better i thikn

.github/actions/typecheck/action.yml

@@ -0,0 +1,12 @@
+name: Type Check
+
+description: "Run type checker"
+
+runs:
+  using: "composite"
+  steps:
+    - name: Run type checker
+      run: |
+        nix --extra-experimental-features nix-command --extra-experimental-features flakes develop -c just sync
+        nix --extra-experimental-features nix-command --extra-experimental-features flakes develop -c just check
+      shell: bash

.github/workflows/pipeline.yml

@@ -26,14 +26,73 @@ jobs:
           name: exo
           authToken: "${{ secrets.CACHIX_AUTH_TOKEN }}"
 
-      - name: Load nix develop environment
-        run: nix run github:nicknovitski/nix-develop/v1
+      - name: Configure git user
+        run: |
+          git config --local user.email "github-actions@users.noreply.github.com"
+          git config --local user.name  "github-actions bot"
+        shell: bash
 
-      - name: Sync dependencies
-        run: uv sync --all-packages
+      - name: Pull LFS files
+        run: |
+          echo "Pulling Git LFS files..."
+          git lfs pull
+        shell: bash
 
-      - name: Run type checker
-        run: uv run basedpyright --project pyproject.toml
+      - name: Setup Nix Environment
+        run: |
+          echo "Checking for nix installation..."
+          
+          # Check if nix binary exists directly
+          if [ -f /nix/var/nix/profiles/default/bin/nix ]; then
+            echo "Found nix binary at /nix/var/nix/profiles/default/bin/nix"
+            export PATH="/nix/var/nix/profiles/default/bin:$PATH"
+            echo "PATH=$PATH" >> $GITHUB_ENV
+            nix --version
+          elif [ -f /nix/var/nix/profiles/default/etc/profile.d/nix-daemon.sh ]; then
+            echo "Found nix profile script, sourcing..."
+            source /nix/var/nix/profiles/default/etc/profile.d/nix-daemon.sh
+            nix --version
+          elif command -v nix >/dev/null 2>&1; then
+            echo "Nix already in PATH"
+            nix --version
+          else
+            echo "Nix not found. Debugging info:"
+            echo "Contents of /nix/var/nix/profiles/default/:"
+            ls -la /nix/var/nix/profiles/default/ 2>/dev/null || echo "Directory not found"
+            echo "Contents of /nix/var/nix/profiles/default/bin/:"
+            ls -la /nix/var/nix/profiles/default/bin/ 2>/dev/null || echo "Directory not found"
+            exit 1
+          fi
+        shell: bash
+
+      - name: Configure basedpyright include for local MLX
+        run: |
+          RUNNER_LABELS='${{ toJSON(runner.labels) }}'
+          if echo "$RUNNER_LABELS" | grep -q "local_mlx"; then
+            if [ -d "/Users/Shared/mlx" ]; then
+              echo "Updating [tool.basedpyright].include to use /Users/Shared/mlx"
+              awk '
+                BEGIN { in=0 }
+                /^\[tool\.basedpyright\]/ { in=1; print; next }
+                in && /^\[/ { in=0 }  # next section
+                in && /^[ \t]*include[ \t]*=/ {
+                  print "include = [\"/Users/Shared/mlx\"]"
+                  next
+                }
+                { print }
+              ' pyproject.toml > pyproject.toml.tmp && mv pyproject.toml.tmp pyproject.toml
+
+              echo "New [tool.basedpyright] section:"
+              sed -n '/^\[tool\.basedpyright\]/,/^\[/p' pyproject.toml | sed '$d' || true
+            else
+              echo "local_mlx tag present but /Users/Shared/mlx not found; leaving pyproject unchanged."
+            fi
+          else
+            echo "Runner does not have 'local_mlx' tag; leaving pyproject unchanged."
+          fi
+        shell: bash
+
+      - uses: ./.github/actions/typecheck
 
   nix:
     name: Build and check (${{ matrix.system }})
@@ -132,14 +191,3 @@ jobs:
 
