fix: enable psutil fallback for memory monitoring when macmon is missing on macOS

On Darwin, the psutil memory poller was disabled (memory_poll_rate=None),
relying entirely on macmon. When macmon is not installed, no memory data
was reported, causing nodes to show zero memory in the cluster state and
blocking shard placement.

Now falls back to psutil-based memory polling when macmon is not found.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

.mlx_typings/mlx_lm/models/step3p5.pyi

@@ -0,0 +1,151 @@
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs
+from .switch_layers import SwitchGLU
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str
+    hidden_size: int
+    num_hidden_layers: int
+    vocab_size: int
+    num_attention_heads: int
+    num_attention_groups: int
+    head_dim: int
+    intermediate_size: int
+    rms_norm_eps: float
+    rope_theta: float
+    rope_scaling: Optional[Dict[str, Any]]
+    max_position_embeddings: int
+    sliding_window: int
+    layer_types: Optional[List[str]]
+    yarn_only_types: Optional[List[str]]
+    partial_rotary_factors: Optional[List[float]]
+    attention_other_setting: Optional[Dict[str, Any]]
+    use_head_wise_attn_gate: bool
+    moe_num_experts: int
+    moe_top_k: int
+    moe_intermediate_size: int
+    share_expert_dim: int
+    moe_layers_enum: Optional[str]
+    moe_router_scaling_factor: float
+    norm_expert_weight: bool
+    swiglu_limits: Optional[List[float]]
+    swiglu_limits_shared: Optional[List[float]]
+    tie_word_embeddings: bool
+
+class Step3p5MLP(nn.Module):
+    hidden_size: int
+    intermediate_size: int
+    gate_proj: nn.Linear
+    up_proj: nn.Linear
+    down_proj: nn.Linear
+    limit: Optional[float]
+
+    def __init__(
+        self, args: ModelArgs, intermediate_size: int, swiglu_limit: float = 0
+    ) -> None: ...
+    def __call__(self, x: mx.array) -> mx.array: ...
+
+class Step3p5MoEGate(nn.Module):
+    top_k: int
+    n_routed_experts: int
+    routed_scaling_factor: float
+    norm_topk_prob: bool
+    gate: nn.Linear
+    router_bias: mx.array
+
+    def __init__(self, args: ModelArgs) -> None: ...
+    def __call__(self, x: mx.array) -> tuple[mx.array, mx.array]: ...
+
+class Step3p5MoE(nn.Module):
+    gate: Step3p5MoEGate
+    switch_mlp: SwitchGLU
+    share_expert: Step3p5MLP
+    sharding_group: Optional[mx.distributed.Group]
+
+    def __init__(self, args: ModelArgs, layer_idx: int) -> None: ...
+    def __call__(self, x: mx.array) -> mx.array: ...
+
+class Step3p5Attention(nn.Module):
+    is_sliding: bool
+    num_heads: int
+    num_kv_heads: int
+    head_dim: int
+    scale: float
+    q_proj: nn.Linear
+    k_proj: nn.Linear
+    v_proj: nn.Linear
+    o_proj: nn.Linear
+    q_norm: nn.Module
+    k_norm: nn.Module
+    use_head_wise_attn_gate: bool
+    g_proj: nn.Linear
+    rope: nn.Module
+
+    def __init__(self, args: ModelArgs, layer_idx: int) -> None: ...
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array: ...
+
+class Step3p5DecoderLayer(nn.Module):
+    self_attn: Step3p5Attention
+    is_sliding: bool
+    is_moe_layer: bool
+    mlp: Step3p5MLP | Step3p5MoE
+    input_layernorm: nn.Module
+    post_attention_layernorm: nn.Module
+
+    def __init__(self, args: ModelArgs, layer_idx: int) -> None: ...
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array: ...
+
+class Step3p5Model(nn.Module):
+    args: ModelArgs
+    vocab_size: int
+    num_layers: int
+    embed_tokens: nn.Embedding
+    layers: list[Step3p5DecoderLayer]
+    norm: nn.Module
+    _swa_idx: Optional[int]
+    _full_idx: Optional[int]
+
+    def __init__(self, args: ModelArgs) -> None: ...
+    def __call__(
+        self,
+        x: mx.array,
+        cache: Optional[List[Any]] = None,
+    ) -> mx.array: ...
+
+class Model(nn.Module):
+    args: ModelArgs
+    model_type: str
+    model: Step3p5Model
+    lm_head: nn.Linear
+
+    def __init__(self, args: ModelArgs) -> None: ...
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache: Optional[List[Any]] = None,
+    ) -> mx.array: ...
+    def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...
+    def shard(self, group: Optional[mx.distributed.Group] = None) -> None: ...
+    @property
+    def layers(self) -> list[Step3p5DecoderLayer]: ...
+    def make_cache(self) -> list[Any]: ...
+    @property
+    def cast_predicate(self) -> Any: ...
+    @property
+    def quant_predicate(self) -> Any: ...

