98,7 @@
 		const shardData = shardObj[shardKeys[0]] as Record<string, unknown>;
 		if (!shardData) return null;
 
-		const modelMeta = shardData.model_meta ?? shardData.modelMeta;
+		const modelMeta = shardData.model_card ?? shardData.modelCard;
 		if (!modelMeta || typeof modelMeta !== 'object') return null;
 
 		const meta = modelMeta as Record<string, unknown>;
@@ -190,7 +190,7 @@
 						const shardKeys = Object.keys(shardObj);
 						if (shardKeys.length !== 1) return null;
 						const shardData = shardObj[shardKeys[0]] as Record<string, unknown>;
-						const modelMeta = shardData?.model_meta ?? shardData?.modelMeta;
+						const modelMeta = shardData?.model_card ?? shardData?.modelCard;
 						if (!modelMeta || typeof modelMeta !== 'object') return null;
 						const meta = modelMeta as Record<string, unknown>;
 						return (meta.prettyName as string) ?? null;

packaging/pyinstaller/exo.spec

@@ -10,6 +10,7 @@ PROJECT_ROOT = Path.cwd()
 SOURCE_ROOT = PROJECT_ROOT / "src"
 ENTRYPOINT = SOURCE_ROOT / "exo" / "__main__.py"
 DASHBOARD_DIR = PROJECT_ROOT / "dashboard" / "build"
+RESOURCES_DIR = PROJECT_ROOT / "resources"
 EXO_SHARED_MODELS_DIR = SOURCE_ROOT / "exo" / "shared" / "models"
 
 if not ENTRYPOINT.is_file():
@@ -18,6 +19,9 @@ if not ENTRYPOINT.is_file():
 if not DASHBOARD_DIR.is_dir():
     raise SystemExit(f"Dashboard assets are missing: {DASHBOARD_DIR}")
 
+if not RESOURCES_DIR.is_dir():
+    raise SystemExit(f"Resources are missing: {RESOURCES_DIR}")
+
 if not EXO_SHARED_MODELS_DIR.is_dir():
     raise SystemExit(f"Shared model assets are missing: {EXO_SHARED_MODELS_DIR}")
 
@@ -58,6 +62,7 @@ HIDDEN_IMPORTS = sorted(
 
 DATAS: list[tuple[str, str]] = [
     (str(DASHBOARD_DIR), "dashboard"),
+    (str(RESOURCES_DIR), "resources"),
     (str(MLX_LIB_DIR), "mlx/lib"),
     (str(EXO_SHARED_MODELS_DIR), "exo/shared/models"),
 ]

resources/mlx-community--DeepSeek-V3.1-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/DeepSeek-V3.1-4bit"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+
+[storage_size]
+in_bytes = 405874409472

resources/mlx-community--DeepSeek-V3.1-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/DeepSeek-V3.1-8bit"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+
+[storage_size]
+in_bytes = 765577920512

resources/mlx-community--GLM-4.5-Air-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/GLM-4.5-Air-8bit"
+n_layers = 46
+hidden_size = 4096
+supports_tensor = false
+
+[storage_size]
+in_bytes = 122406567936

resources/mlx-community--GLM-4.5-Air-bf16.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/GLM-4.5-Air-bf16"
+n_layers = 46
+hidden_size = 4096
+supports_tensor = true
+
+[storage_size]
+in_bytes = 229780750336

resources/mlx-community--GLM-4.7-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/GLM-4.7-4bit"
+n_layers = 91
+hidden_size = 5120
+supports_tensor = true
+
+[storage_size]
+in_bytes = 198556925568

resources/mlx-community--GLM-4.7-6bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/GLM-4.7-6bit"
+n_layers = 91
+hidden_size = 5120
+supports_tensor = true
+
+[storage_size]
+in_bytes = 286737579648

resources/mlx-community--GLM-4.7-8bit-gs32.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/GLM-4.7-8bit-gs32"
+n_layers = 91
+hidden_size = 5120
+supports_tensor = true
+
+[storage_size]
+in_bytes = 396963397248

resources/mlx-community--Kimi-K2-Instruct-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Kimi-K2-Instruct-4bit"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+
+[storage_size]
+in_bytes = 620622774272

resources/mlx-community--Kimi-K2-Thinking.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Kimi-K2-Thinking"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+
+[storage_size]
+in_bytes = 706522120192

resources/mlx-community--Llama-3.2-1B-Instruct-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Llama-3.2-1B-Instruct-4bit"
+n_layers = 16
+hidden_size = 2048
+supports_tensor = true
+
+[storage_size]
+in_bytes = 729808896

resources/mlx-community--Llama-3.2-3B-Instruct-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Llama-3.2-3B-Instruct-4bit"
+n_layers = 28
+hidden_size = 3072
+supports_tensor = true
+
+[storage_size]
+in_bytes = 1863319552

resources/mlx-community--Llama-3.2-3B-Instruct-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Llama-3.2-3B-Instruct-8bit"
+n_layers = 28
+hidden_size = 3072
+supports_tensor = true
+
+[storage_size]
+in_bytes = 3501195264

resources/mlx-community--Llama-3.3-70B-Instruct-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Llama-3.3-70B-Instruct-4bit"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+
+[storage_size]
+in_bytes = 40652242944

resources/mlx-community--Llama-3.3-70B-Instruct-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Llama-3.3-70B-Instruct-8bit"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+
+[storage_size]
+in_bytes = 76799803392

resources/mlx-community--Meta-Llama-3.1-70B-Instruct-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Meta-Llama-3.1-70B-Instruct-4bit"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+
+[storage_size]
+in_bytes = 40652242944

resources/mlx-community--Meta-Llama-3.1-8B-Instruct-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"
+n_layers = 32
+hidden_size = 4096
+supports_tensor = true
+
+[storage_size]
+in_bytes = 4637851648

resources/mlx-community--Meta-Llama-3.1-8B-Instruct-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-8bit"
+n_layers = 32
+hidden_size = 4096
+supports_tensor = true
+
+[storage_size]
+in_bytes = 8954839040

resources/mlx-community--Meta-Llama-3.1-8B-Instruct-bf16.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"
+n_layers = 32
+hidden_size = 4096
+supports_tensor = true
+
+[storage_size]
+in_bytes = 16882073600

resources/mlx-community--MiniMax-M2.1-3bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/MiniMax-M2.1-3bit"
+n_layers = 61
+hidden_size = 3072
+supports_tensor = true
+
+[storage_size]
+in_bytes = 100086644736

resources/mlx-community--MiniMax-M2.1-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/MiniMax-M2.1-8bit"
+n_layers = 61
+hidden_size = 3072
+supports_tensor = true
+
+[storage_size]
+in_bytes = 242986745856

resources/mlx-community--Qwen3-0.6B-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-0.6B-4bit"
+n_layers = 28
+hidden_size = 1024
+supports_tensor = false
+
+[storage_size]
+in_bytes = 342884352

resources/mlx-community--Qwen3-0.6B-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-0.6B-8bit"
+n_layers = 28
+hidden_size = 1024
+supports_tensor = false
+
+[storage_size]
+in_bytes = 698351616

resources/mlx-community--Qwen3-235B-A22B-Instruct-2507-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit"
+n_layers = 94
+hidden_size = 4096
+supports_tensor = true
+
+[storage_size]
+in_bytes = 141733920768

resources/mlx-community--Qwen3-235B-A22B-Instruct-2507-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit"
+n_layers = 94
+hidden_size = 4096
+supports_tensor = true
+
+[storage_size]
+in_bytes = 268435456000

resources/mlx-community--Qwen3-30B-A3B-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-30B-A3B-4bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[storage_size]
+in_bytes = 17612931072

resources/mlx-community--Qwen3-30B-A3B-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-30B-A3B-8bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[storage_size]
+in_bytes = 33279705088

resources/mlx-community--Qwen3-Coder-480B-A35B-Instruct-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"
+n_layers = 62
+hidden_size = 6144
+supports_tensor = true
+
+[storage_size]
+in_bytes = 289910292480

resources/mlx-community--Qwen3-Coder-480B-A35B-Instruct-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-Coder-480B-A35B-Instruct-8bit"
+n_layers = 62
+hidden_size = 6144
+supports_tensor = true
+
+[storage_size]
+in_bytes = 579820584960

resources/mlx-community--Qwen3-Next-80B-A3B-Instruct-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[storage_size]
+in_bytes = 46976204800

resources/mlx-community--Qwen3-Next-80B-A3B-Instruct-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[storage_size]
+in_bytes = 88814387200

resources/mlx-community--Qwen3-Next-80B-A3B-Thinking-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[storage_size]
+in_bytes = 88814387200

resources/mlx-community--Qwen3-Next-80B-A3B-Thinking-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Thinking-8bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[storage_size]
+in_bytes = 88814387200

resources/mlx-community--gpt-oss-120b-MXFP4-Q8.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/gpt-oss-120b-MXFP4-Q8"
+n_layers = 36
+hidden_size = 2880
+supports_tensor = true
+
+[storage_size]
+in_bytes = 70652212224

resources/mlx-community--gpt-oss-20b-MXFP4-Q8.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/gpt-oss-20b-MXFP4-Q8"
+n_layers = 24
+hidden_size = 2880
+supports_tensor = true
+
+[storage_size]
+in_bytes = 12025908224

resources/mlx-community--llama-3.3-70b-instruct-fp16.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/llama-3.3-70b-instruct-fp16"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+
+[storage_size]
+in_bytes = 144383672320

src/exo/master/api.py

@@ -1,5 +1,6 @@
 import time
 from collections.abc import AsyncGenerator
+from dataclasses import dataclass, field
 from http import HTTPStatus
 from typing import cast
 
@@ -19,8 +20,7 @@ from exo.master.placement import place_instance as get_instance_placements
 from exo.shared.apply import apply
 from exo.shared.election import ElectionMessage
 from exo.shared.logging import InterceptLogger
-from exo.shared.models.model_cards import MODEL_CARDS
-from exo.shared.models.model_meta import get_model_meta
+from exo.shared.models.model_cards import ModelCard, ModelId, get_model_cards
 from exo.shared.types.api import (
     BenchChatCompletionResponse,
     BenchChatCompletionTaskParams,
@@ -59,14 +59,13 @@ from exo.shared.types.events import (
     IndexedEvent,
 )
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.state import State
 from exo.shared.types.tasks import ChatCompletionTaskParams
 from exo.shared.types.worker.instances import Instance, InstanceId, InstanceMeta
 from exo.shared.types.worker.shards import Sharding
 from exo.utils.banner import print_startup_banner
 from exo.utils.channels import Receiver, Sender, channel
-from exo.utils.dashboard_path import find_dashboard
+from exo.utils.dashboard_path import RuntimeResources, find_directory
 from exo.utils.event_buffer import OrderedBuffer
 
 
@@ -87,57 +86,52 @@ def chunk_to_response(
     )
 
 
-async def resolve_model_meta(model_id: str) -> ModelMetadata:
-    if model_id in MODEL_CARDS:
-        model_card = MODEL_CARDS[model_id]
-        return model_card.metadata
-    else:
-        return await get_model_meta(model_id)
-
-
+@dataclass(eq=False)
 class API:
-    def __init__(
-        self,
+    node_id: NodeId
+    session_id: SessionId
+    port: int
+    app: FastAPI
+    global_event_receiver: Receiver[ForwarderEvent]
+    command_sender: Sender[ForwarderCommand]
+    election_receiver: Receiver[ElectionMessage]
+    state = field(init=False, default_factory=State)
+    _event_log: list[Event] = field(init=False, default_factory=list)
+    event_buffer: OrderedBuffer[Event] = field(init=False, default_factory=OrderedBuffer)
+    _chat_completion_queues: dict[CommandId, Sender[TokenChunk]] = field(init=False, default_factory=dict)
+    _tg: TaskGroup = field(init=False, default_factory=create_task_group)
+    last_completed_election: int = field(init=False, default=0)
+    paused: bool = field(init=False, default = False)
+    paused_ev: anyio.Event = field(init=False, default_factory=anyio.Event)
+
+    @classmethod
+    async def create(
+        cls,
         node_id: NodeId,
         session_id: SessionId,
         *,
         port: int,
-        # Ideally this would be a MasterForwarderEvent but type system says no :(
         global_event_receiver: Receiver[ForwarderEvent],
         command_sender: Sender[ForwarderCommand],
-        # This lets us pause the API if an election is running
         election_receiver: Receiver[ElectionMessage],
     ) -> None:
-        self.state = State()
-        self._event_log: list[Event] = []
-        self.command_sender = command_sender
-        self.global_event_receiver = global_event_receiver
-        self.election_receiver = election_receiver
-        self.event_buffer: OrderedBuffer[Event] = OrderedBuffer[Event]()
-        self.node_id: NodeId = node_id
-        self.session_id: SessionId = session_id
-        self.last_completed_election: int = 0
-        self.port = port
-
-        self.paused: bool = False
-        self.paused_ev: anyio.Event = anyio.Event()
-
-        self.app = FastAPI()
-        self._setup_exception_handlers()
-        self._setup_cors()
-        self._setup_routes()
-
-        self.app.mount(
+        app = FastAPI()
+        app.mount(
             "/",
             StaticFiles(
-                directory=find_dashboard(),
+                directory=await find_directory(RuntimeResources.Dashboard),
                 html=True,
             ),
             name="dashboard",
         )
 
