update api to match

gotta fix some tests

pyproject.toml

@@ -33,6 +33,7 @@ dependencies = [
     "mlx-lm>=0.28.3",
     "tiktoken>=0.12.0", # required for kimi k2 tokenizer
     "hypercorn>=0.18.0",
+    "openai-harmony>=0.0.8",
 ]
 
 [project.scripts]

src/exo/master/api.py

@@ -13,6 +13,12 @@ from hypercorn.asyncio import serve  # pyright: ignore[reportUnknownVariableType
 from hypercorn.config import Config
 from hypercorn.typing import ASGIFramework
 from loguru import logger
+from openai_harmony import (  # pyright: ignore[reportMissingTypeStubs]
+    HarmonyEncodingName,
+    Role,
+    StreamableParser,
+    load_harmony_encoding,
+)
 
 from exo.master.placement import place_instance as get_instance_placements
 from exo.shared.apply import apply
@@ -21,11 +27,13 @@ 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.types.api import (
+    ChatCompletionChoice,
     ChatCompletionMessage,
     ChatCompletionResponse,
     CreateInstanceParams,
     CreateInstanceResponse,
     DeleteInstanceResponse,
+    FinishReason,
     ModelList,
     ModelListModel,
     PlaceInstanceParams,
@@ -56,7 +64,7 @@ from exo.utils.channels import Receiver, Sender, channel
 from exo.utils.dashboard_path import find_dashboard
 from exo.utils.event_buffer import OrderedBuffer
 
-HIDE_THINKING = False
+encoding = load_harmony_encoding(HarmonyEncodingName.HARMONY_GPT_OSS)
 
 
 def chunk_to_response(
@@ -161,7 +169,9 @@ class API:
         self.app.delete("/instance/{instance_id}")(self.delete_instance)
         self.app.get("/models")(self.get_models)
         self.app.get("/v1/models")(self.get_models)
-        self.app.post("/v1/chat/completions")(self.chat_completions)
+        self.app.post("/v1/chat/completions", response_model=None)(
+            self.chat_completions
+        )
         self.app.get("/state")(lambda: self.state)
         self.app.get("/events")(lambda: self._event_log)
 
@@ -177,17 +187,32 @@ class API:
         return CreateInstanceResponse(
             message="Command received.",
             command_id=command.command_id,
+            model_meta=command.model_meta,
         )
 
     async def create_instance(
         self, payload: CreateInstanceParams
     ) -> CreateInstanceResponse:
-        command = CreateInstance(instance=payload.instance)
+        instance = payload.instance
+        model_meta = await resolve_model_meta(instance.shard_assignments.model_id)
+        required_memory = model_meta.storage_size
+        available_memory = self._calculate_total_available_memory()
+
+        if required_memory > available_memory:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Insufficient memory to create instance. Required: {required_memory.in_gb:.1f}GB, Available: {available_memory.in_gb:.1f}GB",
+            )
+
+        command = CreateInstance(
+            instance=instance,
+        )
         await self._send(command)
 
         return CreateInstanceResponse(
             message="Command received.",
             command_id=command.command_id,
+            model_meta=model_meta,
         )
 
     async def get_placement(
@@ -352,32 +377,52 @@ class API:
             instance_id=instance_id,
         )
 
-    async def _generate_chat_stream(
+    async def _process_gpt_oss(self, token_chunks: Receiver[TokenChunk]):
-        self, command_id: CommandId
+        stream = StreamableParser(encoding, role=Role.ASSISTANT)
-    ) -> AsyncGenerator[str, None]:
+        thinking = False
-        """Generate chat completion stream as JSON strings."""
+
+        async for chunk in token_chunks:
+            stream.process(chunk.token_id)
+
+            delta = stream.last_content_delta
+            ch = stream.current_channel
+
+            if ch == "analysis" and not thinking:
+                thinking = True
+                yield chunk.model_copy(update={"text": "<think>"})
+
+            if ch != "analysis" and thinking:
+                thinking = False
+                yield chunk.model_copy(update={"text": "</think>"})
+
+            if delta:
+                yield chunk.model_copy(update={"text": delta})
+
+            if chunk.finish_reason is not None:
+                if thinking:
+                    yield chunk.model_copy(update={"text": "</think>"})
+                yield chunk
+                break
+
+    async def _chat_chunk_stream(
+        self, command_id: CommandId, parse_gpt_oss: bool
+    ) -> AsyncGenerator[TokenChunk, None]:
+        """Yield `TokenChunk`s for a given command until completion."""
 