bench/exo_bench.py

@@ -19,6 +19,11 @@ from urllib.parse import urlencode
 from loguru import logger
 from transformers import AutoTokenizer
 
+# Backoff constants for cluster settling retry
+_SETTLE_INITIAL_BACKOFF_S = 1.0
+_SETTLE_MAX_BACKOFF_S = 60.0
+_SETTLE_BACKOFF_MULTIPLIER = 2.0
+
 # Monkey-patch for transformers 5.x compatibility
 # Kimi's tokenization_kimi.py imports bytes_to_unicode from the old location
 # which was moved in transformers 5.0.0rc2
@@ -388,6 +393,66 @@ class PromptSizer:
         return content, tok
 
 
+def fetch_and_filter_placements(
+    client: ExoClient, full_model_id: str, args: argparse.Namespace
+) -> list[dict[str, Any]]:
+    previews_resp = client.request_json(
+        "GET", "/instance/previews", params={"model_id": full_model_id}
+    )
+    previews = previews_resp.get("previews") or []
+
+    selected: list[dict[str, Any]] = []
+    for p in previews:
+        if p.get("error") is not None:
+            continue
+        if not placement_filter(str(p.get("instance_meta", "")), args.instance_meta):
+            continue
+        if not sharding_filter(str(p.get("sharding", "")), args.sharding):
+            continue
+
+        instance = p.get("instance")
+        if not isinstance(instance, dict):
+            continue
+
+        n = nodes_used_in_instance(instance)
+        # Skip tensor ring single node as it is pointless when pipeline ring
+        if n == 1 and (
+            (args.sharding == "both" and "tensor" in p.get("sharding", "").lower())
+            or (
+                args.instance_meta == "both"
+                and "jaccl" in p.get("instance_meta", "").lower()
+            )
+        ):
+            continue
+
+        if (
+            args.skip_pipeline_jaccl
+            and (
+                args.instance_meta == "both"
+                and "jaccl" in p.get("instance_meta", "").lower()
+            )
+            and (
+                args.sharding == "both" and "pipeline" in p.get("sharding", "").lower()
+            )
+        ):
+            continue
+
+        if (
+            args.skip_tensor_ring
+            and (
+                args.instance_meta == "both"
+                and "ring" in p.get("instance_meta", "").lower()
+            )
+            and (args.sharding == "both" and "tensor" in p.get("sharding", "").lower())
+        ):
+            continue
+
+        if args.min_nodes <= n <= args.max_nodes:
+            selected.append(p)
+
+    return selected
+
+
 def main() -> int:
     ap = argparse.ArgumentParser(
         prog="exo-bench",
@@ -464,6 +529,12 @@ def main() -> int:
         action="store_true",
         help="Force all pp×tg combinations (cartesian product) even when lists have equal length.",
     )
+    ap.add_argument(
+        "--settle-timeout",
+        type=float,
+        default=0,
+        help="Max seconds to wait for the cluster to produce valid placements (0 = try once).",
+    )
     args = ap.parse_args()
 
     pp_list = parse_int_list(args.pp)
@@ -487,11 +558,6 @@ def main() -> int:
     client = ExoClient(args.host, args.port, timeout_s=args.timeout)
     short_id, full_model_id = resolve_model_short_id(client, args.model)
 
-    previews_resp = client.request_json(
-        "GET", "/instance/previews", params={"model_id": full_model_id}
-    )
-    previews = previews_resp.get("previews") or []
-
     tokenizer = load_tokenizer_for_bench(full_model_id)
     if tokenizer is None:
         raise RuntimeError("[exo-bench] tokenizer load failed")
@@ -503,54 +569,20 @@ def main() -> int:
         logger.error("[exo-bench] tokenizer usable but prompt sizing failed")
         raise
 