-        self._chat_completion_queues: dict[CommandId, Sender[TokenChunk]] = {}
-        self._tg: TaskGroup | None = None
+        cls(node_id, session_id, port, app, global_event_receiver, command_sender, election_receiver)
+
+    def __post_init__(self) -> None:
+        self._setup_exception_handlers()
+        self._setup_cors()
+        self._setup_routes()
+
 
     def reset(self, new_session_id: SessionId, result_clock: int):
         logger.info("Resetting API State")
@@ -197,7 +191,7 @@ class API:
 
     async def place_instance(self, payload: PlaceInstanceParams):
         command = PlaceInstance(
-            model_meta=await resolve_model_meta(payload.model_id),
+            model_card=await resolve_model_card(payload.model_id),
             sharding=payload.sharding,
             instance_meta=payload.instance_meta,
             min_nodes=payload.min_nodes,
@@ -207,15 +201,15 @@ class API:
         return CreateInstanceResponse(
             message="Command received.",
             command_id=command.command_id,
-            model_meta=command.model_meta,
+            model_card=command.model_card,
         )
 
     async def create_instance(
         self, payload: CreateInstanceParams
     ) -> CreateInstanceResponse:
         instance = payload.instance
-        model_meta = await resolve_model_meta(instance.shard_assignments.model_id)
-        required_memory = model_meta.storage_size
+        model_card = await ModelCard.from_hf(instance.shard_assignments.model_id)
+        required_memory = model_card.storage_size
         available_memory = self._calculate_total_available_memory()
 
         if required_memory > available_memory:
@@ -232,7 +226,7 @@ class API:
         return CreateInstanceResponse(
             message="Command received.",
             command_id=command.command_id,
-            model_meta=model_meta,
+            model_card=model_card,
         )
 
     async def get_placement(
@@ -242,12 +236,12 @@ class API:
         instance_meta: InstanceMeta = InstanceMeta.MlxRing,
         min_nodes: int = 1,
     ) -> Instance:
-        model_meta = await resolve_model_meta(model_id)
+        model_card = await resolve_model_card(model_id)
 
         try:
             placements = get_instance_placements(
                 PlaceInstance(
-                    model_meta=model_meta,
+                    model_card=model_card,
                     sharding=sharding,
                     instance_meta=instance_meta,
                     min_nodes=min_nodes,
@@ -280,7 +274,7 @@ class API:
         if len(list(self.state.topology.list_nodes())) == 0:
             return PlacementPreviewResponse(previews=[])
 
-        cards = [card for card in MODEL_CARDS.values() if card.short_id == model_id]
+        cards = [card for card in await get_model_cards() if card.short_id == model_id]
         if not cards:
             raise HTTPException(status_code=404, detail=f"Model {model_id} not found")
 
@@ -298,13 +292,12 @@ class API:
         # TODO: PDD
         # instance_combinations.append((Sharding.PrefillDecodeDisaggregation, InstanceMeta.MlxRing, 1))
 
-        for card in cards:
-            model_meta = card.metadata
+        for model_card in cards:
             for sharding, instance_meta, min_nodes in instance_combinations:
                 try:
                     placements = get_instance_placements(
                         PlaceInstance(
-                            model_meta=model_meta,
+                            model_card=model_card,
                             sharding=sharding,
                             instance_meta=instance_meta,
                             min_nodes=min_nodes,
@@ -315,17 +308,17 @@ class API:
                         current_instances=self.state.instances,
                     )
                 except ValueError as exc:
-                    if (card.model_id, sharding, instance_meta, 0) not in seen:
+                    if (model_card.model_id, sharding, instance_meta, 0) not in seen:
                         previews.append(
                             PlacementPreview(
-                                model_id=card.model_id,
+                                model_id=model_card.model_id,
                                 sharding=sharding,
                                 instance_meta=instance_meta,
                                 instance=None,
                                 error=str(exc),
                             )
                         )
-                    seen.add((card.model_id, sharding, instance_meta, 0))
+                    seen.add((model_card.model_id, sharding, instance_meta, 0))
                     continue
 
                 current_ids = set(self.state.instances.keys())
@@ -336,17 +329,17 @@ class API:
                 ]
 
                 if len(new_instances) != 1:
-                    if (card.model_id, sharding, instance_meta, 0) not in seen:
+                    if (model_card.model_id, sharding, instance_meta, 0) not in seen:
                         previews.append(
                             PlacementPreview(
-                                model_id=card.model_id,
+                                model_id=model_card.model_id,
                                 sharding=sharding,
                                 instance_meta=instance_meta,
                                 instance=None,
                                 error="Expected exactly one new instance from placement",
                             )
                         )
-                    seen.add((card.model_id, sharding, instance_meta, 0))
+                    seen.add((model_card.model_id, sharding, instance_meta, 0))
                     continue
 
                 instance = new_instances[0]
@@ -355,7 +348,7 @@ class API:
 
                 memory_delta_by_node: dict[str, int] = {}
                 if node_ids:
-                    total_bytes = model_meta.storage_size.in_bytes
+                    total_bytes = model_card.storage_size.in_bytes
                     per_node = total_bytes // len(node_ids)
                     remainder = total_bytes % len(node_ids)
                     for index, node_id in enumerate(sorted(node_ids, key=str)):
@@ -363,14 +356,14 @@ class API:
                         memory_delta_by_node[str(node_id)] = per_node + extra
 
                 if (
-                    card.model_id,
+                    model_card.model_id,
                     sharding,
                     instance_meta,
                     len(node_ids),
                 ) not in seen:
                     previews.append(
                         PlacementPreview(
-                            model_id=card.model_id,
+                            model_id=model_card.model_id,
                             sharding=sharding,
                             instance_meta=instance_meta,
                             instance=instance,
@@ -378,7 +371,7 @@ class API:
                             error=None,
                         )
                     )
-                seen.add((card.model_id, sharding, instance_meta, len(node_ids)))
+                seen.add((model_card.model_id, sharding, instance_meta, len(node_ids)))
 
         return PlacementPreviewResponse(previews=previews)
 
@@ -553,8 +546,8 @@ class API:
         self, payload: ChatCompletionTaskParams
     ) -> ChatCompletionResponse | StreamingResponse:
         """Handle chat completions, supporting both streaming and non-streaming responses."""
-        model_meta = await resolve_model_meta(payload.model)
-        payload.model = model_meta.model_id
+        model_card = await resolve_model_card(payload.model)
+        payload.model = model_card.model_id
 
         if not any(
             instance.shard_assignments.model_id == payload.model
@@ -580,8 +573,8 @@ class API:
     async def bench_chat_completions(
         self, payload: BenchChatCompletionTaskParams
     ) -> BenchChatCompletionResponse:
-        model_meta = await resolve_model_meta(payload.model)
-        payload.model = model_meta.model_id
+        model_card = await resolve_model_card(payload.model)
+        payload.model = model_card.model_id
 
         if not any(
             instance.shard_assignments.model_id == payload.model
@@ -614,15 +607,15 @@ class API:
         return ModelList(
             data=[
                 ModelListModel(
-                    id=card.short_id,
+                    id=card.model_id,
                     hugging_face_id=card.model_id,
-                    name=card.name,
-                    description=card.description,
-                    tags=card.tags,
-                    storage_size_megabytes=int(card.metadata.storage_size.in_mb),
-                    supports_tensor=card.metadata.supports_tensor,
+                    name=card.model_id.short(),
+                    description="",
+                    tags=[],
+                    storage_size_megabytes=int(card.storage_size.in_mb),
+                    supports_tensor=card.supports_tensor,
                 )
-                for card in MODEL_CARDS.values()
+                for card in model_cards()
             ]
         )
 

src/exo/master/placement.py

@@ -14,6 +14,7 @@ from exo.master.placement_utils import (
     get_shard_assignments,
     get_smallest_cycles,
 )
+from exo.shared.models.model_cards import ModelId
 from exo.shared.topology import Topology
 from exo.shared.types.commands import (
     CreateInstance,
@@ -23,7 +24,6 @@ from exo.shared.types.commands import (
 from exo.shared.types.common import NodeId
 from exo.shared.types.events import Event, InstanceCreated, InstanceDeleted
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId
 from exo.shared.types.profiling import MemoryUsage, NodeNetworkInfo
 from exo.shared.types.worker.instances import (
     Instance,
@@ -60,27 +60,27 @@ def place_instance(
     cycles = topology.get_cycles()
     candidate_cycles = list(filter(lambda it: len(it) >= command.min_nodes, cycles))
     cycles_with_sufficient_memory = filter_cycles_by_memory(
-        candidate_cycles, node_memory, command.model_meta.storage_size
+        candidate_cycles, node_memory, command.model_card.storage_size
     )
     if len(cycles_with_sufficient_memory) == 0:
         raise ValueError("No cycles found with sufficient memory")
 
     if command.sharding == Sharding.Tensor:
-        if not command.model_meta.supports_tensor:
+        if not command.model_card.supports_tensor:
             raise ValueError(
-                f"Requested Tensor sharding but this model does not support tensor parallelism: {command.model_meta.model_id}"
+                f"Requested Tensor sharding but this model does not support tensor parallelism: {command.model_card.model_id}"
             )
         # TODO: the condition here for tensor parallel is not correct, but it works good enough for now.
         cycles_with_sufficient_memory = [
             cycle
             for cycle in cycles_with_sufficient_memory
-            if command.model_meta.hidden_size % len(cycle) == 0
+            if command.model_card.hidden_size % len(cycle) == 0
         ]
         if not cycles_with_sufficient_memory:
             raise ValueError(
-                f"No tensor sharding found for model with hidden_size {command.model_meta.hidden_size} candidate cycles"
+                f"No tensor sharding found for model with hidden_size {command.model_card.hidden_size} candidate cycles"
             )
-    if command.sharding == Sharding.Pipeline and command.model_meta.model_id == ModelId(
+    if command.sharding == Sharding.Pipeline and command.model_card.model_id == ModelId(
         "mlx-community/DeepSeek-V3.1-8bit"
     ):
         raise ValueError(
@@ -111,7 +111,7 @@ def place_instance(
     )
 
     shard_assignments = get_shard_assignments(
-        command.model_meta, selected_cycle, command.sharding, node_memory
+        command.model_card, selected_cycle, command.sharding, node_memory
     )
 
     cycle_digraph: Topology = topology.get_subgraph_from_nodes(selected_cycle.node_ids)

src/exo/master/placement_utils.py

@@ -2,10 +2,10 @@ from collections.abc import Generator, Mapping
 
 from loguru import logger
 
+from exo.shared.models.model_cards import ModelCard
 from exo.shared.topology import Topology
 from exo.shared.types.common import Host, NodeId
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelMetadata
 from exo.shared.types.profiling import MemoryUsage, NodeNetworkInfo
 from exo.shared.types.topology import Cycle, RDMAConnection, SocketConnection
 from exo.shared.types.worker.runners import RunnerId, ShardAssignments
@@ -75,7 +75,7 @@ def allocate_layers_proportionally(
 
 
 def get_shard_assignments_for_pipeline_parallel(
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
     cycle: Cycle,
     node_memory: Mapping[NodeId, MemoryUsage],
 ):
@@ -86,11 +86,10 @@ def get_shard_assignments_for_pipeline_parallel(
         (node_memory[node_id].ram_available for node_id in cycle.node_ids),
         start=Memory(),
     )
-
     if cycle_memory.in_bytes == 0:
         raise ValueError("Cannot create shard assignments: total available memory is 0")
 