         try:
             self._chat_completion_queues[command_id], recv = channel[TokenChunk]()
 
-            is_thinking = False
             with recv as token_chunks:
-                async for chunk in token_chunks:
+                if parse_gpt_oss:
-                    if HIDE_THINKING:
+                    async for chunk in self._process_gpt_oss(token_chunks):
-                        if chunk.text == "<think>":
+                        yield chunk
-                            is_thinking = True
+                        if chunk.finish_reason is not None:
-                        if chunk.text == "</think>":
+                            break
-                            is_thinking = False
+                else:
-                    chunk_response: ChatCompletionResponse = chunk_to_response(
+                    async for chunk in token_chunks:
-                        chunk, command_id
+                        yield chunk
-                    )
+                        if chunk.finish_reason is not None:
-                    if not (is_thinking and HIDE_THINKING):
+                            break
-                        logger.debug(f"chunk_response: {chunk_response}")
-                        yield f"data: {chunk_response.model_dump_json()}\n\n"
-
-                    if chunk.finish_reason is not None:
-                        yield "data: [DONE]\n\n"
-                        break
 
         except anyio.get_cancelled_exc_class():
             # TODO: TaskCancelled
@@ -392,6 +437,59 @@ class API:
             await self._send(command)
             del self._chat_completion_queues[command_id]
 
+    async def _generate_chat_stream(
+        self, command_id: CommandId, parse_gpt_oss: bool
+    ) -> AsyncGenerator[str, None]:
+        """Generate chat completion stream as JSON strings."""
+
+        async for chunk in self._chat_chunk_stream(command_id, parse_gpt_oss):
+            chunk_response: ChatCompletionResponse = chunk_to_response(
+                chunk, command_id
+            )
+            logger.debug(f"chunk_response: {chunk_response}")
+
+            yield f"data: {chunk_response.model_dump_json()}\n\n"
+
+            if chunk.finish_reason is not None:
+                yield "data: [DONE]\n\n"
+
+    async def _collect_chat_completion(
+        self, command_id: CommandId, parse_gpt_oss: bool
+    ) -> ChatCompletionResponse:
+        """Collect all token chunks for a chat completion and return a single response."""
+
+        text_parts: list[str] = []
+        model: str | None = None
+        finish_reason: FinishReason | None = None
+
+        async for chunk in self._chat_chunk_stream(command_id, parse_gpt_oss):
+            if model is None:
+                model = chunk.model
+
+            text_parts.append(chunk.text)
+
+            if chunk.finish_reason is not None:
+                finish_reason = chunk.finish_reason
+
+        combined_text = "".join(text_parts)
+        assert model is not None
+
+        return ChatCompletionResponse(
+            id=command_id,
+            created=int(time.time()),
+            model=model,
+            choices=[
+                ChatCompletionChoice(
+                    index=0,
+                    message=ChatCompletionMessage(
+                        role="assistant",
+                        content=combined_text,
+                    ),
+                    finish_reason=finish_reason,
+                )
+            ],
+        )
+
     async def _trigger_notify_user_to_download_model(self, model_id: str) -> None:
         logger.warning(
             "TODO: we should send a notification to the user to download the model"
@@ -399,10 +497,12 @@ class API:
 
     async def chat_completions(
         self, payload: ChatCompletionTaskParams
-    ) -> StreamingResponse:
+    ) -> ChatCompletionResponse | StreamingResponse:
-        """Handle chat completions with proper streaming response."""
+        """Handle chat completions, supporting both streaming and non-streaming responses."""
         model_meta = await resolve_model_meta(payload.model)
         payload.model = model_meta.model_id
+        parse_gpt_oss = "gpt-oss" in model_meta.model_id.lower()
+        logger.info(f"{parse_gpt_oss=}")
 
         if not any(
             instance.shard_assignments.model_id == payload.model
@@ -417,10 +517,13 @@ class API:
             request_params=payload,
         )
         await self._send(command)
-        return StreamingResponse(
+        if payload.stream:
-            self._generate_chat_stream(command.command_id),
+            return StreamingResponse(
-            media_type="text/event-stream",
+                self._generate_chat_stream(command.command_id, parse_gpt_oss),
-        )
+                media_type="text/event-stream",
+            )
+
+        return await self._collect_chat_completion(command.command_id, parse_gpt_oss)
 