-    selected: list[dict[str, Any]] = []
-    for p in previews:
-        if p.get("error") is not None:
-            continue
-        if not placement_filter(str(p.get("instance_meta", "")), args.instance_meta):
-            continue
-        if not sharding_filter(str(p.get("sharding", "")), args.sharding):
-            continue
+    selected = fetch_and_filter_placements(client, full_model_id, args)
 
-        instance = p.get("instance")
-        if not isinstance(instance, dict):
-            continue
-
-        n = nodes_used_in_instance(instance)
-        # Skip tensor ring single node as it is pointless when pipeline ring
-        if n == 1 and (
-            (args.sharding == "both" and "tensor" in p.get("sharding", "").lower())
-            or (
-                args.instance_meta == "both"
-                and "jaccl" in p.get("instance_meta", "").lower()
+    if not selected and args.settle_timeout > 0:
+        backoff = _SETTLE_INITIAL_BACKOFF_S
+        deadline = time.monotonic() + args.settle_timeout
+        while not selected and time.monotonic() < deadline:
+            remaining = deadline - time.monotonic()
+            logger.warning(
+                f"No valid placements yet (cluster may still be settling). "
+                f"Retrying in {backoff:.1f}s ({remaining:.0f}s remaining)..."
             )
-        ):
-            continue
-
-        if (
-            args.skip_pipeline_jaccl
-            and (
-                args.instance_meta == "both"
-                and "jaccl" in p.get("instance_meta", "").lower()
-            )
-            and (
-                args.sharding == "both" and "pipeline" in p.get("sharding", "").lower()
-            )
-        ):
-            continue
-
-        if (
-            args.skip_tensor_ring
-            and (
-                args.instance_meta == "both"
-                and "ring" in p.get("instance_meta", "").lower()
-            )
-            and (args.sharding == "both" and "tensor" in p.get("sharding", "").lower())
-        ):
-            continue
-
-        if args.min_nodes <= n <= args.max_nodes:
-            selected.append(p)
+            time.sleep(min(backoff, remaining))
+            backoff = min(backoff * _SETTLE_BACKOFF_MULTIPLIER, _SETTLE_MAX_BACKOFF_S)
+            selected = fetch_and_filter_placements(client, full_model_id, args)
 
     if not selected:
         logger.error("No valid placements matched your filters.")

dashboard/src/lib/components/ChatForm.svelte

@@ -12,6 +12,8 @@
     ttftMs,
     tps,
     totalTokens,
+    thinkingEnabled as thinkingEnabledStore,
+    setConversationThinking,
   } from "$lib/stores/app.svelte";
   import ChatAttachments from "./ChatAttachments.svelte";
   import ImageParamsPanel from "./ImageParamsPanel.svelte";
@@ -25,6 +27,7 @@
     autofocus?: boolean;
     showModelSelector?: boolean;
     modelTasks?: Record<string, string[]>;
+    modelCapabilities?: Record<string, string[]>;
   }
 
   let {
@@ -34,6 +37,7 @@
     autofocus = true,
     showModelSelector = false,
     modelTasks = {},
+    modelCapabilities = {},
   }: Props = $props();
 
   let message = $state("");
@@ -41,6 +45,7 @@
   let fileInputRef: HTMLInputElement | undefined = $state();
   let uploadedFiles = $state<ChatUploadedFile[]>([]);
   let isDragOver = $state(false);
+  const thinkingEnabled = $derived(thinkingEnabledStore());
   let loading = $derived(isLoading());
   const currentModel = $derived(selectedChatModel());
   const instanceData = $derived(instances());
@@ -95,6 +100,12 @@
     );
   });
 
+  const modelSupportsThinking = $derived(() => {
+    if (!currentModel) return false;
+    const caps = modelCapabilities[currentModel] || [];
+    return caps.includes("thinking") && caps.includes("text");
+  });
+
   const isEditOnlyWithoutImage = $derived(
     currentModel !== null &&
       modelSupportsOnlyImageEditing(currentModel) &&
@@ -282,7 +293,11 @@
       // Use image generation for text-to-image models
       generateImage(content);
     } else {
-      sendMessage(content, files);
+      sendMessage(
+        content,
+        files,
+        modelSupportsThinking() ? thinkingEnabled : null,
+      );
     }
 