-    total_layers = model_meta.n_layers
+    total_layers = model_card.n_layers
     world_size = len(cycle)
     runner_to_shard: dict[RunnerId, ShardMetadata] = {}
     node_to_runner: dict[NodeId, RunnerId] = {}
@@ -104,7 +103,7 @@ def get_shard_assignments_for_pipeline_parallel(
     )
 
     # Validate each node has sufficient memory for its assigned layers
-    memory_per_layer = model_meta.storage_size.in_bytes / total_layers
+    memory_per_layer = model_card.storage_size.in_bytes / total_layers
     for i, (node_id, node_layers) in enumerate(
         zip(cycle.node_ids, layer_allocations, strict=True)
     ):
@@ -124,7 +123,7 @@ def get_shard_assignments_for_pipeline_parallel(
         runner_id = RunnerId()
 
         shard = PipelineShardMetadata(
-            model_meta=model_meta,
+            model_card=model_card,
             device_rank=i,
             world_size=world_size,
             start_layer=layers_assigned,
@@ -137,7 +136,7 @@ def get_shard_assignments_for_pipeline_parallel(
         layers_assigned += node_layers
 
     shard_assignments = ShardAssignments(
-        model_id=model_meta.model_id,
+        model_id=model_card.model_id,
         runner_to_shard=runner_to_shard,
         node_to_runner=node_to_runner,
     )
@@ -146,17 +145,17 @@ def get_shard_assignments_for_pipeline_parallel(
 
 
 def get_shard_assignments_for_tensor_parallel(
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
     cycle: Cycle,
 ):
-    total_layers = model_meta.n_layers
+    total_layers = model_card.n_layers
     world_size = len(cycle)
     runner_to_shard: dict[RunnerId, ShardMetadata] = {}
     node_to_runner: dict[NodeId, RunnerId] = {}
 
     for i, node_id in enumerate(cycle):
         shard = TensorShardMetadata(
-            model_meta=model_meta,
+            model_card=model_card,
             device_rank=i,
             world_size=world_size,
             start_layer=0,
@@ -170,7 +169,7 @@ def get_shard_assignments_for_tensor_parallel(
         node_to_runner[node_id] = runner_id
 
     shard_assignments = ShardAssignments(
-        model_id=model_meta.model_id,
+        model_id=model_card.model_id,
         runner_to_shard=runner_to_shard,
         node_to_runner=node_to_runner,
     )
@@ -179,7 +178,7 @@ def get_shard_assignments_for_tensor_parallel(
 
 
 def get_shard_assignments(
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
     cycle: Cycle,
     sharding: Sharding,
     node_memory: Mapping[NodeId, MemoryUsage],
@@ -187,13 +186,13 @@ def get_shard_assignments(
     match sharding:
         case Sharding.Pipeline:
             return get_shard_assignments_for_pipeline_parallel(
-                model_meta=model_meta,
+                model_card=model_card,
                 cycle=cycle,
                 node_memory=node_memory,
             )
         case Sharding.Tensor:
             return get_shard_assignments_for_tensor_parallel(
-                model_meta=model_meta,
+                model_card=model_card,
                 cycle=cycle,
             )
 

src/exo/master/tests/test_master.py

@@ -7,6 +7,7 @@ from loguru import logger
 
 from exo.master.main import Master
 from exo.routing.router import get_node_id_keypair
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.types.api import ChatCompletionMessage, ChatCompletionTaskParams
 from exo.shared.types.commands import (
     ChatCompletion,
@@ -23,7 +24,6 @@ from exo.shared.types.events import (
     TaskCreated,
 )
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.profiling import (
     MemoryUsage,
 )
@@ -109,9 +109,8 @@ async def test_master():
                 command=(
                     PlaceInstance(
                         command_id=CommandId(),
-                        model_meta=ModelMetadata(
+                        model_card=ModelCard(
                             model_id=ModelId("llama-3.2-1b"),
-                            pretty_name="Llama 3.2 1B",
                             n_layers=16,
                             storage_size=Memory.from_bytes(678948),
                             hidden_size=7168,
@@ -167,9 +166,8 @@ async def test_master():
                     start_layer=0,
                     end_layer=16,
                     n_layers=16,
-                    model_meta=ModelMetadata(
+                    model_card=ModelCard(
                         model_id=ModelId("llama-3.2-1b"),
-                        pretty_name="Llama 3.2 1B",
                         n_layers=16,
                         storage_size=Memory.from_bytes(678948),
                         hidden_size=7168,

src/exo/master/tests/test_placement.py

@@ -10,12 +10,12 @@ from exo.master.tests.conftest import (
     create_rdma_connection,
     create_socket_connection,
 )
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.topology import Topology
 from exo.shared.types.commands import PlaceInstance
 from exo.shared.types.common import CommandId, NodeId
 from exo.shared.types.events import InstanceCreated, InstanceDeleted
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.multiaddr import Multiaddr
 from exo.shared.types.profiling import NetworkInterfaceInfo, NodeNetworkInfo
 from exo.shared.types.topology import Connection, SocketConnection
@@ -43,21 +43,20 @@ def instance() -> Instance:
 
 
 @pytest.fixture
-def model_meta() -> ModelMetadata:
-    return ModelMetadata(
+def model_card() -> ModelCard:
+    return ModelCard(
         model_id=ModelId("test-model"),
         storage_size=Memory.from_kb(1000),
-        pretty_name="Test Model",
         n_layers=10,
         hidden_size=30,
         supports_tensor=True,
     )
 
 
-def place_instance_command(model_meta: ModelMetadata) -> PlaceInstance:
+def place_instance_command(model_card: ModelCard) -> PlaceInstance:
     return PlaceInstance(
         command_id=CommandId(),
-        model_meta=model_meta,
+        model_card=model_card,
         sharding=Sharding.Pipeline,
         instance_meta=InstanceMeta.MlxRing,
         min_nodes=1,
@@ -76,16 +75,16 @@ def test_get_instance_placements_create_instance(
     available_memory: tuple[int, int, int],
     total_layers: int,
     expected_layers: tuple[int, int, int],
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
 ):
     # arrange
-    model_meta.n_layers = total_layers
-    model_meta.storage_size.in_bytes = sum(
+    model_card.n_layers = total_layers
+    model_card.storage_size.in_bytes = sum(
         available_memory
     )  # make it exactly fit across all nodes
     topology = Topology()
 
-    cic = place_instance_command(model_meta)
+    cic = place_instance_command(model_card)
     node_id_a = NodeId()
     node_id_b = NodeId()
     node_id_c = NodeId()
@@ -137,7 +136,7 @@ def test_get_instance_placements_create_instance(
     assert len(placements) == 1
     instance_id = list(placements.keys())[0]
     instance = placements[instance_id]
-    assert instance.shard_assignments.model_id == model_meta.model_id
+    assert instance.shard_assignments.model_id == model_card.model_id
 
     runner_id_a = instance.shard_assignments.node_to_runner[node_id_a]
     runner_id_b = instance.shard_assignments.node_to_runner[node_id_b]
@@ -164,10 +163,9 @@ def test_get_instance_placements_one_node_exact_fit() -> None:
     node_memory = {node_id: create_node_memory(1000 * 1024)}
     node_network = {node_id: create_node_network()}
     cic = place_instance_command(
-        ModelMetadata(
+        ModelCard(
             model_id=ModelId("test-model"),
             storage_size=Memory.from_kb(1000),
-            pretty_name="Test Model",
             n_layers=10,
             hidden_size=1000,
             supports_tensor=True,
@@ -191,10 +189,9 @@ def test_get_instance_placements_one_node_fits_with_extra_memory() -> None:
     node_memory = {node_id: create_node_memory(1001 * 1024)}
     node_network = {node_id: create_node_network()}
     cic = place_instance_command(
-        ModelMetadata(
+        ModelCard(
             model_id=ModelId("test-model"),
             storage_size=Memory.from_kb(1000),
-            pretty_name="Test Model",
             n_layers=10,
             hidden_size=1000,
             supports_tensor=True,
@@ -218,10 +215,9 @@ def test_get_instance_placements_one_node_not_fit() -> None:
     node_memory = {node_id: create_node_memory(1000 * 1024)}
     node_network = {node_id: create_node_network()}
     cic = place_instance_command(
-        model_meta=ModelMetadata(
+        model_card=ModelCard(
             model_id=ModelId("test-model"),
             storage_size=Memory.from_kb(1001),
-            pretty_name="Test Model",
             n_layers=10,
             hidden_size=1000,
             supports_tensor=True,
@@ -275,12 +271,14 @@ def test_get_transition_events_delete_instance(instance: Instance):
 
 
 def test_placement_selects_leaf_nodes(
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
 ):
     # arrange
     topology = Topology()
 
-    model_meta.storage_size = Memory.from_bytes(1000)
+    # Model requires more than any single node but fits within a 3-node cycle
+    model_card.storage_size.in_bytes = 1500
+    model_card.n_layers = 12
 
     node_id_a = NodeId()
     node_id_b = NodeId()
@@ -325,7 +323,7 @@ def test_placement_selects_leaf_nodes(
         Connection(source=node_id_d, sink=node_id_c, edge=create_socket_connection(1))
     )
 
-    cic = place_instance_command(model_meta=model_meta)
+    cic = place_instance_command(model_card=model_card)
 
     # act
     placements = place_instance(cic, topology, {}, node_memory, node_network)
@@ -344,12 +342,12 @@ def test_placement_selects_leaf_nodes(
 
 
 def test_tensor_rdma_backend_connectivity_matrix(
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
 ):
     # arrange
     topology = Topology()
-    model_meta.n_layers = 12
-    model_meta.storage_size.in_bytes = 1500
+    model_card.n_layers = 12
+    model_card.storage_size.in_bytes = 1500
 
     node_a = NodeId()
     node_b = NodeId()
@@ -411,7 +409,7 @@ def test_tensor_rdma_backend_connectivity_matrix(
         sharding=Sharding.Tensor,
         instance_meta=InstanceMeta.MlxJaccl,
         command_id=CommandId(),
-        model_meta=model_meta,
+        model_card=model_card,
         min_nodes=1,
     )
 

src/exo/master/tests/test_placement_utils.py

@@ -12,10 +12,10 @@ from exo.master.tests.conftest import (
     create_node_memory,
     create_socket_connection,
 )
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.topology import Topology
 from exo.shared.types.common import Host, NodeId
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.profiling import (
     NetworkInterfaceInfo,
     NodeNetworkInfo,
@@ -232,9 +232,8 @@ def test_get_shard_assignments(
         node_c_id: node_c_mem,
     }
 
-    model_meta = ModelMetadata(
+    model_card = ModelCard(
         model_id=ModelId("test-model"),
-        pretty_name="Test Model",
         n_layers=total_layers,
         storage_size=Memory.from_kb(1000),
         hidden_size=1000,
@@ -248,7 +247,7 @@ def test_get_shard_assignments(
 
     # act
     shard_assignments = get_shard_assignments(
-        model_meta, selected_cycle, Sharding.Pipeline, node_memory=node_memory
+        model_card, selected_cycle, Sharding.Pipeline, node_memory=node_memory
     )
 
     # assert
@@ -512,9 +511,8 @@ def test_get_shard_assignments_insufficient_memory_raises():
         node_c_id: node_c_mem,
     }
 
-    model_meta = ModelMetadata(
+    model_card = ModelCard(
         model_id=ModelId("test-model"),
-        pretty_name="Test Model",
         n_layers=20,
         storage_size=Memory.from_kb(1000),
         hidden_size=1000,
@@ -525,5 +523,5 @@ def test_get_shard_assignments_insufficient_memory_raises():
 
     with pytest.raises(ValueError, match="insufficient memory"):
         get_shard_assignments(
-            model_meta, selected_cycle, Sharding.Pipeline, node_memory
+            model_card, selected_cycle, Sharding.Pipeline, node_memory
         )

src/exo/shared/models/model_cards.py

@@ -1,613 +1,209 @@
-from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
-from exo.utils.pydantic_ext import CamelCaseModel
+from typing import Annotated
 