     def _calculate_total_available_memory(self) -> Memory:
         """Calculate total available memory across all nodes in bytes."""
@@ -442,6 +545,8 @@ class API:
                     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,
                 )
                 for card in MODEL_CARDS.values()
             ]
@@ -458,7 +563,7 @@ class API:
         async with create_task_group() as tg:
             self._tg = tg
             logger.info("Starting API")
-            tg.start_soon(self._applystate)
+            tg.start_soon(self._apply_state)
             tg.start_soon(self._pause_on_new_election)
             print_startup_banner(self.port)
             await serve(
@@ -470,7 +575,7 @@ class API:
         self.command_sender.close()
         self.global_event_receiver.close()
 
-    async def _applystate(self):
+    async def _apply_state(self):
         with self.global_event_receiver as events:
             async for f_event in events:
                 if f_event.origin != self.session_id.master_node_id:

src/exo/master/tests/test_master.py

@@ -123,6 +123,8 @@ async def test_master():
                             pretty_name="Llama 3.2 1B",
                             n_layers=16,
                             storage_size=Memory.from_bytes(678948),
+                            hidden_size=7168,
+                            supports_tensor=True,
                         ),
                         sharding=Sharding.Pipeline,
                         instance_meta=InstanceMeta.MlxRing,
@@ -180,6 +182,8 @@ async def test_master():
                             pretty_name="Llama 3.2 1B",
                             n_layers=16,
                             storage_size=Memory.from_bytes(678948),
+                            hidden_size=7168,
+                            supports_tensor=True,
                         ),
                         device_rank=0,
                         world_size=1,

src/exo/master/tests/test_placement.py

@@ -49,6 +49,8 @@ def model_meta() -> ModelMetadata:
         storage_size=Memory.from_kb(1000),
         pretty_name="Test Model",
         n_layers=10,
+        hidden_size=10,
+        supports_tensor=True,
     )
 
 
@@ -135,6 +137,8 @@ def test_get_instance_placements_one_node_exact_fit(
             storage_size=Memory.from_kb(1000),
             pretty_name="Test Model",
             n_layers=10,
+            hidden_size=1000,
+            supports_tensor=True,
         ),
     )
     placements = place_instance(cic, topology, {})
@@ -160,6 +164,8 @@ def test_get_instance_placements_one_node_fits_with_extra_memory(
             storage_size=Memory.from_kb(1000),
             pretty_name="Test Model",
             n_layers=10,
+            hidden_size=1000,
+            supports_tensor=True,
         ),
     )
     placements = place_instance(cic, topology, {})
@@ -185,6 +191,8 @@ def test_get_instance_placements_one_node_not_fit(
             storage_size=Memory.from_kb(1001),
             pretty_name="Test Model",
             n_layers=10,
+            hidden_size=1000,
+            supports_tensor=True,
         ),
     )
 

src/exo/master/tests/test_placement_utils.py

@@ -198,6 +198,8 @@ def test_get_shard_assignments(
         pretty_name="Test Model",
         n_layers=total_layers,
         storage_size=Memory.from_kb(1000),
+        hidden_size=1000,
+        supports_tensor=True,
     )
     cycles = topology.get_cycles()
     selected_cycle = cycles[0]