     // Refocus the textarea after sending
@@ -520,6 +535,35 @@
             </div>
           {/if}
         </div>
+        <!-- Thinking toggle -->
+        {#if modelSupportsThinking()}
+          <button
+            type="button"
+            onclick={() => setConversationThinking(!thinkingEnabled)}
+            class="flex items-center gap-1.5 px-2 py-1 rounded text-xs font-mono tracking-wide transition-all duration-200 flex-shrink-0 cursor-pointer border {thinkingEnabled
+              ? 'bg-exo-yellow/15 border-exo-yellow/40 text-exo-yellow'
+              : 'bg-exo-medium-gray/30 border-exo-medium-gray/50 text-exo-light-gray/60 hover:text-exo-light-gray'}"
+            title={thinkingEnabled
+              ? "Thinking enabled — click to disable"
+              : "Thinking disabled — click to enable"}
+          >
+            <svg
+              class="w-3.5 h-3.5"
+              viewBox="0 0 24 24"
+              fill="none"
+              stroke="currentColor"
+              stroke-width="1.5"
+            >
+              <path
+                d="M12 2a7 7 0 0 0-7 7c0 2.38 1.19 4.47 3 5.74V17a1 1 0 0 0 1 1h6a1 1 0 0 0 1-1v-2.26c1.81-1.27 3-3.36 3-5.74a7 7 0 0 0-7-7zM9 20h6M10 22h4"
+                stroke-linecap="round"
+                stroke-linejoin="round"
+              />
+            </svg>
+            <span>{thinkingEnabled ? "THINK" : "NO THINK"}</span>
+          </button>
+        {/if}
+
         <!-- Performance stats -->
         {#if currentTtft !== null || currentTps !== null}
           <div class="flex items-center gap-4 text-xs font-mono flex-shrink-0">

dashboard/src/lib/components/ModelPickerModal.svelte

@@ -806,6 +806,7 @@
                 isFavorite={favorites.has(group.id)}
                 {selectedModelId}
                 {canModelFit}
+                {getModelFitStatus}
                 onToggleExpand={() => toggleGroupExpanded(group.id)}
                 onSelectModel={handleSelect}
                 {onToggleFavorite}

dashboard/src/lib/components/TopologyGraph.svelte

@@ -7,6 +7,7 @@
     debugMode,
     nodeThunderboltBridge,
     nodeRdmaCtl,
+    nodeIdentities,
     type NodeInfo,
   } from "$lib/stores/app.svelte";
 
@@ -33,6 +34,7 @@
   const debugEnabled = $derived(debugMode());
   const tbBridgeData = $derived(nodeThunderboltBridge());
   const rdmaCtlData = $derived(nodeRdmaCtl());
+  const identitiesData = $derived(nodeIdentities());
 
   function getNodeLabel(nodeId: string): string {
     const node = data?.nodes?.[nodeId];
@@ -1177,6 +1179,22 @@
             .attr("font-size", debugFontSize)
             .attr("font-family", "SF Mono, Monaco, monospace")
             .text(rdmaText);
+          debugLabelY += debugLineHeight;
+        }
+
+        const identity = identitiesData[nodeInfo.id];
+        if (identity?.osVersion) {
+          nodeG
+            .append("text")
+            .attr("x", nodeInfo.x)
+            .attr("y", debugLabelY)
+            .attr("text-anchor", "middle")
+            .attr("fill", "rgba(179,179,179,0.7)")
+            .attr("font-size", debugFontSize)
+            .attr("font-family", "SF Mono, Monaco, monospace")
+            .text(
+              `macOS ${identity.osVersion}${identity.osBuildVersion ? ` (${identity.osBuildVersion})` : ""}`,
+            );
         }
       }
     });

dashboard/src/lib/stores/app.svelte.ts

@@ -296,6 +296,7 @@ export interface Conversation {
   modelId: string | null;
   sharding: string | null;
   instanceType: string | null;
+  enableThinking: boolean | null;
 }
 
 const STORAGE_KEY = "exo-conversations";
@@ -605,6 +606,7 @@ class AppStore {
           modelId: conversation.modelId ?? null,
           sharding: conversation.sharding ?? null,
           instanceType: conversation.instanceType ?? null,
+          enableThinking: conversation.enableThinking ?? null,
         }));
       }
     } catch (error) {
@@ -794,6 +796,7 @@ class AppStore {
       modelId: derivedModelId,
       sharding: derivedSharding,
       instanceType: derivedInstanceType,
+      enableThinking: null,
     };
 