+import aiofiles
+import aiofiles.os as aios
+import tomlkit
+from anyio import Path, open_file
+from huggingface_hub import model_info
+from loguru import logger
+from pydantic import BaseModel, Field, PositiveInt, ValidationError
+from tomlkit.exceptions import TOMLKitError
+
+from exo.shared.models.model_cards import ModelCard, ModelId
+from exo.shared.types.common import Id
+from exo.shared.types.memory import Memory
+from exo.utils.dashboard_path import RuntimeResources, find_directory
+from exo.utils.pydantic_ext import CamelCaseModel
+from exo.worker.download.download_utils import (
+    ModelSafetensorsIndex,
+    download_file_with_retry,
+    ensure_models_dir,
+)
+
+
+class ModelId(Id):
+    def normalize(self) -> str:
+        return self.replace("/", "--")
+
+    def short(self) -> str:
+        return self.split("/")[-1]
+
+_card_cache: dict[str, ModelCard] = {}
 
 class ModelCard(CamelCaseModel):
-    short_id: str
     model_id: ModelId
-    name: str
-    description: str
-    tags: list[str]
-    metadata: ModelMetadata
+    storage_size: Memory
+    n_layers: PositiveInt
+    hidden_size: PositiveInt
+    supports_tensor: bool
 
+    async def save(self, path: Path) -> None:
+        async with await open_file(path, "w") as f:
+            py = self.model_dump()
+            data = tomlkit.dumps(py)  # pyright: ignore[reportUnknownMemberType]
+            await f.write(data)
+
+    async def save_to_default_path(self) -> None:
+        dir = await find_directory(RuntimeResources.Resources)
+        await self.save(dir / self.model_id.normalize())
+
+    @staticmethod
+    async def load_from_path(path: Path) -> ModelCard:
+        async with await open_file(path, "r") as f:
+            py = tomlkit.loads(await f.read())
+            return ModelCard.model_validate(py)
+
+    @staticmethod
+    async def load_from_default_path(model_id: ModelId) -> ModelCard:
+        return await ModelCard.load_from_path(await find_directory(RuntimeResources.Resources) / model_id.normalize())
+
+    @staticmethod
+    async def load(model_id: ModelId) -> ModelCard:
+        try:
+            return await ModelCard.load_from_default_path(model_id)
+        except (ValidationError, TOMLKitError, FileNotFoundError):
+            return await ModelCard.from_hf(model_id)
+
+
+    @staticmethod
+    async def from_hf(model_id: ModelId) -> ModelCard:
+        """Fetches storage size and number of layers for a Hugging Face model, returns Pydantic ModelMeta."""
+        if (mc := _card_cache.get(model_id, None)) is not None:
+            return mc
+        config_data = await get_config_data(model_id)
+        num_layers = config_data.layer_count
+        mem_size_bytes = await get_safetensors_size(model_id)
+
+        mc = ModelCard(
+            model_id=ModelId(model_id),
+            storage_size=mem_size_bytes,
+            n_layers=num_layers,
+            hidden_size=config_data.hidden_size or 0,
+            # TODO: all custom models currently do not support tensor. We could add a dynamic test for this?
+            supports_tensor=False,
+        )
+        _card_cache[model_id] = mc
+        return mc
+
+# TODO: should we cache this? how do we check for changes
+async def get_model_cards() -> list[ModelCard]:
+    dir = await find_directory(RuntimeResources.Resources)
+    cards: list[ModelCard] = []
+    async for file in dir.glob("*.toml"):
+        try:
+            cards.append(await ModelCard.load_from_path(file))
+        except (TOMLKitError, ValidationError):
+            continue
+
+    return cards
 