src/exo/shared/models/model_cards.py

@@ -51,6 +51,8 @@ MODEL_CARDS: dict[str, ModelCard] = {
             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(
@@ -64,6 +66,8 @@ MODEL_CARDS: dict[str, ModelCard] = {
             pretty_name="DeepSeek V3.1 (8-bit)",
             storage_size=Memory.from_gb(713),
             n_layers=61,
+            hidden_size=7168,
+            supports_tensor=True,
         ),
     ),
     # "deepseek-v3.2": ModelCard(
@@ -135,6 +139,8 @@ MODEL_CARDS: dict[str, ModelCard] = {
             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(
@@ -148,6 +154,8 @@ MODEL_CARDS: dict[str, ModelCard] = {
             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
@@ -162,6 +170,38 @@ MODEL_CARDS: dict[str, ModelCard] = {
             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(
@@ -175,6 +215,8 @@ MODEL_CARDS: dict[str, ModelCard] = {
             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
@@ -189,6 +231,8 @@ MODEL_CARDS: dict[str, ModelCard] = {
             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(
@@ -202,6 +246,8 @@ MODEL_CARDS: dict[str, ModelCard] = {
             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(
@@ -215,6 +261,8 @@ MODEL_CARDS: dict[str, ModelCard] = {
             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
@@ -229,6 +277,8 @@ MODEL_CARDS: dict[str, ModelCard] = {
             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(
@@ -242,6 +292,8 @@ MODEL_CARDS: dict[str, ModelCard] = {
             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(
@@ -255,20 +307,8 @@ MODEL_CARDS: dict[str, ModelCard] = {
             pretty_name="Llama 3.3 70B (FP16)",
             storage_size=Memory.from_mb(137695),
             n_layers=80,
-        ),
+            hidden_size=8192,
-    ),
+            supports_tensor=True,
-    # phi-3
-    "phi-3-mini": ModelCard(
-        short_id="phi-3-mini",
-        model_id=ModelId("mlx-community/Phi-3-mini-128k-instruct-4bit"),
-        name="Phi 3 Mini 128k (4-bit)",
-        description="""Phi 3 Mini is a large language model trained on the Phi 3 Mini dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Phi-3-mini-128k-instruct-4bit"),
-            pretty_name="Phi 3 Mini 128k (4-bit)",
-            storage_size=Memory.from_mb(2099),
-            n_layers=32,
         ),
     ),
     # qwen3
@@ -283,6 +323,8 @@ MODEL_CARDS: dict[str, ModelCard] = {
             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(
@@ -296,6 +338,8 @@ MODEL_CARDS: dict[str, ModelCard] = {
             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(
@@ -309,6 +353,8 @@ MODEL_CARDS: dict[str, ModelCard] = {
             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(
@@ -322,6 +368,68 @@ MODEL_CARDS: dict[str, ModelCard] = {
             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(
@@ -335,6 +443,8 @@ MODEL_CARDS: dict[str, ModelCard] = {
             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(
@@ -348,6 +458,8 @@ MODEL_CARDS: dict[str, ModelCard] = {
             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(
@@ -361,6 +473,8 @@ MODEL_CARDS: dict[str, ModelCard] = {
             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(
@@ -374,77 +488,84 @@ MODEL_CARDS: dict[str, ModelCard] = {
             pretty_name="Qwen3 Coder 480B A35B (8-bit)",
             storage_size=Memory.from_gb(540),
             n_layers=62,
+            hidden_size=6144,
+            supports_tensor=True,
         ),
     ),
-    # granite
+    # gpt-oss
-    "granite-3.3-2b": ModelCard(
+    "gpt-oss-120b-MXFP4-Q8": ModelCard(
-        short_id="granite-3.3-2b",
+        short_id="gpt-oss-120b-MXFP4-Q8",
-        model_id=ModelId("mlx-community/granite-3.3-2b-instruct-fp16"),
+        model_id=ModelId("mlx-community/gpt-oss-120b-MXFP4-Q8"),
-        name="Granite 3.3 2B (FP16)",
+        name="GPT-OSS 120B (MXFP4-Q8, MLX)",
-        description="""Granite-3.3-2B-Instruct is a 2-billion parameter 128K context length language model fine-tuned for improved reasoning and instruction-following capabilities.""",