     this.conversations.unshift(conversation);
@@ -819,6 +822,7 @@ class AppStore {
     this.hasStartedChat = true;
     this.isTopologyMinimized = true;
     this.isSidebarOpen = true; // Auto-open sidebar when chatting
+    this.thinkingEnabled = conversation.enableThinking ?? true;
     this.refreshConversationModelFromInstances();
 
     return true;
@@ -1932,6 +1936,11 @@ class AppStore {
     }
   }
 
+  /**
+   * Whether thinking is enabled for the current conversation
+   */
+  thinkingEnabled = $state(true);
+
   /**
    * Selected model for chat (can be set by the UI)
    */
@@ -2110,6 +2119,7 @@ class AppStore {
       textContent?: string;
       preview?: string;
     }[],
+    enableThinking?: boolean | null,
   ): Promise<void> {
     if ((!content.trim() && (!files || files.length === 0)) || this.isLoading)
       return;
@@ -2257,6 +2267,9 @@ class AppStore {
           stream: true,
           logprobs: true,
           top_logprobs: 5,
+          ...(enableThinking != null && {
+            enable_thinking: enableThinking,
+          }),
         }),
       });
 
@@ -2915,6 +2928,18 @@ class AppStore {
     );
   }
 
+  /**
+   * Update the thinking preference for the active conversation
+   */
+  setConversationThinking(enabled: boolean) {
+    this.thinkingEnabled = enabled;
+    const conv = this.getActiveConversation();
+    if (conv) {
+      conv.enableThinking = enabled;
+      this.saveConversationsToStorage();
+    }
+  }
+
   /**
    * Start a download on a specific node
    */
@@ -3028,6 +3053,7 @@ export const isLoadingPreviews = () => appStore.isLoadingPreviews;
 export const lastUpdate = () => appStore.lastUpdate;
 export const isTopologyMinimized = () => appStore.isTopologyMinimized;
 export const selectedChatModel = () => appStore.selectedChatModel;
+export const thinkingEnabled = () => appStore.thinkingEnabled;
 export const debugMode = () => appStore.getDebugMode();
 export const topologyOnlyMode = () => appStore.getTopologyOnlyMode();
 export const chatSidebarVisible = () => appStore.getChatSidebarVisible();
@@ -3043,7 +3069,8 @@ export const sendMessage = (
     textContent?: string;
     preview?: string;
   }[],
-) => appStore.sendMessage(content, files);
+  enableThinking?: boolean | null,
+) => appStore.sendMessage(content, files, enableThinking);
 export const generateImage = (prompt: string, modelId?: string) =>
   appStore.generateImage(prompt, modelId);
 export const editImage = (
@@ -3086,6 +3113,8 @@ export const deleteAllConversations = () => appStore.deleteAllConversations();
 export const renameConversation = (id: string, name: string) =>
   appStore.renameConversation(id, name);
 export const getActiveConversation = () => appStore.getActiveConversation();
+export const setConversationThinking = (enabled: boolean) =>
+  appStore.setConversationThinking(enabled);
 
 // Sidebar actions
 export const isSidebarOpen = () => appStore.isSidebarOpen;

dashboard/src/routes/+page.svelte

@@ -190,6 +190,19 @@
     return tasks;
   });
 
+  const modelCapabilities = $derived(() => {
+    const caps: Record<string, string[]> = {};
+    for (const model of models) {
+      if (model.capabilities && model.capabilities.length > 0) {
+        caps[model.id] = model.capabilities;
+        if (model.hugging_face_id) {
+          caps[model.hugging_face_id] = model.capabilities;
+        }
+      }
+    }
+    return caps;
+  });
+
   // Helper to check if a model supports image generation
   function modelSupportsImageGeneration(modelId: string): boolean {
     const model = models.find(
@@ -2270,6 +2283,7 @@
                 showHelperText={false}
                 showModelSelector={true}
                 modelTasks={modelTasks()}
+                modelCapabilities={modelCapabilities()}
               />
             </div>
           </div>
@@ -3049,6 +3063,7 @@
                 placeholder="Ask anything"
                 showModelSelector={true}
                 modelTasks={modelTasks()}
+                modelCapabilities={modelCapabilities()}
               />
             </div>
           </div>