 MODEL_CARDS: dict[str, ModelCard] = {
-    # deepseek v3
-    "deepseek-v3.1-4bit": ModelCard(
-        short_id="deepseek-v3.1-4bit",
-        model_id=ModelId("mlx-community/DeepSeek-V3.1-4bit"),
-        name="DeepSeek V3.1 (4-bit)",
-        description="""DeepSeek V3.1 is a large language model trained on the DeepSeek V3.1 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/DeepSeek-V3.1-4bit"),
-            pretty_name="DeepSeek V3.1 (4-bit)",
-            storage_size=Memory.from_gb(378),
-            n_layers=61,
-            hidden_size=7168,
-            supports_tensor=True,
-        ),
-    ),
-    "deepseek-v3.1-8bit": ModelCard(
-        short_id="deepseek-v3.1-8bit",
-        model_id=ModelId("mlx-community/DeepSeek-V3.1-8bit"),
-        name="DeepSeek V3.1 (8-bit)",
-        description="""DeepSeek V3.1 is a large language model trained on the DeepSeek V3.1 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/DeepSeek-V3.1-8bit"),
-            pretty_name="DeepSeek V3.1 (8-bit)",
-            storage_size=Memory.from_gb(713),
-            n_layers=61,
-            hidden_size=7168,
-            supports_tensor=True,
-        ),
-    ),
-    # kimi k2
-    "kimi-k2-instruct-4bit": ModelCard(
-        short_id="kimi-k2-instruct-4bit",
-        model_id=ModelId("mlx-community/Kimi-K2-Instruct-4bit"),
-        name="Kimi K2 Instruct (4-bit)",
-        description="""Kimi K2 is a large language model trained on the Kimi K2 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Kimi-K2-Instruct-4bit"),
-            pretty_name="Kimi K2 Instruct (4-bit)",
-            storage_size=Memory.from_gb(578),
-            n_layers=61,
-            hidden_size=7168,
-            supports_tensor=True,
-        ),
-    ),
-    "kimi-k2-thinking": ModelCard(
-        short_id="kimi-k2-thinking",
-        model_id=ModelId("mlx-community/Kimi-K2-Thinking"),
-        name="Kimi K2 Thinking (4-bit)",
-        description="""Kimi K2 Thinking is the latest, most capable version of open-source thinking model.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Kimi-K2-Thinking"),
-            pretty_name="Kimi K2 Thinking (4-bit)",
-            storage_size=Memory.from_gb(658),
-            n_layers=61,
-            hidden_size=7168,
-            supports_tensor=True,
-        ),
-    ),
-    # llama-3.1
-    "llama-3.1-8b": ModelCard(
-        short_id="llama-3.1-8b",
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"),
-        name="Llama 3.1 8B (4-bit)",
-        description="""Llama 3.1 is a large language model trained on the Llama 3.1 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"),
-            pretty_name="Llama 3.1 8B (4-bit)",
-            storage_size=Memory.from_mb(4423),
-            n_layers=32,
-            hidden_size=4096,
-            supports_tensor=True,
-        ),
-    ),
-    "llama-3.1-8b-8bit": ModelCard(
-        short_id="llama-3.1-8b-8bit",
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-8bit"),
-        name="Llama 3.1 8B (8-bit)",
-        description="""Llama 3.1 is a large language model trained on the Llama 3.1 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-8bit"),
-            pretty_name="Llama 3.1 8B (8-bit)",
-            storage_size=Memory.from_mb(8540),
-            n_layers=32,
-            hidden_size=4096,
-            supports_tensor=True,
-        ),
-    ),
-    "llama-3.1-8b-bf16": ModelCard(
-        short_id="llama-3.1-8b-bf16",
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"),
-        name="Llama 3.1 8B (BF16)",
-        description="""Llama 3.1 is a large language model trained on the Llama 3.1 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"),
-            pretty_name="Llama 3.1 8B (BF16)",
-            storage_size=Memory.from_mb(16100),
-            n_layers=32,
-            hidden_size=4096,
-            supports_tensor=True,
-        ),
-    ),
-    "llama-3.1-70b": ModelCard(
-        short_id="llama-3.1-70b",
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-70B-Instruct-4bit"),
-        name="Llama 3.1 70B (4-bit)",
-        description="""Llama 3.1 is a large language model trained on the Llama 3.1 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Meta-Llama-3.1-70B-Instruct-4bit"),
-            pretty_name="Llama 3.1 70B (4-bit)",
-            storage_size=Memory.from_mb(38769),
-            n_layers=80,
-            hidden_size=8192,
-            supports_tensor=True,
-        ),
-    ),
-    # llama-3.2
-    "llama-3.2-1b": ModelCard(
-        short_id="llama-3.2-1b",
-        model_id=ModelId("mlx-community/Llama-3.2-1B-Instruct-4bit"),
-        name="Llama 3.2 1B (4-bit)",
-        description="""Llama 3.2 is a large language model trained on the Llama 3.2 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Llama-3.2-1B-Instruct-4bit"),
-            pretty_name="Llama 3.2 1B (4-bit)",
-            storage_size=Memory.from_mb(696),
-            n_layers=16,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
-    ),
-    "llama-3.2-3b": ModelCard(
-        short_id="llama-3.2-3b",
-        model_id=ModelId("mlx-community/Llama-3.2-3B-Instruct-4bit"),
-        name="Llama 3.2 3B (4-bit)",
-        description="""Llama 3.2 is a large language model trained on the Llama 3.2 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Llama-3.2-3B-Instruct-4bit"),
-            pretty_name="Llama 3.2 3B (4-bit)",
-            storage_size=Memory.from_mb(1777),
-            n_layers=28,
-            hidden_size=3072,
-            supports_tensor=True,
-        ),
-    ),
-    "llama-3.2-3b-8bit": ModelCard(
-        short_id="llama-3.2-3b-8bit",
-        model_id=ModelId("mlx-community/Llama-3.2-3B-Instruct-8bit"),
-        name="Llama 3.2 3B (8-bit)",
-        description="""Llama 3.2 is a large language model trained on the Llama 3.2 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Llama-3.2-3B-Instruct-8bit"),
-            pretty_name="Llama 3.2 3B (8-bit)",
-            storage_size=Memory.from_mb(3339),
-            n_layers=28,
-            hidden_size=3072,
-            supports_tensor=True,
-        ),
-    ),
-    # llama-3.3
-    "llama-3.3-70b": ModelCard(
-        short_id="llama-3.3-70b",
-        model_id=ModelId("mlx-community/Llama-3.3-70B-Instruct-4bit"),
-        name="Llama 3.3 70B (4-bit)",
-        description="""The Meta Llama 3.3 multilingual large language model (LLM) is an instruction tuned generative model in 70B (text in/text out)""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Llama-3.3-70B-Instruct-4bit"),
-            pretty_name="Llama 3.3 70B",
-            storage_size=Memory.from_mb(38769),
-            n_layers=80,
-            hidden_size=8192,
-            supports_tensor=True,
-        ),
-    ),
-    "llama-3.3-70b-8bit": ModelCard(
-        short_id="llama-3.3-70b-8bit",
-        model_id=ModelId("mlx-community/Llama-3.3-70B-Instruct-8bit"),
-        name="Llama 3.3 70B (8-bit)",
-        description="""The Meta Llama 3.3 multilingual large language model (LLM) is an instruction tuned generative model in 70B (text in/text out)""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Llama-3.3-70B-Instruct-8bit"),
-            pretty_name="Llama 3.3 70B (8-bit)",
-            storage_size=Memory.from_mb(73242),
-            n_layers=80,
-            hidden_size=8192,
-            supports_tensor=True,
-        ),
-    ),
-    "llama-3.3-70b-fp16": ModelCard(
-        short_id="llama-3.3-70b-fp16",
-        model_id=ModelId("mlx-community/llama-3.3-70b-instruct-fp16"),
-        name="Llama 3.3 70B (FP16)",
-        description="""The Meta Llama 3.3 multilingual large language model (LLM) is an instruction tuned generative model in 70B (text in/text out)""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/llama-3.3-70b-instruct-fp16"),
-            pretty_name="Llama 3.3 70B (FP16)",
-            storage_size=Memory.from_mb(137695),
-            n_layers=80,
-            hidden_size=8192,
-            supports_tensor=True,
-        ),
-    ),
-    # qwen3
-    "qwen3-0.6b": ModelCard(
-        short_id="qwen3-0.6b",
-        model_id=ModelId("mlx-community/Qwen3-0.6B-4bit"),
-        name="Qwen3 0.6B (4-bit)",
-        description="""Qwen3 0.6B is a large language model trained on the Qwen3 0.6B dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-0.6B-4bit"),
-            pretty_name="Qwen3 0.6B (4-bit)",
-            storage_size=Memory.from_mb(327),
-            n_layers=28,
-            hidden_size=1024,
-            supports_tensor=False,
-        ),
-    ),
-    "qwen3-0.6b-8bit": ModelCard(
-        short_id="qwen3-0.6b-8bit",
-        model_id=ModelId("mlx-community/Qwen3-0.6B-8bit"),
-        name="Qwen3 0.6B (8-bit)",
-        description="""Qwen3 0.6B is a large language model trained on the Qwen3 0.6B dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-0.6B-8bit"),
-            pretty_name="Qwen3 0.6B (8-bit)",
-            storage_size=Memory.from_mb(666),
-            n_layers=28,
-            hidden_size=1024,
-            supports_tensor=False,
-        ),
-    ),
-    "qwen3-30b": ModelCard(
-        short_id="qwen3-30b",
-        model_id=ModelId("mlx-community/Qwen3-30B-A3B-4bit"),
-        name="Qwen3 30B A3B (4-bit)",
-        description="""Qwen3 30B is a large language model trained on the Qwen3 30B dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-30B-A3B-4bit"),
-            pretty_name="Qwen3 30B A3B (4-bit)",
-            storage_size=Memory.from_mb(16797),
-            n_layers=48,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-30b-8bit": ModelCard(
-        short_id="qwen3-30b-8bit",
-        model_id=ModelId("mlx-community/Qwen3-30B-A3B-8bit"),
-        name="Qwen3 30B A3B (8-bit)",
-        description="""Qwen3 30B is a large language model trained on the Qwen3 30B dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-30B-A3B-8bit"),
-            pretty_name="Qwen3 30B A3B (8-bit)",
-            storage_size=Memory.from_mb(31738),
-            n_layers=48,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-80b-a3B-4bit": ModelCard(
-        short_id="qwen3-80b-a3B-4bit",
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit"),
-        name="Qwen3 80B A3B (4-bit)",
-        description="""Qwen3 80B""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit"),
-            pretty_name="Qwen3 80B A3B (4-bit)",
-            storage_size=Memory.from_mb(44800),
-            n_layers=48,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-80b-a3B-8bit": ModelCard(
-        short_id="qwen3-80b-a3B-8bit",
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit"),
-        name="Qwen3 80B A3B (8-bit)",
-        description="""Qwen3 80B""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit"),
-            pretty_name="Qwen3 80B A3B (8-bit)",
-            storage_size=Memory.from_mb(84700),
-            n_layers=48,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-80b-a3B-thinking-4bit": ModelCard(
-        short_id="qwen3-80b-a3B-thinking-4bit",
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit"),
-        name="Qwen3 80B A3B Thinking (4-bit)",
-        description="""Qwen3 80B Reasoning model""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit"),
-            pretty_name="Qwen3 80B A3B (4-bit)",
-            storage_size=Memory.from_mb(84700),
-            n_layers=48,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-80b-a3B-thinking-8bit": ModelCard(
-        short_id="qwen3-80b-a3B-thinking-8bit",
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Thinking-8bit"),
-        name="Qwen3 80B A3B Thinking (8-bit)",
-        description="""Qwen3 80B Reasoning model""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Thinking-8bit"),
-            pretty_name="Qwen3 80B A3B (8-bit)",
-            storage_size=Memory.from_mb(84700),
-            n_layers=48,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-235b-a22b-4bit": ModelCard(
-        short_id="qwen3-235b-a22b-4bit",
-        model_id=ModelId("mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit"),
-        name="Qwen3 235B A22B (4-bit)",
-        description="""Qwen3 235B (Active 22B) is a large language model trained on the Qwen3 235B dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit"),
-            pretty_name="Qwen3 235B A22B (4-bit)",
-            storage_size=Memory.from_gb(132),
-            n_layers=94,
-            hidden_size=4096,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-235b-a22b-8bit": ModelCard(
-        short_id="qwen3-235b-a22b-8bit",
-        model_id=ModelId("mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit"),
-        name="Qwen3 235B A22B (8-bit)",
-        description="""Qwen3 235B (Active 22B) is a large language model trained on the Qwen3 235B dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit"),
-            pretty_name="Qwen3 235B A22B (8-bit)",
-            storage_size=Memory.from_gb(250),
-            n_layers=94,
-            hidden_size=4096,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-coder-480b-a35b-4bit": ModelCard(
-        short_id="qwen3-coder-480b-a35b-4bit",
-        model_id=ModelId("mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"),
-        name="Qwen3 Coder 480B A35B (4-bit)",
-        description="""Qwen3 Coder 480B (Active 35B) is a large language model trained on the Qwen3 Coder 480B dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"),
-            pretty_name="Qwen3 Coder 480B A35B (4-bit)",
-            storage_size=Memory.from_gb(270),
-            n_layers=62,
-            hidden_size=6144,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-coder-480b-a35b-8bit": ModelCard(
-        short_id="qwen3-coder-480b-a35b-8bit",
-        model_id=ModelId("mlx-community/Qwen3-Coder-480B-A35B-Instruct-8bit"),
-        name="Qwen3 Coder 480B A35B (8-bit)",
-        description="""Qwen3 Coder 480B (Active 35B) is a large language model trained on the Qwen3 Coder 480B dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-Coder-480B-A35B-Instruct-8bit"),
-            pretty_name="Qwen3 Coder 480B A35B (8-bit)",
-            storage_size=Memory.from_gb(540),
-            n_layers=62,
-            hidden_size=6144,
-            supports_tensor=True,
-        ),
-    ),
-    # gpt-oss
-    "gpt-oss-120b-MXFP4-Q8": ModelCard(
-        short_id="gpt-oss-120b-MXFP4-Q8",
-        model_id=ModelId("mlx-community/gpt-oss-120b-MXFP4-Q8"),
-        name="GPT-OSS 120B (MXFP4-Q8, MLX)",
-        description="""OpenAI's GPT-OSS 120B is a 117B-parameter Mixture-of-Experts model designed for high-reasoning and general-purpose use; this variant is a 4-bit MLX conversion for Apple Silicon.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/gpt-oss-120b-MXFP4-Q8"),
-            pretty_name="GPT-OSS 120B (MXFP4-Q8, MLX)",
-            storage_size=Memory.from_kb(68_996_301),
-            n_layers=36,
-            hidden_size=2880,
-            supports_tensor=True,
-        ),
-    ),
-    "gpt-oss-20b-MXFP4-Q8": ModelCard(
-        short_id="gpt-oss-20b-MXFP4-Q8",
-        model_id=ModelId("mlx-community/gpt-oss-20b-MXFP4-Q8"),
-        name="GPT-OSS 20B (MXFP4-Q8, MLX)",
-        description="""OpenAI's GPT-OSS 20B is a medium-sized MoE model for lower-latency and local or specialized use cases; this variant is a 4-bit MLX conversion for Apple Silicon.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/gpt-oss-20b-MXFP4-Q8"),
-            pretty_name="GPT-OSS 20B (MXFP4-Q8, MLX)",
-            storage_size=Memory.from_kb(11_744_051),
-            n_layers=24,
-            hidden_size=2880,
-            supports_tensor=True,
-        ),
-    ),
-    # glm 4.5
-    "glm-4.5-air-8bit": ModelCard(
-        # Needs to be quantized g32 or g16 to work with tensor parallel
-        short_id="glm-4.5-air-8bit",
-        model_id=ModelId("mlx-community/GLM-4.5-Air-8bit"),
-        name="GLM 4.5 Air 8bit",
-        description="""GLM 4.5 Air 8bit""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/GLM-4.5-Air-8bit"),
-            pretty_name="GLM 4.5 Air 8bit",
-            storage_size=Memory.from_gb(114),
-            n_layers=46,
-            hidden_size=4096,
-            supports_tensor=False,
-        ),
-    ),
-    "glm-4.5-air-bf16": ModelCard(
-        short_id="glm-4.5-air-bf16",
-        model_id=ModelId("mlx-community/GLM-4.5-Air-bf16"),
-        name="GLM 4.5 Air bf16",
-        description="""GLM 4.5 Air bf16""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/GLM-4.5-Air-bf16"),
-            pretty_name="GLM 4.5 Air bf16",
-            storage_size=Memory.from_gb(214),
-            n_layers=46,
-            hidden_size=4096,
-            supports_tensor=True,
-        ),
-    ),
-    # glm 4.7
-    "glm-4.7-4bit": ModelCard(
-        short_id="glm-4.7-4bit",
-        model_id=ModelId("mlx-community/GLM-4.7-4bit"),
-        name="GLM 4.7 4bit",
-        description="GLM 4.7 4bit",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/GLM-4.7-4bit"),
-            pretty_name="GLM 4.7 4bit",
-            storage_size=Memory.from_bytes(198556925568),
-            n_layers=91,
-            hidden_size=5120,
-            supports_tensor=True,
-        ),
-    ),
-    "glm-4.7-6bit": ModelCard(
-        short_id="glm-4.7-6bit",
-        model_id=ModelId("mlx-community/GLM-4.7-6bit"),
-        name="GLM 4.7 6bit",
-        description="GLM 4.7 6bit",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/GLM-4.7-6bit"),
-            pretty_name="GLM 4.7 6bit",
-            storage_size=Memory.from_bytes(286737579648),
-            n_layers=91,
-            hidden_size=5120,
-            supports_tensor=True,
-        ),
-    ),
-    "glm-4.7-8bit-gs32": ModelCard(
-        short_id="glm-4.7-8bit-gs32",
-        model_id=ModelId("mlx-community/GLM-4.7-8bit-gs32"),
-        name="GLM 4.7 8bit (gs32)",
-        description="GLM 4.7 8bit (gs32)",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/GLM-4.7-8bit-gs32"),
-            pretty_name="GLM 4.7 8bit (gs32)",
-            storage_size=Memory.from_bytes(396963397248),
-            n_layers=91,
-            hidden_size=5120,
-            supports_tensor=True,
-        ),
-    ),
     # glm 4.7 flash
     "glm-4.7-flash-4bit": ModelCard(
-        short_id="glm-4.7-flash-4bit",
         model_id=ModelId("mlx-community/GLM-4.7-Flash-4bit"),
-        name="GLM 4.7 Flash 4bit",
-        description="GLM 4.7 Flash 4bit",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/GLM-4.7-Flash-4bit"),
-            pretty_name="GLM 4.7 Flash 4bit",
-            storage_size=Memory.from_gb(18),
-            n_layers=47,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
+        storage_size=Memory.from_gb(18),
+        n_layers=47,
+        hidden_size=2048,
+        supports_tensor=True,
     ),
     "glm-4.7-flash-5bit": ModelCard(
-        short_id="glm-4.7-flash-5bit",
         model_id=ModelId("mlx-community/GLM-4.7-Flash-5bit"),
-        name="GLM 4.7 Flash 5bit",
-        description="GLM 4.7 Flash 5bit",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/GLM-4.7-Flash-5bit"),
-            pretty_name="GLM 4.7 Flash 5bit",
-            storage_size=Memory.from_gb(21),
-            n_layers=47,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
+        storage_size=Memory.from_gb(21),
+        n_layers=47,
+        hidden_size=2048,
+        supports_tensor=True,
     ),
     "glm-4.7-flash-6bit": ModelCard(
-        short_id="glm-4.7-flash-6bit",
         model_id=ModelId("mlx-community/GLM-4.7-Flash-6bit"),
-        name="GLM 4.7 Flash 6bit",
-        description="GLM 4.7 Flash 6bit",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/GLM-4.7-Flash-6bit"),
-            pretty_name="GLM 4.7 Flash 6bit",
-            storage_size=Memory.from_gb(25),
-            n_layers=47,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
+        storage_size=Memory.from_gb(25),
+        n_layers=47,
+        hidden_size=2048,
+        supports_tensor=True,
     ),
     "glm-4.7-flash-8bit": ModelCard(
-        short_id="glm-4.7-flash-8bit",
         model_id=ModelId("mlx-community/GLM-4.7-Flash-8bit"),
-        name="GLM 4.7 Flash 8bit",
-        description="GLM 4.7 Flash 8bit",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/GLM-4.7-Flash-8bit"),
-            pretty_name="GLM 4.7 Flash 8bit",
-            storage_size=Memory.from_gb(32),
-            n_layers=47,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
-    ),
-    # minimax-m2
-    "minimax-m2.1-8bit": ModelCard(
-        short_id="minimax-m2.1-8bit",
-        model_id=ModelId("mlx-community/MiniMax-M2.1-8bit"),
-        name="MiniMax M2.1 8bit",
-        description="MiniMax M2.1 8bit",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/MiniMax-M2.1-8bit"),
-            pretty_name="MiniMax M2.1 8bit",
-            storage_size=Memory.from_bytes(242986745856),
-            n_layers=61,
-            hidden_size=3072,
-            supports_tensor=True,
-        ),
-    ),
-    "minimax-m2.1-3bit": ModelCard(
-        short_id="minimax-m2.1-3bit",
-        model_id=ModelId("mlx-community/MiniMax-M2.1-3bit"),
-        name="MiniMax M2.1 3bit",
-        description="MiniMax M2.1 3bit",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/MiniMax-M2.1-3bit"),
-            pretty_name="MiniMax M2.1 3bit",
-            storage_size=Memory.from_bytes(100086644736),
-            n_layers=61,
-            hidden_size=3072,
-            supports_tensor=True,
-        ),
+        storage_size=Memory.from_gb(32),
+        n_layers=47,
+        hidden_size=2048,
+        supports_tensor=True,
     ),
 }
+
+
+
+
+
+class ConfigData(BaseModel):
+    model_config = {"extra": "ignore"}  # Allow unknown fields
+
+    # Common field names for number of layers across different architectures
+    num_hidden_layers: Annotated[int, Field(ge=0)] | None = None
+    num_layers: Annotated[int, Field(ge=0)] | None = None
+    n_layer: Annotated[int, Field(ge=0)] | None = None
+    n_layers: Annotated[int, Field(ge=0)] | None = None  # Sometimes used
+    num_decoder_layers: Annotated[int, Field(ge=0)] | None = None  # Transformer models
+    decoder_layers: Annotated[int, Field(ge=0)] | None = None  # Some architectures
+    hidden_size: Annotated[int, Field(ge=0)] | None = None
+
+    @property
+    def layer_count(self) -> int:
+        # Check common field names for layer count
+        layer_fields = [
+            self.num_hidden_layers,
+            self.num_layers,
+            self.n_layer,
+            self.n_layers,
+            self.num_decoder_layers,
+            self.decoder_layers,
+        ]
+
+        for layer_count in layer_fields:
+            if layer_count is not None:
+                return layer_count
+
+        raise ValueError(
+            f"No layer count found in config.json: {self.model_dump_json()}"
+        )
+
+
+async def get_config_data(model_id: ModelId) -> ConfigData:
+    """Downloads and parses config.json for a model."""
+    target_dir = (await ensure_models_dir()) / model_id.normalize()
+    await aios.makedirs(target_dir, exist_ok=True)
+    config_path = await download_file_with_retry(
+        str(model_id),
+        "main",
+        "config.json",
+        target_dir,
+        lambda curr_bytes, total_bytes, is_renamed: logger.info(
+            f"Downloading config.json for {model_id}: {curr_bytes}/{total_bytes} ({is_renamed=})"
+        ),
+    )
+    async with aiofiles.open(config_path, "r") as f:
+        return ConfigData.model_validate_json(await f.read())
+
+
+async def get_safetensors_size(model_id: ModelId) -> Memory:
+    """Gets model size from safetensors index or falls back to HF API."""
+    target_dir = (await ensure_models_dir()) / model_id.normalize()
+    await aios.makedirs(target_dir, exist_ok=True)
+    index_path = await download_file_with_retry(
+        str(model_id),
+        "main",
+        "model.safetensors.index.json",
+        target_dir,
+        lambda curr_bytes, total_bytes, is_renamed: logger.info(
+            f"Downloading model.safetensors.index.json for {model_id}: {curr_bytes}/{total_bytes} ({is_renamed=})"
+        ),
+    )
+    async with aiofiles.open(index_path, "r") as f:
+        index_data = ModelSafetensorsIndex.model_validate_json(await f.read())
+
+    metadata = index_data.metadata
+    if metadata is not None:
+        return Memory.from_bytes(metadata.total_size)
+
+    info = model_info(model_id)
+    if info.safetensors is None:
+        raise ValueError(f"No safetensors info found for {model_id}")
+    return Memory.from_bytes(info.safetensors.total)