+        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/granite-3.3-2b-instruct-fp16"),
+            model_id=ModelId("mlx-community/gpt-oss-120b-MXFP4-Q8"),
-            pretty_name="Granite 3.3 2B (FP16)",
+            pretty_name="GPT-OSS 120B (MXFP4-Q8, MLX)",
-            storage_size=Memory.from_mb(4951),
+            storage_size=Memory.from_kb(68_996_301),
-            n_layers=40,
+            n_layers=36,
+            hidden_size=2880,
+            supports_tensor=True,
         ),
     ),
-    # "granite-3.3-8b": ModelCard(
+    "gpt-oss-20b-4bit": ModelCard(
-    #     short_id="granite-3.3-8b",
+        short_id="gpt-oss-20b-4bit",
-    #     model_id=ModelId("mlx-community/granite-3.3-8b-instruct-fp16"),
+        model_id=ModelId("mlx-community/gpt-oss-20b-MXFP4-Q4"),
-    #     name="Granite 3.3 8B",
+        name="GPT-OSS 20B (MXFP4-Q4, MLX)",
-    #     description="""Granite-3.3-8B-Instruct is a 8-billion parameter 128K context length language model fine-tuned for improved reasoning and instruction-following capabilities.""",
+        description="""OpenAI's GPT-OSS 20B is a medium-sized MoE model for lower-latency and local or specialized use cases; this MLX variant uses MXFP4 4-bit quantization.""",
+        tags=[],
+        metadata=ModelMetadata(
+            model_id=ModelId("mlx-community/gpt-oss-20b-MXFP4-Q4"),
+            pretty_name="GPT-OSS 20B (MXFP4-Q4, MLX)",
+            storage_size=Memory.from_kb(11_744_051),
+            n_layers=24,
+            hidden_size=2880,
+            supports_tensor=True,
+        ),
+    ),
+    # Needs to be quantized g32 or g16.
+    "glm-4.5-air-8bit": ModelCard(
+        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,
+        ),
+    ),
+    # "devstral-2-123b-instruct-2512-8bit": ModelCard(
+    #     short_id="devstral-2-123b-instruct-2512-8bit",
+    #     model_id=ModelId("mlx-community/Devstral-2-123B-Instruct-2512-8bit"),
+    #     name="Devstral 2 123B Instruct 2512 (8-bit, MLX)",
+    #     description="""Mistral AI's Devstral 2 123B Instruct (2512) is an agentic coding model.""",
     #     tags=[],
     #     metadata=ModelMetadata(
-    #         model_id=ModelId("mlx-community/granite-3.3-8b-instruct-fp16"),
+    #         model_id=ModelId("mlx-community/Devstral-2-123B-Instruct-2512-8bit"),
-    #         pretty_name="Granite 3.3 8B",
+    #         pretty_name="Devstral 2 123B Instruct 2512 (8-bit, MLX)",
-    #         storage_size=Memory.from_kb(15958720),
+    #         storage_size=Memory.from_kb(133_000_000),
-    #         n_layers=40,
+    #         n_layers=88,
-    #     ),
+    #         hidden_size=12288,
-    # ),
-    # smol-lm
-    # "smol-lm-135m": ModelCard(
-    #     short_id="smol-lm-135m",
-    #     model_id="mlx-community/SmolLM-135M-4bit",
-    #     name="Smol LM 135M",
-    #     description="""SmolLM is a series of state-of-the-art small language models available in three sizes: 135M, 360M, and 1.7B parameters. """,
-    #     tags=[],
-    #     metadata=ModelMetadata(
-    #         model_id=ModelId("mlx-community/SmolLM-135M-4bit"),
-    #         pretty_name="Smol LM 135M",
-    #         storage_size=Memory.from_kb(73940),
-    #         n_layers=30,
-    #     ),
-    # ),
-    # 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-4bit": ModelCard(
-    #     short_id="gpt-oss-20b-4bit",
-    #     model_id=ModelId("mlx-community/gpt-oss-20b-MXFP4-Q4"),
-    #     name="GPT-OSS 20B (MXFP4-Q4, MLX)",
-    #     description="""OpenAI's GPT-OSS 20B is a medium-sized MoE model for lower-latency and local or specialized use cases; this MLX variant uses MXFP4 4-bit quantization.""",
-    #     tags=[],
-    #     metadata=ModelMetadata(
-    #         model_id=ModelId("mlx-community/gpt-oss-20b-MXFP4-Q4"),
-    #         pretty_name="GPT-OSS 20B (MXFP4-Q4, MLX)",
-    #         storage_size=Memory.from_kb(11_744_051),
-    #         n_layers=24,
-    #         hidden_size=2880,
     #         supports_tensor=True,
     #     ),
     # ),