resources/inference_model_cards/mlx-community--Step-3.5-Flash-4bit.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/Step-3.5-Flash-4bit"
+n_layers = 45
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "step"
+quantization = "4bit"
+base_model = "Step 3.5 Flash"
+capabilities = ["text", "thinking"]
+
+[storage_size]
+in_bytes = 114572190076

resources/inference_model_cards/mlx-community--Step-3.5-Flash-6bit.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/Step-3.5-Flash-6bit"
+n_layers = 45
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "step"
+quantization = "6bit"
+base_model = "Step 3.5 Flash"
+capabilities = ["text", "thinking"]
+
+[storage_size]
+in_bytes = 159039627774

resources/inference_model_cards/mlx-community--Step-3.5-Flash-8Bit.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/Step-3.5-Flash-8Bit"
+n_layers = 45
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "step"
+quantization = "8bit"
+base_model = "Step 3.5 Flash"
+capabilities = ["text", "thinking"]
+
+[storage_size]
+in_bytes = 209082699847

src/exo/master/adapters/chat_completions.py

@@ -79,6 +79,7 @@ def chat_request_to_text_generation(
         seed=request.seed,
         stream=request.stream,
         tools=request.tools,
+        enable_thinking=request.enable_thinking,
         chat_template_messages=chat_template_messages
         if chat_template_messages
         else None,

src/exo/shared/models/model_cards.py

@@ -189,6 +189,7 @@ class ConfigData(BaseModel):
             ["MiniMaxM2ForCausalLM"],
             ["LlamaForCausalLM"],
             ["GptOssForCausalLM"],
+            ["Step3p5ForCausalLM"],
         ]
 
     @model_validator(mode="before")

src/exo/shared/types/api.py

@@ -199,6 +199,7 @@ class ChatCompletionRequest(BaseModel):
     top_p: float | None = None
     top_k: int | None = None
     tools: list[dict[str, Any]] | None = None
+    enable_thinking: bool | None = None
     tool_choice: str | dict[str, Any] | None = None
     parallel_tool_calls: bool | None = None
     user: str | None = None

src/exo/shared/types/text_generation.py

@@ -40,5 +40,6 @@ class TextGenerationTaskParams(BaseModel, frozen=True):
     stop: str | list[str] | None = None
     seed: int | None = None
     chat_template_messages: list[dict[str, Any]] | None = None
+    enable_thinking: bool | None = None
     logprobs: bool = False
     top_logprobs: int | None = None

src/exo/utils/info_gatherer/info_gatherer.py

@@ -388,6 +388,10 @@ class InfoGatherer:
             if IS_DARWIN:
                 if (macmon_path := shutil.which("macmon")) is not None:
                     tg.start_soon(self._monitor_macmon, macmon_path)
+                else:
+                    # macmon not installed — fall back to psutil for memory
+                    logger.warning("macmon not found, falling back to psutil for memory monitoring")
+                    self.memory_poll_rate = 1
                 tg.start_soon(self._monitor_system_profiler_thunderbolt_data)
                 tg.start_soon(self._monitor_thunderbolt_bridge_status)
                 tg.start_soon(self._monitor_rdma_ctl_status)

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

@@ -35,6 +35,9 @@ from mlx_lm.models.qwen3_moe import Model as Qwen3MoeModel
 from mlx_lm.models.qwen3_moe import Qwen3MoeSparseMoeBlock
 from mlx_lm.models.qwen3_next import Model as Qwen3NextModel
 from mlx_lm.models.qwen3_next import Qwen3NextDecoderLayer, Qwen3NextSparseMoeBlock
+from mlx_lm.models.step3p5 import Model as Step35Model
+from mlx_lm.models.step3p5 import Step3p5MLP as Step35MLP
+from mlx_lm.models.step3p5 import Step3p5Model as Step35InnerModel
 from transformers.models.qwen3.modeling_qwen3 import Qwen3DecoderLayer
 
 from exo.shared.logging import logger
@@ -264,6 +267,19 @@ def pipeline_auto_parallel(
             )
         )
 