src/exo/shared/models/model_meta.py

@@ -6,9 +6,8 @@ from huggingface_hub import model_info
 from loguru import logger
 from pydantic import BaseModel, Field
 
-from exo.shared.models.model_cards import MODEL_CARDS
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.worker.download.download_utils import (
     ModelSafetensorsIndex,
     download_file_with_retry,
@@ -91,36 +90,23 @@ async def get_safetensors_size(model_id: str) -> Memory:
         raise ValueError(f"No safetensors info found for {model_id}")
     return Memory.from_bytes(info.safetensors.total)
 
+_model_card_cache: dict[str, ModelCard] = {}
 
-_model_meta_cache: dict[str, ModelMetadata] = {}
-
-
-async def get_model_meta(model_id: str) -> ModelMetadata:
-    if model_id in _model_meta_cache:
-        return _model_meta_cache[model_id]
-    model_meta = await _get_model_meta(model_id)
-    _model_meta_cache[model_id] = model_meta
-    return model_meta
-
-
-async def _get_model_meta(model_id: str) -> ModelMetadata:
+async def get_model_card(model_id: str) -> ModelCard:
     """Fetches storage size and number of layers for a Hugging Face model, returns Pydantic ModelMeta."""
+    if model_id in _model_card_cache:
+        return _model_card_cache[model_id]
     config_data = await get_config_data(model_id)
     num_layers = config_data.layer_count
     mem_size_bytes = await get_safetensors_size(model_id)
-    model_card = next(
-        (card for card in MODEL_CARDS.values() if card.model_id == ModelId(model_id)),
-        None,
-    )
 
-    return ModelMetadata(
+    mc = ModelCard(
         model_id=ModelId(model_id),
-        pretty_name=model_card.name if model_card is not None else model_id,
         storage_size=mem_size_bytes,
         n_layers=num_layers,
         hidden_size=config_data.hidden_size or 0,
         # TODO: all custom models currently do not support tensor. We could add a dynamic test for this?
-        supports_tensor=model_card.metadata.supports_tensor
-        if model_card is not None
-        else False,
+        supports_tensor=False,
     )
+    _model_card_cache[model_id] = mc
+    return mc

src/exo/shared/tests/conftest.py

@@ -7,8 +7,8 @@ import pytest
 from _pytest.logging import LogCaptureFixture
 from loguru import logger
 
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.worker.shards import PipelineShardMetadata, ShardMetadata
 
 
@@ -31,9 +31,8 @@ def get_pipeline_shard_metadata(
     model_id: ModelId, device_rank: int, world_size: int = 1
 ) -> ShardMetadata:
     return PipelineShardMetadata(
-        model_meta=ModelMetadata(
+        model_card=ModelCard(
             model_id=model_id,
-            pretty_name=str(model_id),
             storage_size=Memory.from_mb(100000),
             n_layers=32,
             hidden_size=1000,

src/exo/shared/types/api.py

@@ -4,9 +4,9 @@ from typing import Any, Literal
 from pydantic import BaseModel, Field, field_validator
 from pydantic_core import PydanticUseDefault
 
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.types.common import CommandId
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.worker.instances import Instance, InstanceId, InstanceMeta
 from exo.shared.types.worker.shards import Sharding
 
@@ -206,7 +206,7 @@ class DeleteInstanceTaskParams(BaseModel):
 class CreateInstanceResponse(BaseModel):
     message: str
     command_id: CommandId
-    model_meta: ModelMetadata
+    model_card: ModelCard
 
 
 class DeleteInstanceResponse(BaseModel):

src/exo/shared/types/chunks.py

@@ -1,10 +1,10 @@
 from enum import Enum
 
+from exo.shared.models.model_cards import ModelId
 from exo.shared.types.api import GenerationStats
 from exo.utils.pydantic_ext import TaggedModel
 
 from .api import FinishReason
-from .models import ModelId
 
 
 class ChunkType(str, Enum):

src/exo/shared/types/commands.py

@@ -1,8 +1,8 @@
 from pydantic import Field
 
+from exo.shared.models.model_cards import ModelCard
 from exo.shared.types.api import ChatCompletionTaskParams
 from exo.shared.types.common import CommandId, NodeId
-from exo.shared.types.models import ModelMetadata
 from exo.shared.types.worker.instances import Instance, InstanceId, InstanceMeta
 from exo.shared.types.worker.shards import Sharding
 from exo.utils.pydantic_ext import CamelCaseModel, TaggedModel
@@ -21,7 +21,7 @@ class ChatCompletion(BaseCommand):
 
 
 class PlaceInstance(BaseCommand):
-    model_meta: ModelMetadata
+    model_card: ModelCard
     sharding: Sharding
     instance_meta: InstanceMeta
     min_nodes: int

src/exo/shared/types/common.py

@@ -16,7 +16,9 @@ class Id(str):
         cls, _source: type, handler: GetCoreSchemaHandler
     ) -> core_schema.CoreSchema:
         # Just use a plain string schema
-        return core_schema.str_schema()
+        return core_schema.no_info_after_validator_function(
+            cls, core_schema.str_schema()
+        )
 
 
 class NodeId(Id):

src/exo/shared/types/models.py

@@ -1,18 +0,0 @@
-from pydantic import PositiveInt
-
-from exo.shared.types.common import Id
-from exo.shared.types.memory import Memory
-from exo.utils.pydantic_ext import CamelCaseModel
-
-
-class ModelId(Id):
-    pass
-
-
-class ModelMetadata(CamelCaseModel):
-    model_id: ModelId
-    pretty_name: str
-    storage_size: Memory
-    n_layers: PositiveInt
-    hidden_size: PositiveInt
-    supports_tensor: bool

src/exo/shared/types/worker/runners.py

@@ -2,8 +2,8 @@ from collections.abc import Mapping
 
 from pydantic import model_validator
 
+from exo.shared.models.model_cards import ModelId
 from exo.shared.types.common import Id, NodeId
-from exo.shared.types.models import ModelId
 from exo.shared.types.worker.shards import ShardMetadata
 from exo.utils.pydantic_ext import CamelCaseModel, TaggedModel
 

src/exo/shared/types/worker/shards.py

@@ -2,7 +2,7 @@ from enum import Enum
 
 from pydantic import Field
 
-from exo.shared.types.models import ModelMetadata
+from exo.shared.models.model_cards import ModelCard
 from exo.utils.pydantic_ext import TaggedModel
 
 
@@ -17,7 +17,7 @@ class BaseShardMetadata(TaggedModel):
     Replaces previous `Shard` object.
     """
 
-    model_meta: ModelMetadata
+    model_card: ModelCard
     device_rank: int
     world_size: int
 
@@ -41,7 +41,7 @@ class BaseShardMetadata(TaggedModel):
     def __hash__(self) -> int:
         return hash(
             (
-                self.model_meta.model_id,
+                self.model_card.model_id,
                 self.start_layer,
                 self.end_layer,
                 self.n_layers,

src/exo/utils/dashboard_path.py

@@ -1,45 +1,72 @@
+import enum
 import os
 import sys
-from pathlib import Path
 from typing import cast
 
+from anyio import Path
 
-def find_dashboard() -> Path:
-    dashboard = (
-        _find_dashboard_in_env()
-        or _find_dashboard_in_repo()
-        or _find_dashboard_in_bundle()
+
+class RuntimeResources(enum.Enum):
+    Dashboard = enum.auto
+    Resources = enum.auto
+
+_dir_cache: dict[RuntimeResources, Path]
+
+async def find_directory(rr: RuntimeResources) -> Path:
+    dir = (
+        _dir_cache.get(rr, None)
+        or await _find_in_env(rr)
+        or await _find_in_repo(rr)
+        or await _find_in_bundle(rr)
     )
-    if not dashboard:
+    if not dir:
         raise FileNotFoundError(
-            "Unable to locate dashboard assets - make sure the dashboard has been built, or export DASHBOARD_DIR if you've built the dashboard elsewhere."
+            "Unable to locate directory - make sure the dashboard has been built and the runtime resources (model cards) exist."
         )
-    return dashboard
+    _dir_cache[rr] = dir
+    return dir
 
 
-def _find_dashboard_in_env() -> Path | None:
-    env = os.environ.get("DASHBOARD_DIR")
+async def _find_in_env(rr: RuntimeResources) -> Path | None:
+    match rr:
+        case RuntimeResources.Dashboard:
+            env = os.environ.get("DASHBOARD_DIR")
+        case RuntimeResources.Resources:
+            env = os.environ.get("RESOURCES_DIR")
     if not env:
         return None
-    resolved_env = Path(env).expanduser().resolve()
+    resolved_env = await (await Path(env).expanduser()).resolve()
 
     return resolved_env
 
 
-def _find_dashboard_in_repo() -> Path | None:
-    current_module = Path(__file__).resolve()
+async def _find_in_repo(rr: RuntimeResources) -> Path | None:
+    current_module = await Path(__file__).resolve()
     for parent in current_module.parents:
-        build = parent / "dashboard" / "build"
-        if build.is_dir() and (build / "index.html").exists():
-            return build
+        match rr:
+            case RuntimeResources.Dashboard:
+                build = parent / "dashboard" / "build"
+                if await build.is_dir() and await (build / "index.html").exists():
+                    return build
+            case RuntimeResources.Resources:
+                res = parent / "resources"
+                if await res.is_dir():
+                    return res
     return None
 
 
-def _find_dashboard_in_bundle() -> Path | None:
+async def _find_in_bundle(rr: RuntimeResources) -> Path | None:
     frozen_root = cast(str | None, getattr(sys, "_MEIPASS", None))
     if frozen_root is None:
         return None
-    candidate = Path(frozen_root) / "dashboard"
-    if candidate.is_dir():
-        return candidate
+
+    match rr:
+        case RuntimeResources.Dashboard:
+            candidate = Path(frozen_root) / "dashboard"
+            if await candidate.is_dir():
+                return candidate
+        case RuntimeResources.Resources:
+            candidate = Path(frozen_root) / "resources"
+            if await candidate.is_dir():
+                return candidate
     return None

src/exo/worker/download/download_utils.py

@@ -460,10 +460,10 @@ async def resolve_allow_patterns(shard: ShardMetadata) -> list[str]:
     # (iii) Tensor parallel requires all files.
     return ["*"]
     try:
-        weight_map = await get_weight_map(str(shard.model_meta.model_id))
+        weight_map = await get_weight_map(str(shard.model_card.model_id))
         return get_allow_patterns(weight_map, shard)
     except Exception:
-        logger.error(f"Error getting weight map for {shard.model_meta.model_id=}")
+        logger.error(f"Error getting weight map for {shard.model_card.model_id=}")
         logger.error(traceback.format_exc())
         return ["*"]
 
@@ -532,18 +532,18 @@ async def download_shard(
     allow_patterns: list[str] | None = None,
 ) -> tuple[Path, RepoDownloadProgress]:
     if not skip_download:
-        logger.info(f"Downloading {shard.model_meta.model_id=}")
+        logger.info(f"Downloading {shard.model_card.model_id=}")
 
     # Handle local paths
-    if await aios.path.exists(str(shard.model_meta.model_id)):
-        logger.info(f"Using local model path {shard.model_meta.model_id}")
-        local_path = Path(str(shard.model_meta.model_id))
+    if await aios.path.exists(str(shard.model_card.model_id)):
+        logger.info(f"Using local model path {shard.model_card.model_id}")
+        local_path = Path(str(shard.model_card.model_id))
         return local_path, await download_progress_for_local_path(
-            str(shard.model_meta.model_id), shard, local_path
+            str(shard.model_card.model_id), shard, local_path
         )
 
     revision = "main"
-    target_dir = await ensure_models_dir() / str(shard.model_meta.model_id).replace(
+    target_dir = await ensure_models_dir() / str(shard.model_card.model_id).replace(
         "/", "--"
     )
     if not skip_download:
@@ -552,13 +552,13 @@ async def download_shard(
     if not allow_patterns:
         allow_patterns = await resolve_allow_patterns(shard)
 
-    logger.info(f"Downloading {shard.model_meta.model_id=} with {allow_patterns=}")
+    logger.info(f"Downloading {shard.model_card.model_id=} with {allow_patterns=}")
 
     all_start_time = time.time()
     # TODO: currently not recursive. Some models might require subdirectories - thus this will need to be changed.
     #  Update: <- This does not seem to be the case. Yay?
     file_list = await fetch_file_list_with_cache(
-        str(shard.model_meta.model_id), revision, recursive=True
+        str(shard.model_card.model_id), revision, recursive=True
     )
     filtered_file_list = list(
         filter_repo_objects(
@@ -592,7 +592,7 @@ async def download_shard(
             else timedelta(seconds=0)
         )
         file_progress[file.path] = RepoFileDownloadProgress(
-            repo_id=str(shard.model_meta.model_id),
+            repo_id=str(shard.model_card.model_id),
             repo_revision=revision,
             file_path=file.path,
             downloaded=Memory.from_bytes(curr_bytes),
@@ -609,7 +609,7 @@ async def download_shard(
             shard,
             calculate_repo_progress(
                 shard,
-                str(shard.model_meta.model_id),
+                str(shard.model_card.model_id),
                 revision,
                 file_progress,
                 all_start_time,
@@ -619,7 +619,7 @@ async def download_shard(
     for file in filtered_file_list:
         downloaded_bytes = await get_downloaded_size(target_dir / file.path)
         file_progress[file.path] = RepoFileDownloadProgress(
-            repo_id=str(shard.model_meta.model_id),
+            repo_id=str(shard.model_card.model_id),
             repo_revision=revision,
             file_path=file.path,
             downloaded=Memory.from_bytes(downloaded_bytes),
@@ -643,7 +643,7 @@ async def download_shard(
     async def download_with_semaphore(file: FileListEntry) -> None:
         async with semaphore:
             await download_file_with_retry(
-                str(shard.model_meta.model_id),
+                str(shard.model_card.model_id),
                 revision,
                 file.path,
                 target_dir,
@@ -657,7 +657,7 @@ async def download_shard(
             *[download_with_semaphore(file) for file in filtered_file_list]
         )
     final_repo_progress = calculate_repo_progress(
-        shard, str(shard.model_meta.model_id), revision, file_progress, all_start_time
+        shard, str(shard.model_card.model_id), revision, file_progress, all_start_time
     )
     await on_progress(shard, final_repo_progress)
     if gguf := next((f for f in filtered_file_list if f.path.endswith(".gguf")), None):

src/exo/worker/download/impl_shard_downloader.py

@@ -3,8 +3,7 @@ from collections.abc import Awaitable
 from pathlib import Path
 from typing import AsyncIterator, Callable
 
-from exo.shared.models.model_cards import MODEL_CARDS
-from exo.shared.models.model_meta import get_model_meta
+from exo.shared.models.model_cards import ModelCard, get_model_cards
 from exo.shared.types.worker.shards import (
     PipelineShardMetadata,
     ShardMetadata,
@@ -20,21 +19,21 @@ def exo_shard_downloader(max_parallel_downloads: int = 8) -> ShardDownloader:
 
 
 async def build_base_shard(model_id: str) -> ShardMetadata:
-    model_meta = await get_model_meta(model_id)
+    model_card = await ModelCard.from_hf(model_id)
     return PipelineShardMetadata(
-        model_meta=model_meta,
+        model_card=model_card,
         device_rank=0,
         world_size=1,
         start_layer=0,
-        end_layer=model_meta.n_layers,
-        n_layers=model_meta.n_layers,
+        end_layer=model_card.n_layers,
+        n_layers=model_card.n_layers,
     )
 
 
 async def build_full_shard(model_id: str) -> PipelineShardMetadata:
     base_shard = await build_base_shard(model_id)
     return PipelineShardMetadata(
-        model_meta=base_shard.model_meta,
+        model_card=base_shard.model_card,
         device_rank=base_shard.device_rank,
         world_size=base_shard.world_size,
         start_layer=base_shard.start_layer,
@@ -93,11 +92,11 @@ class CachedShardDownloader(ShardDownloader):
     async def ensure_shard(
         self, shard: ShardMetadata, config_only: bool = False
     ) -> Path:
-        if (shard.model_meta.model_id, shard) in self.cache:
-            return self.cache[(shard.model_meta.model_id, shard)]
+        if (shard.model_card.model_id, shard) in self.cache:
+            return self.cache[(shard.model_card.model_id, shard)]
 
         target_dir = await self.shard_downloader.ensure_shard(shard, config_only)
-        self.cache[(shard.model_meta.model_id, shard)] = target_dir
+        self.cache[(shard.model_card.model_id, shard)] = target_dir
         return target_dir
 
     async def get_shard_download_status(
@@ -159,7 +158,7 @@ class ResumableShardDownloader(ShardDownloader):
         # Kick off download status coroutines concurrently
         tasks = [
             asyncio.create_task(_status_for_model(model_card.model_id))
-            for model_card in MODEL_CARDS.values()
+            for model_card in await get_model_cards()
         ]
 
         for task in asyncio.as_completed(tasks):

src/exo/worker/download/shard_downloader.py

@@ -5,8 +5,8 @@ from datetime import timedelta
 from pathlib import Path
 from typing import AsyncIterator, Callable
 
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.worker.shards import (
     PipelineShardMetadata,
     ShardMetadata,
@@ -86,9 +86,8 @@ NOOP_DOWNLOAD_PROGRESS = RepoDownloadProgress(
     repo_id="noop",
     repo_revision="noop",
     shard=PipelineShardMetadata(
-        model_meta=ModelMetadata(
+        model_card=ModelCard(
             model_id=ModelId("noop"),
-            pretty_name="noope",
             storage_size=Memory.from_bytes(0),
             n_layers=1,
             hidden_size=1,

src/exo/worker/engines/mlx/utils_mlx.py

@@ -75,7 +75,7 @@ def get_weights_size(model_shard_meta: ShardMetadata) -> Memory:
     return Memory.from_float_kb(
         (model_shard_meta.end_layer - model_shard_meta.start_layer)
         / model_shard_meta.n_layers
-        * model_shard_meta.model_meta.storage_size.in_kb
+        * model_shard_meta.model_card.storage_size.in_kb
         / (
             1
             if isinstance(model_shard_meta, PipelineShardMetadata)
@@ -206,7 +206,7 @@ def load_mlx_items(
 ) -> tuple[Model, TokenizerWrapper]:
     if group is None:
         logger.info(f"Single device used for {bound_instance.instance}")
-        model_path = build_model_path(bound_instance.bound_shard.model_meta.model_id)
+        model_path = build_model_path(bound_instance.bound_shard.model_card.model_id)
         start_time = time.perf_counter()
         model, _ = load_model(model_path, strict=True)
         end_time = time.perf_counter()
@@ -234,7 +234,7 @@ def shard_and_load(
     group: Group,
     on_timeout: TimeoutCallback | None = None,
 ) -> tuple[nn.Module, TokenizerWrapper]:
-    model_path = build_model_path(shard_metadata.model_meta.model_id)
+    model_path = build_model_path(shard_metadata.model_card.model_id)
 
     model, _ = load_model(model_path, lazy=True, strict=False)
     logger.debug(model)
@@ -293,7 +293,7 @@ def shard_and_load(
 
 def get_tokenizer(model_path: Path, shard_metadata: ShardMetadata) -> TokenizerWrapper:
     """Load tokenizer for a model shard. Delegates to load_tokenizer_for_model_id."""
-    return load_tokenizer_for_model_id(shard_metadata.model_meta.model_id, model_path)
+    return load_tokenizer_for_model_id(shard_metadata.model_card.model_id, model_path)
 
 
 def get_eos_token_ids_for_model(model_id: str) -> list[int] | None:

src/exo/worker/main.py

@@ -8,6 +8,7 @@ from loguru import logger
 
 from exo.routing.connection_message import ConnectionMessage, ConnectionMessageType
 from exo.shared.apply import apply
+from exo.shared.models.model_cards import ModelId
 from exo.shared.types.commands import ForwarderCommand, RequestEventLog
 from exo.shared.types.common import NodeId, SessionId
 from exo.shared.types.events import (
@@ -22,7 +23,6 @@ from exo.shared.types.events import (
     TopologyEdgeCreated,
     TopologyEdgeDeleted,
 )
-from exo.shared.types.models import ModelId
 from exo.shared.types.multiaddr import Multiaddr
 from exo.shared.types.state import State
 from exo.shared.types.tasks import (
@@ -186,11 +186,11 @@ class Worker:
                         )
                     )
                 case DownloadModel(shard_metadata=shard):
-                    if shard.model_meta.model_id not in self.download_status:
+                    if shard.model_card.model_id not in self.download_status:
                         progress = DownloadPending(
                             shard_metadata=shard, node_id=self.node_id
                         )
-                        self.download_status[shard.model_meta.model_id] = progress
+                        self.download_status[shard.model_card.model_id] = progress
                         await self.event_sender.send(
                             NodeDownloadProgress(download_progress=progress)
                         )
@@ -205,7 +205,7 @@ class Worker:
                             node_id=self.node_id,
                             total_bytes=initial_progress.total_bytes,
                         )
-                        self.download_status[shard.model_meta.model_id] = progress
+                        self.download_status[shard.model_card.model_id] = progress
                         await self.event_sender.send(
                             NodeDownloadProgress(download_progress=progress)
                         )
@@ -339,7 +339,7 @@ class Worker:
                 initial_progress
             ),
         )
-        self.download_status[task.shard_metadata.model_meta.model_id] = status
+        self.download_status[task.shard_metadata.model_card.model_id] = status
         self.event_sender.send_nowait(NodeDownloadProgress(download_progress=status))
 
         last_progress_time = 0.0
@@ -356,7 +356,7 @@ class Worker:
                     node_id=self.node_id,
                     total_bytes=progress.total_bytes,
                 )
-                self.download_status[shard.model_meta.model_id] = status
+                self.download_status[shard.model_card.model_id] = status
                 await self.event_sender.send(
                     NodeDownloadProgress(download_progress=status)
                 )
@@ -376,7 +376,7 @@ class Worker:
                         progress
                     ),
                 )
-                self.download_status[shard.model_meta.model_id] = status
+                self.download_status[shard.model_card.model_id] = status
                 await self.event_sender.send(
                     NodeDownloadProgress(download_progress=status)
                 )
@@ -478,7 +478,7 @@ class Worker:
                     else:
                         continue
 
-                    self.download_status[progress.shard.model_meta.model_id] = status
+                    self.download_status[progress.shard.model_card.model_id] = status
                     await self.event_sender.send(
                         NodeDownloadProgress(download_progress=status)
                     )

src/exo/worker/plan.py

@@ -2,8 +2,8 @@
 
 from collections.abc import Mapping, Sequence
 
+from exo.shared.models.model_cards import ModelId
 from exo.shared.types.common import NodeId
-from exo.shared.types.models import ModelId
 from exo.shared.types.tasks import (
     ChatCompletion,
     ConnectToGroup,
@@ -114,7 +114,7 @@ def _model_needs_download(
     download_status: Mapping[ModelId, DownloadProgress],
 ) -> DownloadModel | None:
     for runner in runners.values():
-        model_id = runner.bound_instance.bound_shard.model_meta.model_id
+        model_id = runner.bound_instance.bound_shard.model_card.model_id
         if isinstance(runner.status, RunnerIdle) and (
             model_id not in download_status
             or not isinstance(
@@ -191,7 +191,7 @@ def _load_model(
             nid in global_download_status
             and any(
                 isinstance(dp, DownloadCompleted)
-                and dp.shard_metadata.model_meta.model_id == shard_assignments.model_id
+                and dp.shard_metadata.model_card.model_id == shard_assignments.model_id
                 for dp in global_download_status[nid]
             )
             for nid in shard_assignments.node_to_runner

src/exo/worker/runner/runner.py

@@ -213,7 +213,7 @@ def main(
                                                 command_id=command_id,
                                                 chunk=TokenChunk(
                                                     idx=response.token,
-                                                    model=shard_metadata.model_meta.model_id,
+                                                    model=shard_metadata.model_card.model_id,
                                                     text=response.text,
                                                     token_id=response.token,
                                                     finish_reason=response.finish_reason,
@@ -230,7 +230,7 @@ def main(
                                     command_id=command_id,
                                     chunk=TokenChunk(
                                         idx=0,
-                                        model=shard_metadata.model_meta.model_id,
+                                        model=shard_metadata.model_card.model_id,
                                         text="",
                                         token_id=0,
                                         finish_reason="error",

src/exo/worker/tests/constants.py

@@ -1,7 +1,7 @@
 from typing import Final
 
+from exo.shared.models.model_cards import ModelId
 from exo.shared.types.common import CommandId, NodeId
-from exo.shared.types.models import ModelId
 from exo.shared.types.tasks import TaskId
 from exo.shared.types.worker.instances import InstanceId, RunnerId
 

src/exo/worker/tests/unittests/conftest.py

@@ -1,8 +1,8 @@
 from dataclasses import dataclass, field
 
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.types.common import NodeId
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.tasks import BaseTask, TaskId
 from exo.shared.types.worker.instances import (
     BoundInstance,
@@ -32,9 +32,8 @@ def get_pipeline_shard_metadata(
     model_id: ModelId, device_rank: int, world_size: int = 1
 ) -> ShardMetadata:
     return PipelineShardMetadata(
-        model_meta=ModelMetadata(
+        model_card=ModelCard(
             model_id=model_id,
-            pretty_name=str(model_id),
             storage_size=Memory.from_mb(100000),
             n_layers=32,
             hidden_size=2048,

src/exo/worker/tests/unittests/test_mlx/conftest.py

@@ -11,9 +11,9 @@ import mlx.core as mx
 import mlx.nn as nn
 
 from exo.shared.constants import EXO_MODELS_DIR
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.types.api import ChatCompletionMessage
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.tasks import ChatCompletionTaskParams
 from exo.shared.types.worker.shards import PipelineShardMetadata, TensorShardMetadata
 from exo.worker.engines.mlx import Model
@@ -81,9 +81,8 @@ def run_gpt_oss_pipeline_device(
         start_layer, end_layer = layer_splits[rank]
 
         shard_meta = PipelineShardMetadata(
-            model_meta=ModelMetadata(
+            model_card=ModelCard(
                 model_id=ModelId(DEFAULT_GPT_OSS_MODEL_ID),
-                pretty_name="GPT-OSS 20B",
                 storage_size=Memory.from_gb(12),
                 n_layers=24,
                 hidden_size=2880,
@@ -151,9 +150,8 @@ def run_gpt_oss_tensor_parallel_device(
 
         # For tensor parallelism, all devices run all layers
         shard_meta = TensorShardMetadata(
-            model_meta=ModelMetadata(
+            model_card=ModelCard(
                 model_id=ModelId(DEFAULT_GPT_OSS_MODEL_ID),
-                pretty_name="GPT-OSS 20B",
                 storage_size=Memory.from_gb(12),
                 n_layers=24,
                 hidden_size=2880,

src/exo/worker/tests/unittests/test_mlx/test_tokenizers.py

@@ -76,13 +76,13 @@ def get_test_models() -> list[tuple[str, ModelCard]]:
     """Get a representative sample of models to test."""
     # Pick one model from each family to test
     families: dict[str, tuple[str, ModelCard]] = {}
-    for short_id, card in MODEL_CARDS.items():
+    for _, card in MODEL_CARDS.items():
         # Extract family name (e.g., "llama-3.1" from "llama-3.1-8b")
-        parts = short_id.split("-")
+        parts = card.model_id.short().split("-")
         family = "-".join(parts[:2]) if len(parts) >= 2 else parts[0]
 
         if family not in families:
-            families[family] = (short_id, card)
+            families[family] = (card.model_id.short(), card)
 
     return list(families.values())
 

src/exo/worker/tests/unittests/test_plan/test_download_and_loading.py

@@ -1,7 +1,7 @@
 import exo.worker.plan as plan_mod
+from exo.shared.models.model_cards import ModelId
 from exo.shared.types.common import NodeId
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId
 from exo.shared.types.tasks import LoadModel
 from exo.shared.types.worker.downloads import DownloadCompleted, DownloadProgress
 from exo.shared.types.worker.instances import BoundInstance

tests/headless_runner.py

@@ -12,7 +12,7 @@ from loguru import logger
 from pydantic import BaseModel
 
 from exo.shared.logging import InterceptLogger, logger_setup
-from exo.shared.models.model_cards import MODEL_CARDS, ModelId
+from exo.shared.models.model_cards import ModelId
 from exo.shared.types.api import ChatCompletionMessage, ChatCompletionTaskParams
 from exo.shared.types.commands import CommandId
 from exo.shared.types.common import Host, NodeId
@@ -82,29 +82,29 @@ async def tb_detection():
     send, recv = channel[GatheredInfo]()
     ig = InfoGatherer(send)
     with anyio.move_on_after(1):
-        await ig._monitor_system_profiler()  # pyright: ignore[reportPrivateUsage]
+        await ig._monitor_system_profiler_thunderbolt_data()  # pyright: ignore[reportPrivateUsage]
     with recv:
         return recv.collect()
 
 
 async def assert_downloads():
     sd = exo_shard_downloader()
-    # await sd.ensure_shard(await build_full_shard(MODEL_CARDS["qwen3-0.6b"].model_id))
+    # await sd.ensure_shard(ModelId("mlx-community/Qwen3-0.6B-8bit")))
     await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["llama-3.1-8b-bf16"].model_id)
+        await build_full_shard(ModelId("mlx-community/Llama-3.1-8b-bf16"))
     )
-    await sd.ensure_shard(await build_full_shard(MODEL_CARDS["qwen3-30b"].model_id))
+    await sd.ensure_shard(await build_full_shard(ModelId("mlx-community/Qwen3-30b-A3B")))
     await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["gpt-oss-120b-MXFP4-Q8"].model_id)
+        await build_full_shard(ModelId("mlx-commmunity/gpt-oss-120b-MXFP4-Q8"))
     )
     await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["gpt-oss-20b-4bit"].model_id)
+        await build_full_shard(ModelId("mlx-community/gpt-oss-20b-4bit"))
     )
     await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["glm-4.7-8bit-gs32"].model_id)
+        await build_full_shard(ModelId("mlx-community/GLM-4.7-8bit-gs32"))
     )
     await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["minimax-m2.1-8bit"].model_id)
+        await build_full_shard(ModelId("mlx-community/MiniMax-M2.1-8bit"))
     )
 
 
@@ -135,7 +135,7 @@ def ring_instance(test: Tests, iid: InstanceId, hn: str) -> Instance:
     else:
         raise ValueError(f"{hn} not in {test.devs}")
 
-    meta = MODEL_CARDS[test.model_id].metadata
+    card = MODEL_CARDS[test.model_id]
     instance = MlxRingInstance(
         instance_id=iid,
         ephemeral_port=52416,
@@ -145,15 +145,15 @@ def ring_instance(test: Tests, iid: InstanceId, hn: str) -> Instance:
             node_to_runner={NodeId(host[0]): RunnerId(host[0]) for host in test.devs},
             runner_to_shard={
                 RunnerId(test.devs[i][0]): PipelineShardMetadata(
-                    model_meta=meta,
+                    model_card=card,
                     device_rank=i,
                     world_size=world_size,
-                    start_layer=(meta.n_layers // world_size) * i,
+                    start_layer=(card.n_layers // world_size) * i,
                     end_layer=min(
-                        meta.n_layers, (meta.n_layers // world_size) * (i + 1)
+                        card.n_layers, (card.n_layers // world_size) * (i + 1)
                     ),
-                    n_layers=min(meta.n_layers, (meta.n_layers // world_size) * (i + 1))
-                    - (meta.n_layers // world_size) * i,
+                    n_layers=min(card.n_layers, (card.n_layers // world_size) * (i + 1))
+                    - (card.n_layers // world_size) * i,
                 )
                 for i in range(world_size)
             },
@@ -224,7 +224,7 @@ async def jaccl_backend(test: Tests):
 
 
 def jaccl_instance(test: Tests, iid: InstanceId):
-    meta = MODEL_CARDS[test.model_id].metadata
+    card = MODEL_CARDS[test.model_id]
     world_size = len(test.devs)
 
     return MlxJacclInstance(
@@ -239,12 +239,12 @@ def jaccl_instance(test: Tests, iid: InstanceId):
             node_to_runner={NodeId(host[0]): RunnerId(host[0]) for host in test.devs},
             runner_to_shard={
                 RunnerId(test.devs[i][0]): TensorShardMetadata(
-                    model_meta=meta,
+                    model_card=card,
                     device_rank=i,
                     world_size=world_size,
-                    start_layer=meta.n_layers,
-                    end_layer=meta.n_layers,
-                    n_layers=meta.n_layers,
+                    start_layer=card.n_layers,
+                    end_layer=card.n_layers,
+                    n_layers=card.n_layers,
                 )
                 for i in range(world_size)
             },