src/exo/shared/models/model_meta.py

@@ -6,6 +6,7 @@ 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.types.memory import Memory
 from exo.shared.types.models import ModelId, ModelMetadata
 from exo.worker.download.download_utils import (
@@ -25,6 +26,7 @@ class ConfigData(BaseModel):
     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:
@@ -106,10 +108,19 @@ async def _get_model_meta(model_id: str) -> ModelMetadata:
     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(
         model_id=ModelId(model_id),
-        pretty_name=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,
     )

src/exo/shared/types/api.py

@@ -5,7 +5,7 @@ from pydantic import BaseModel, Field, field_validator
 from pydantic_core import PydanticUseDefault
 
 from exo.shared.types.common import CommandId
-from exo.shared.types.models import ModelId
+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
 
@@ -174,6 +174,7 @@ class DeleteInstanceTaskParams(BaseModel):
 class CreateInstanceResponse(BaseModel):
     message: str
     command_id: CommandId
+    model_meta: ModelMetadata
 
 
 class DeleteInstanceResponse(BaseModel):

src/exo/shared/types/models.py

@@ -14,3 +14,5 @@ class ModelMetadata(CamelCaseModel):
     pretty_name: str
     storage_size: Memory
     n_layers: PositiveInt
+    hidden_size: PositiveInt
+    supports_tensor: bool

src/exo/worker/download/shard_downloader.py

@@ -1,4 +1,5 @@
 from abc import ABC, abstractmethod
+from copy import copy
 from datetime import timedelta
 from pathlib import Path
 from typing import AsyncIterator, Callable
@@ -12,7 +13,7 @@ from exo.shared.types.worker.shards import (
 from exo.worker.download.download_utils import RepoDownloadProgress
 
 
-# TODO: the PipelineShardMetadata getting reinstantiated is a bit messy. Shoudl this be a classmethod?
+# TODO: the PipelineShardMetadata getting reinstantiated is a bit messy. Should this be a classmethod?
 class ShardDownloader(ABC):
     @abstractmethod
     async def ensure_shard(
@@ -43,34 +44,7 @@ class ShardDownloader(ABC):
         Yields:
             tuple[Path, RepoDownloadProgress]: The path and progress of a shard download.
         """
-        yield (
+        yield (Path("/tmp/noop_shard"), NOOP_DOWNLOAD_PROGRESS)
-            Path("/tmp/noop_shard"),
-            RepoDownloadProgress(
-                repo_id="noop",
-                repo_revision="noop",
-                shard=PipelineShardMetadata(
-                    model_meta=ModelMetadata(
-                        model_id=ModelId("noop"),
-                        pretty_name="noope",
-                        storage_size=Memory.from_bytes(0),
-                        n_layers=1,
-                    ),
-                    device_rank=0,
-                    world_size=1,
-                    start_layer=0,
-                    end_layer=1,
-                    n_layers=1,
-                ),
-                completed_files=0,
-                total_files=0,
-                downloaded_bytes=Memory.from_bytes(0),
-                downloaded_bytes_this_session=Memory.from_bytes(0),
-                total_bytes=Memory.from_bytes(0),
-                overall_speed=0,
-                overall_eta=timedelta(seconds=0),
-                status="complete",
-            ),
-        )
 
     @abstractmethod
     async def get_shard_download_status_for_shard(
@@ -94,46 +68,41 @@ class NoopShardDownloader(ShardDownloader):
     ) -> AsyncIterator[tuple[Path, RepoDownloadProgress]]:
         yield (
             Path("/tmp/noop_shard"),
-            RepoDownloadProgress(
+            NOOP_DOWNLOAD_PROGRESS,
-                repo_id="noop",
-                repo_revision="noop",
-                shard=PipelineShardMetadata(
-                    model_meta=ModelMetadata(
-                        model_id=ModelId("noop"),
-                        pretty_name="noope",
-                        storage_size=Memory.from_bytes(0),
-                        n_layers=1,
-                    ),
-                    device_rank=0,
-                    world_size=1,
-                    start_layer=0,
-                    end_layer=1,
-                    n_layers=1,
-                ),
-                completed_files=0,
-                total_files=0,
-                downloaded_bytes=Memory.from_bytes(0),
-                downloaded_bytes_this_session=Memory.from_bytes(0),
-                total_bytes=Memory.from_bytes(0),
-                overall_speed=0,
-                overall_eta=timedelta(seconds=0),
-                status="complete",
-            ),
         )
 
     async def get_shard_download_status_for_shard(
         self, shard: ShardMetadata
     ) -> RepoDownloadProgress:
-        return RepoDownloadProgress(
+        dp = copy(NOOP_DOWNLOAD_PROGRESS)
-            repo_id="noop",
+        dp.shard = shard
-            repo_revision="noop",
+        return dp
-            shard=shard,
+
-            completed_files=0,
+
-            total_files=0,
+NOOP_DOWNLOAD_PROGRESS = RepoDownloadProgress(
-            downloaded_bytes=Memory.from_bytes(0),
+    repo_id="noop",
-            downloaded_bytes_this_session=Memory.from_bytes(0),
+    repo_revision="noop",
-            total_bytes=Memory.from_bytes(0),
+    shard=PipelineShardMetadata(
-            overall_speed=0,
+        model_meta=ModelMetadata(
-            overall_eta=timedelta(seconds=0),
+            model_id=ModelId("noop"),
-            status="complete",
+            pretty_name="noope",
-        )
+            storage_size=Memory.from_bytes(0),
+            n_layers=1,
+            hidden_size=0,
+            supports_tensor=False,
+        ),
+        device_rank=0,
+        world_size=1,
+        start_layer=0,
+        end_layer=1,
+        n_layers=1,
+    ),
+    completed_files=0,
+    total_files=0,
+    downloaded_bytes=Memory.from_bytes(0),
+    downloaded_bytes_this_session=Memory.from_bytes(0),
+    total_bytes=Memory.from_bytes(0),
+    overall_speed=0,
+    overall_eta=timedelta(seconds=0),
+    status="complete",
+)

src/exo/worker/runner/bootstrap.py

@@ -2,16 +2,13 @@ import os
 
 import loguru
 
-from exo.shared.types.events import Event
+from exo.shared.types.events import Event, RunnerStatusUpdated
 from exo.shared.types.tasks import Task
 from exo.shared.types.worker.instances import BoundInstance, MlxJacclInstance
+from exo.shared.types.worker.runners import RunnerFailed
 from exo.utils.channels import MpReceiver, MpSender
 
-logger: "loguru.Logger"
+logger: "loguru.Logger" = loguru.logger
-
-
-if os.getenv("EXO_TESTS") == "1":
-    logger = loguru.logger
 
 
 def entrypoint(
@@ -30,6 +27,23 @@ def entrypoint(
     logger = _logger
 
     # Import main after setting global logger - this lets us just import logger from this module
-    from exo.worker.runner.runner import main
+    try:
+        from exo.worker.runner.runner import main
 
-    main(bound_instance, event_sender, task_receiver)
+        main(bound_instance, event_sender, task_receiver)
+    except Exception as e:
+        logger.opt(exception=e).warning(
+            f"Runner {bound_instance.bound_runner_id} crashed with critical exception {e}"
+        )
+        event_sender.send(
+            RunnerStatusUpdated(
+                runner_id=bound_instance.bound_runner_id,
+                runner_status=RunnerFailed(error_message=str(e)),
+            )
+        )
+    finally:
+        event_sender.close()
+        task_receiver.close()
+        event_sender.join()
+        task_receiver.join()
+        logger.info("bye from the runner")

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

@@ -1,5 +1,10 @@
+from __future__ import annotations
+
+from collections.abc import Iterator
 from dataclasses import dataclass
 
+from anyio import ClosedResourceError, WouldBlock
+
 from exo.shared.types.common import NodeId
 from exo.shared.types.memory import Memory
 from exo.shared.types.models import ModelId, ModelMetadata
@@ -14,6 +19,96 @@ from exo.shared.types.worker.runners import RunnerId, RunnerStatus, ShardAssignm
 from exo.shared.types.worker.shards import PipelineShardMetadata, ShardMetadata
 
 
+# Synchronous trivial sender and receiver.
+@dataclass
+class _State[T]:
+    buffer: list[T]
+    closed: bool = False
+
+
+class MockSender[T]:
+    def __init__(self, _state: _State[T] | None = None):
+        self._state = _state or _State(buffer=[])
+        self._closed = False
+
+    def send(self, item: T):
+        if self._closed:
+            raise ClosedResourceError
+        self._state.buffer.append(item)
+
+    def close(self):
+        self._closed = True
+        self._state.closed = True
+
+    def join(self):
+        pass
+
+    def clone(self) -> MockSender[T]:
+        if self._closed:
+            raise ClosedResourceError
+        return MockSender(_state=self._state)
+
+    def clone_receiver(self) -> MockReceiver[T]:
+        if self._closed:
+            raise ClosedResourceError
+        return MockReceiver(_state=self._state)
+
+
+class MockReceiver[T]:
+    def __init__(self, _state: _State[T] | None = None):
+        self._state = _state or _State(buffer=[])
+        self._closed = False
+
+    def close(self):
+        self._closed = True
+        self._state.closed = True
+
+    def join(self):
+        pass
+
+    def clone(self) -> MockReceiver[T]:
+        if self._closed:
+            raise ClosedResourceError
+        return MockReceiver(_state=self._state)
+
+    def clone_sender(self) -> MockSender[T]:
+        if self._closed:
+            raise ClosedResourceError
+        return MockSender(_state=self._state)
+
+    def receive_nowait(self) -> T:
+        if self._state.buffer:
+            return self._state.buffer.pop(0)
+        raise WouldBlock
+
+    def collect(self) -> list[T]:
+        out: list[T] = []
+        while True:
+            try:
+                out.append(self.receive_nowait())
+            except WouldBlock:
+                break
+        return out
+
+    async def receive_at_least(self, n: int) -> list[T]:
+        raise NotImplementedError
+
+    def __enter__(self):
+        return self
+
+    def __iter__(self) -> Iterator[T]:
+        while True:
+            try:
+                yield self.receive_nowait()
+            except WouldBlock:
+                break
+
+    def __exit__(self, exc_type, exc, tb):
+        # Don't swallow exceptions
+        return False
+
+
+# Runner supervisor without multiprocessing logic.
 @dataclass(frozen=True)
 class FakeRunnerSupervisor:
     bound_instance: BoundInstance
@@ -35,6 +130,8 @@ def get_pipeline_shard_metadata(
             pretty_name=str(model_id),
             storage_size=Memory.from_mb(100000),
             n_layers=32,
+            hidden_size=2048,
+            supports_tensor=False,
         ),
         device_rank=device_rank,
         world_size=world_size,
@@ -67,5 +164,21 @@ def get_mlx_ring_instance(
         shard_assignments=get_shard_assignments(
             model_id, node_to_runner, runner_to_shard
         ),
-        hosts=[],
+        hosts_by_node={},
+        ephemeral_port=0,
+    )
+
+
+def get_bound_mlx_ring_instance(
+    instance_id: InstanceId, model_id: ModelId, runner_id: RunnerId, node_id: NodeId
+) -> BoundInstance:
+    shard = get_pipeline_shard_metadata(model_id=model_id, device_rank=0, world_size=1)
+    instance = get_mlx_ring_instance(
+        instance_id=instance_id,
+        model_id=model_id,
+        node_to_runner={node_id: runner_id},
+        runner_to_shard={runner_id: shard},
+    )
+    return BoundInstance(
+        instance=instance, bound_runner_id=runner_id, bound_node_id=node_id
     )

src/exo/worker/tests/unittests/test_runner/test_runner_supervisor.py

@@ -0,0 +1 @@
+# TODO:

uv.lock

@@ -336,6 +336,7 @@ dependencies = [
     { name = "mlx", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "mlx-lm", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "networkx", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
+    { name = "openai-harmony", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "protobuf", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "psutil", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "pydantic", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
@@ -377,6 +378,7 @@ requires-dist = [
     { name = "mlx", specifier = ">=0.29.3" },
     { name = "mlx-lm", specifier = ">=0.28.3" },
     { name = "networkx", specifier = ">=3.5" },
+    { name = "openai-harmony", specifier = ">=0.0.8" },
     { name = "protobuf", specifier = ">=6.32.0" },
     { name = "psutil", specifier = ">=7.0.0" },
     { name = "pydantic", specifier = ">=2.11.7" },
@@ -940,6 +942,27 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e2/c1/6dba12fdf68b02a21ac411c9df19afa66bed2540f467150ca64d246b463d/numpy-2.3.4-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:e1708fac43ef8b419c975926ce1eaf793b0c13b7356cfab6ab0dc34c0a02ac0f", size = 18652691, upload-time = "2025-10-15T16:17:46.247Z" },
 ]
 
+[[package]]
+name = "openai-harmony"
+version = "0.0.8"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pydantic", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/3e/92/2d038d096f29179c7c9571b431f9e739f87a487121901725e23fe338dd9d/openai_harmony-0.0.8.tar.gz", hash = "sha256:6e43f98e6c242fa2de6f8ea12eab24af63fa2ed3e89c06341fb9d92632c5cbdf", size = 284777, upload-time = "2025-11-05T19:07:06.727Z" }
+wheels = [
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