+    if isinstance(inner_model_instance, Step35InnerModel):
+        inner_model_instance.num_layers = len(layers)
+        sliding_layers = [
+            i for i, layer in enumerate(layers) if getattr(layer, "is_sliding", False)
+        ]
+        full_layers = [
+            i
+            for i, layer in enumerate(layers)
+            if not getattr(layer, "is_sliding", True)
+        ]
+        inner_model_instance._swa_idx = 0 if not sliding_layers else sliding_layers[0]
+        inner_model_instance._full_idx = 0 if not full_layers else full_layers[0]
+
     _set_layers(model, layers)
 
     assert isinstance(layers, list), (
@@ -427,6 +443,14 @@ def tensor_auto_parallel(
             all_to_sharded_linear_in_place,
             sharded_to_all_linear_in_place,
         )
+    elif isinstance(model, Step35Model):
+        tensor_parallel_sharding_strategy = Step35ShardingStrategy(
+            group,
+            all_to_sharded_linear,
+            sharded_to_all_linear,
+            all_to_sharded_linear_in_place,
+            sharded_to_all_linear_in_place,
+        )
     else:
         raise ValueError(f"Unsupported model type: {type(model)}")
 
@@ -981,3 +1005,46 @@ class GptOssShardingStrategy(TensorParallelShardingStrategy):
             layer.mlp.sharding_group = self.group  # pyright: ignore[reportAttributeAccessIssue]
             mx.eval(layer)
         return model
+
+
+class Step35ShardingStrategy(TensorParallelShardingStrategy):
+    def shard_model(
+        self,
+        model: nn.Module,
+        timeout_seconds: float,
+        on_timeout: TimeoutCallback | None,
+    ) -> nn.Module:
+        model = cast(Step35Model, model)
+
+        for layer in model.layers:
+            eval_with_timeout(
+                layer.parameters(), timeout_seconds / len(model.layers), on_timeout
+            )
+            layer.self_attn.q_proj = self.all_to_sharded_linear(layer.self_attn.q_proj)
+            layer.self_attn.k_proj = self.all_to_sharded_linear(layer.self_attn.k_proj)
+            layer.self_attn.v_proj = self.all_to_sharded_linear(layer.self_attn.v_proj)
+            layer.self_attn.o_proj = self.sharded_to_all_linear(layer.self_attn.o_proj)
+
+            layer.self_attn.num_heads //= self.N
+            layer.self_attn.num_kv_heads //= self.N
+
+            if getattr(layer.self_attn, "use_head_wise_attn_gate", False):
+                layer.self_attn.g_proj = self.all_to_sharded_linear(
+                    layer.self_attn.g_proj
+                )
+
+            if isinstance(layer.mlp, Step35MLP):
+                layer.mlp.gate_proj = self.all_to_sharded_linear(layer.mlp.gate_proj)
+                layer.mlp.up_proj = self.all_to_sharded_linear(layer.mlp.up_proj)
+                layer.mlp.down_proj = self.sharded_to_all_linear(layer.mlp.down_proj)
+            else:
+                layer.mlp.sharding_group = self.group
+                self.all_to_sharded_linear_in_place(layer.mlp.share_expert.gate_proj)
+                self.all_to_sharded_linear_in_place(layer.mlp.share_expert.up_proj)
+                self.sharded_to_all_linear_in_place(layer.mlp.share_expert.down_proj)
+                self.all_to_sharded_linear_in_place(layer.mlp.switch_mlp.gate_proj)
+                self.all_to_sharded_linear_in_place(layer.mlp.switch_mlp.up_proj)
+                self.sharded_to_all_linear_in_place(layer.mlp.switch_mlp.down_proj)
+
+            mx.eval(layer)
+        return model

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

@@ -462,11 +462,19 @@ def apply_chat_template(
         partial_assistant_content = cast(str, formatted_messages[-1].get("content", ""))
         formatted_messages = formatted_messages[:-1]
 
+    extra_kwargs: dict[str, Any] = {}
+    if task_params.enable_thinking is not None:
+        # Qwen3 and GLM use "enable_thinking"; DeepSeek uses "thinking".
+        # Jinja ignores unknown variables, so passing both is safe.
+        extra_kwargs["enable_thinking"] = task_params.enable_thinking
+        extra_kwargs["thinking"] = task_params.enable_thinking
+
     prompt: str = tokenizer.apply_chat_template(
         formatted_messages,
         tokenize=False,
         add_generation_prompt=True,
         tools=task_params.tools,
+        **extra_kwargs,
     )
 
     if partial_assistant_content: