dashboard: show macOS version in debug mode (#1454)

## Motivation

When debugging cluster issues, it's useful to see which macOS version
each node is running — especially since version mismatches can cause
compatibility problems. The OS version data is already collected by the
identity gatherer but wasn't shown in the topology graph.

## Changes

- Added OS version label (e.g. "macOS 15.2") to each node in the
topology graph when debug mode is enabled
- Renders below the existing TB and RDMA debug labels using the same
styling conventions
- Sources data from the existing `nodeIdentities` store (no backend
changes needed)

## Why It Works

The `nodeIdentities` store already contains `osVersion` for each node.
We simply read it in the `TopologyGraph` component and append a text
label in the debug section, following the exact same pattern as the TB
and RDMA labels.

## Test Plan

### Manual Testing
<!-- Hardware: MacBook Pro -->
- Enable debug mode in the dashboard
- Verify OS version label appears below TB/RDMA labels on each node
- Verify label disappears when debug mode is disabled

### Automated Testing
- Dashboard build passes (`npm run build`)

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: rltakashige <rl.takashige@gmail.com>
Co-authored-by: Ryuichi Leo Takashige <leo@exolabs.net>

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/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>

pyproject.toml

@@ -17,7 +17,7 @@ dependencies = [
     "loguru>=0.7.3",
     "exo_pyo3_bindings", # rust bindings
     "anyio==4.11.0",
-    "mlx; sys_platform == 'darwin'",
+    "mlx==0.30.6; sys_platform == 'darwin'",
     "mlx[cpu]==0.30.6; sys_platform == 'linux'",
     "mlx-lm==0.30.6",
     "tiktoken>=0.12.0", # required for kimi k2 tokenizer
@@ -64,7 +64,6 @@ members = [
 
 [tool.uv.sources]
 exo_pyo3_bindings = { workspace = true }
-mlx = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git", marker = "sys_platform == 'darwin'" }
 #mlx-lm = { git = "https://github.com/davidmcc73/mlx-lm", branch = "stable" }
 # Uncomment to use local mlx/mlx-lm development versions:
 # mlx = { path = "/Users/Shared/mlx", editable=true }

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/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/channels.py

@@ -141,9 +141,6 @@ class MpSender[T]:
         )
         self._state.buffer.join_thread()
 
-    def cancel_join(self) -> None:
-        self._state.buffer.cancel_join_thread()
-
     # == context manager support ==
     def __enter__(self) -> Self:
         return self
@@ -215,9 +212,6 @@ class MpReceiver[T]:
         )
         self._state.buffer.join_thread()
 
-    def cancel_join(self) -> None:
-        self._state.buffer.cancel_join_thread()
-
     # == iterator support ==
     def __iter__(self) -> Self:
         return self

src/exo/worker/engines/mlx/generator/generate.py

@@ -57,7 +57,6 @@ def prefill(
     sampler: Callable[[mx.array], mx.array],
     prompt_tokens: mx.array,
     cache: KVCacheType,
-    group: mx.distributed.Group | None = None,
 ) -> tuple[float, int, list[CacheSnapshot]]:
     """Prefill the KV cache with prompt tokens.
 
@@ -87,9 +86,6 @@ def prefill(
 
     set_pipeline_prefill(model, is_prefill=True)
 
-    mx_barrier(group)
-    logger.info("Ready to prefill")
-
     # Use max_tokens=1 because max_tokens=0 does not work.
     # We just throw away the generated token - we only care about filling the cache
     for _ in stream_generate(
@@ -309,9 +305,16 @@ def mlx_generate(
     )
     max_stop_len = max((len(s) for s in stop_sequences), default=0)
 
+    mx_barrier(group)
+    logger.info("Ready to prefill")
+
     # Prefill cache with all tokens except the last one
     prefill_tps, prefill_tokens, ssm_snapshots_list = prefill(
-        model, tokenizer, sampler, prompt_tokens[:-1], caches, group
+        model,
+        tokenizer,
+        sampler,
+        prompt_tokens[:-1],
+        caches,
     )
     cache_snapshots: list[CacheSnapshot] | None = ssm_snapshots_list or None
 

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:

src/exo/worker/runner/bootstrap.py

@@ -41,8 +41,6 @@ def entrypoint(
         main(bound_instance, event_sender, task_receiver)
     except ClosedResourceError:
         logger.warning("Runner communication closed unexpectedly")
-    except KeyboardInterrupt:
-        logger.info("Runner received interrupt, shutting down")
     except Exception as e:
         logger.opt(exception=e).warning(
             f"Runner {bound_instance.bound_runner_id} crashed with critical exception {e}"
@@ -57,9 +55,7 @@ def entrypoint(
         try:
             event_sender.close()
             task_receiver.close()
-        except Exception:
-            pass
         finally:
-            event_sender.cancel_join()
-            task_receiver.cancel_join()
+            event_sender.join()
+            task_receiver.join()
             logger.info("bye from the runner")

uv.lock

@@ -377,8 +377,8 @@ dependencies = [
     { name = "hypercorn", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "loguru", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "mflux", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
-    { name = "mlx", version = "0.30.6", source = { registry = "https://pypi.org/simple" }, extra = ["cpu"], marker = "sys_platform == 'linux'" },
-    { name = "mlx", version = "0.30.7.dev20260211+80de5e35", source = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git#80de5e35498d6ae759573225177482f0ca9ded80" }, marker = "sys_platform == 'darwin'" },
+    { name = "mlx", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
+    { name = "mlx", extra = ["cpu"], marker = "sys_platform == 'linux'" },
     { name = "mlx-lm", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "msgspec", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "openai-harmony", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
@@ -416,7 +416,7 @@ requires-dist = [
     { name = "hypercorn", specifier = ">=0.18.0" },
     { name = "loguru", specifier = ">=0.7.3" },
     { name = "mflux", specifier = "==0.15.5" },
-    { name = "mlx", marker = "sys_platform == 'darwin'", git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git" },
+    { name = "mlx", marker = "sys_platform == 'darwin'", specifier = "==0.30.6" },
     { name = "mlx", extras = ["cpu"], marker = "sys_platform == 'linux'", specifier = "==0.30.6" },
     { name = "mlx-lm", specifier = "==0.30.6" },
     { name = "msgspec", specifier = ">=0.19.0" },
@@ -1020,8 +1020,8 @@ dependencies = [
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+    { name = "mlx", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
+    { name = "mlx", extra = ["cuda13"], marker = "sys_platform == 'linux'" },
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 version = "0.30.6"
 source = { registry = "https://pypi.org/simple" }
-resolution-markers = [
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 ]
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@@ -1066,14 +1072,6 @@ cuda13 = [
     { name = "mlx-cuda-13", marker = "sys_platform == 'linux'" },
 ]
 
-[[package]]
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-version = "0.30.7.dev20260211+80de5e35"
-source = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git#80de5e35498d6ae759573225177482f0ca9ded80" }
-resolution-markers = [
-    "sys_platform == 'darwin'",
-]
-
 [[package]]
 name = "mlx-cpu"
 version = "0.30.6"
@@ -1104,7 +1102,7 @@ version = "0.30.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jinja2", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
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+    { name = "mlx", marker = "sys_platform == 'darwin'" },
     { name = "numpy", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "protobuf", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
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 ]
 
+[[package]]
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+version = "0.30.6"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
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+]
+
 [[package]]
 name = "more-itertools"
 version = "10.8.0"