       - name: Run nix flake check
         run: nix flake check
-
-      - name: Run pytest (macOS only)
-        if: runner.os == 'macOS'
-        run: |
-          # Build the test environment (requires relaxed sandbox for uv2nix on macOS)
-          TEST_ENV=$(nix build '.#exo-test-env' --option sandbox relaxed --print-out-paths)
-
-          # Run pytest outside sandbox (needs GPU access for MLX)
-          export HOME="$RUNNER_TEMP"
-          export EXO_TESTS=1
-          $TEST_ENV/bin/python -m pytest src -m "not slow" --import-mode=importlib

app/EXO/EXO.xcodeproj/project.pbxproj

@@ -342,8 +342,6 @@
 				SDKROOT = macosx;
 				SWIFT_ACTIVE_COMPILATION_CONDITIONS = "DEBUG $(inherited)";
 				SWIFT_OPTIMIZATION_LEVEL = "-Onone";
-				SWIFT_TREAT_WARNINGS_AS_ERRORS = YES;
-				GCC_TREAT_WARNINGS_AS_ERRORS = YES;
 			};
 			name = Debug;
 		};
@@ -399,8 +397,6 @@
 				MTL_FAST_MATH = YES;
 				SDKROOT = macosx;
 				SWIFT_COMPILATION_MODE = wholemodule;
-				SWIFT_TREAT_WARNINGS_AS_ERRORS = YES;
-				GCC_TREAT_WARNINGS_AS_ERRORS = YES;
 			};
 			name = Release;
 		};

dashboard/package-lock.json

@@ -865,6 +865,7 @@
 			"integrity": "sha512-oH8tXw7EZnie8FdOWYrF7Yn4IKrqTFHhXvl8YxXxbKwTMcD/5NNCryUSEXRk2ZR4ojnub0P8rNrsVGHXWqIDtA==",
 			"dev": true,
 			"license": "MIT",
+			"peer": true,
 			"dependencies": {
 				"@standard-schema/spec": "^1.0.0",
 				"@sveltejs/acorn-typescript": "^1.0.5",
@@ -904,6 +905,7 @@
 			"integrity": "sha512-Y1Cs7hhTc+a5E9Va/xwKlAJoariQyHY+5zBgCZg4PFWNYQ1nMN9sjK1zhw1gK69DuqVP++sht/1GZg1aRwmAXQ==",
 			"dev": true,
 			"license": "MIT",
+			"peer": true,
 			"dependencies": {
 				"@sveltejs/vite-plugin-svelte-inspector": "^4.0.1",
 				"debug": "^4.4.1",
@@ -1520,6 +1522,7 @@
 			"integrity": "sha512-LCCV0HdSZZZb34qifBsyWlUmok6W7ouER+oQIGBScS8EsZsQbrtFTUrDX4hOl+CS6p7cnNC4td+qrSVGSCTUfQ==",
 			"dev": true,
 			"license": "MIT",
+			"peer": true,
 			"dependencies": {
 				"undici-types": "~6.21.0"
 			}
@@ -1529,6 +1532,7 @@
 			"resolved": "https://registry.npmjs.org/acorn/-/acorn-8.15.0.tgz",
 			"integrity": "sha512-NZyJarBfL7nWwIq+FDL6Zp/yHEhePMNnnJ0y3qfieCrmNvYct8uvtiV41UvlSe6apAfk0fY1FbWx+NwfmpvtTg==",
 			"license": "MIT",
+			"peer": true,
 			"bin": {
 				"acorn": "bin/acorn"
 			},
@@ -1941,6 +1945,7 @@
 			"integrity": "sha512-fmTRWbNMmsmWq6xJV8D19U/gw/bwrHfNXxrIN+HfZgnzqTHp9jOmKMhsTUjXOJnZOdZY9Q28y4yebKzqDKlxlQ==",
 			"dev": true,
 			"license": "ISC",
+			"peer": true,
 			"engines": {
 				"node": ">=12"
 			}
@@ -2648,6 +2653,7 @@
 			"integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==",
 			"dev": true,
 			"license": "MIT",
+			"peer": true,
 			"engines": {
 				"node": ">=12"
 			},
@@ -2690,6 +2696,7 @@
 			"integrity": "sha512-UOnG6LftzbdaHZcKoPFtOcCKztrQ57WkHDeRD9t/PTQtmT0NHSeWWepj6pS0z/N7+08BHFDQVUrfmfMRcZwbMg==",
 			"dev": true,
 			"license": "MIT",
+			"peer": true,
 			"bin": {
 				"prettier": "bin/prettier.cjs"
 			},
@@ -2862,6 +2869,7 @@
 			"resolved": "https://registry.npmjs.org/svelte/-/svelte-5.45.3.tgz",
 			"integrity": "sha512-ngKXNhNvwPzF43QqEhDOue7TQTrG09em1sd4HBxVF0Wr2gopAmdEWan+rgbdgK4fhBtSOTJO8bYU4chUG7VXZQ==",
 			"license": "MIT",
+			"peer": true,
 			"dependencies": {
 				"@jridgewell/remapping": "^2.3.4",
 				"@jridgewell/sourcemap-codec": "^1.5.0",
@@ -3006,6 +3014,7 @@
 			"integrity": "sha512-jl1vZzPDinLr9eUt3J/t7V6FgNEw9QjvBPdysz9KfQDD41fQrC2Y4vKQdiaUpFT4bXlb1RHhLpp8wtm6M5TgSw==",
 			"dev": true,
 			"license": "Apache-2.0",
+			"peer": true,
 			"bin": {
 				"tsc": "bin/tsc",
 				"tsserver": "bin/tsserver"
@@ -3027,6 +3036,7 @@
 			"integrity": "sha512-+Oxm7q9hDoLMyJOYfUYBuHQo+dkAloi33apOPP56pzj+vsdJDzr+j1NISE5pyaAuKL4A3UD34qd0lx5+kfKp2g==",
 			"dev": true,
 			"license": "MIT",
+			"peer": true,
 			"dependencies": {
 				"esbuild": "^0.25.0",
 				"fdir": "^6.4.4",

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

@@ -173,11 +173,6 @@ export interface PlacementPreviewResponse {
   previews: PlacementPreview[];
 }
 
-interface ImageApiResponse {
-  created: number;
-  data: Array<{ b64_json?: string; url?: string }>;
-}
-
 interface RawStateResponse {
   topology?: RawTopology;
   instances?: Record<
@@ -2100,137 +2095,107 @@ class AppStore {
         throw new Error(`API error: ${response.status} - ${errorText}`);
       }
 
-      // Streaming requires both stream=true AND partialImages > 0
-      const isStreaming = params.stream && params.partialImages > 0;
-
-      if (!isStreaming) {
-        // Non-streaming: parse JSON response directly
-        const jsonResponse = (await response.json()) as ImageApiResponse;
-        const format = params.outputFormat || "png";
-        const mimeType = `image/${format}`;
-
-        const attachments: MessageAttachment[] = jsonResponse.data
-          .filter((img) => img.b64_json)
-          .map((img, index) => ({
-            type: "generated-image" as const,
-            name: `generated-image-${index + 1}.${format}`,
-            preview: `data:${mimeType};base64,${img.b64_json}`,
-            mimeType,
-          }));
-
-        this.updateConversationMessage(
-          targetConversationId,
-          assistantMessage.id,
-          (msg) => {
-            msg.content = "";
-            msg.attachments = attachments;
-          },
-        );
-        this.syncActiveMessagesIfNeeded(targetConversationId);
-      } else {
-        // Streaming mode: use SSE parser
-        const reader = response.body?.getReader();
-        if (!reader) {
-          throw new Error("No response body");
-        }
-
-        interface ImageGenerationChunk {
-          data?: { b64_json?: string };
-          format?: string;
-          type?: "partial" | "final";
-          image_index?: number;
-          partial_index?: number;
-          total_partials?: number;
-        }
-
-        const numImages = params.numImages;
-
-        await this.parseSSEStream<ImageGenerationChunk>(
-          reader,
-          targetConversationId,
-          (parsed) => {
-            const imageData = parsed.data?.b64_json;
-
-            if (imageData) {
-              const format = parsed.format || "png";
-              const mimeType = `image/${format}`;
-              const imageIndex = parsed.image_index ?? 0;
-
-              if (parsed.type === "partial") {
-                // Update with partial image and progress
-                const partialNum = (parsed.partial_index ?? 0) + 1;
-                const totalPartials = parsed.total_partials ?? 3;
-                const progressText =
-                  numImages > 1
-                    ? `Generating image ${imageIndex + 1}/${numImages}... ${partialNum}/${totalPartials}`
-                    : `Generating... ${partialNum}/${totalPartials}`;
-
-                const partialAttachment: MessageAttachment = {
-                  type: "generated-image",
-                  name: `generated-image.${format}`,
-                  preview: `data:${mimeType};base64,${imageData}`,
-                  mimeType,
-                };
-
-                this.updateConversationMessage(
-                  targetConversationId,
-                  assistantMessage.id,
-                  (msg) => {
-                    msg.content = progressText;
-                    if (imageIndex === 0) {
-                      // First image - safe to replace attachments with partial preview
-                      msg.attachments = [partialAttachment];
-                    } else {
-                      // Subsequent images - keep existing finals, show partial at current position
-                      const existingAttachments = msg.attachments || [];
-                      // Keep only the completed final images (up to current imageIndex)
-                      const finals = existingAttachments.slice(0, imageIndex);
-                      msg.attachments = [...finals, partialAttachment];
-                    }
-                  },
-                );
-              } else if (parsed.type === "final") {
-                // Final image - replace partial at this position
-                const newAttachment: MessageAttachment = {
-                  type: "generated-image",
-                  name: `generated-image-${imageIndex + 1}.${format}`,
-                  preview: `data:${mimeType};base64,${imageData}`,
-                  mimeType,
-                };
-
-                this.updateConversationMessage(
-                  targetConversationId,
-                  assistantMessage.id,
-                  (msg) => {
-                    if (imageIndex === 0) {
-                      // First final image - replace any partial preview
-                      msg.attachments = [newAttachment];
-                    } else {
-                      // Subsequent images - keep previous finals, replace partial at current position
-                      const existingAttachments = msg.attachments || [];
-                      // Slice keeps indices 0 to imageIndex-1 (the previous final images)
-                      const previousFinals = existingAttachments.slice(
-                        0,
-                        imageIndex,
-                      );
-                      msg.attachments = [...previousFinals, newAttachment];
-                    }
-
-                    // Update progress message for multiple images
-                    if (numImages > 1 && imageIndex < numImages - 1) {
-                      msg.content = `Generating image ${imageIndex + 2}/${numImages}...`;
-                    } else {
-                      msg.content = "";
-                    }
-                  },
-                );
-              }
-
-              this.syncActiveMessagesIfNeeded(targetConversationId);
-            }
-          },
-        );
+      const reader = response.body?.getReader();
+      if (!reader) {
+        throw new Error("No response body");
       }
+
+      interface ImageGenerationChunk {
+        data?: { b64_json?: string };
+        format?: string;
+        type?: "partial" | "final";
+        image_index?: number;
+        partial_index?: number;
+        total_partials?: number;
+      }
+
+      const numImages = params.numImages;
+
+      await this.parseSSEStream<ImageGenerationChunk>(
+        reader,
+        targetConversationId,
+        (parsed) => {
+          const imageData = parsed.data?.b64_json;
+
+          if (imageData) {
+            const format = parsed.format || "png";
+            const mimeType = `image/${format}`;
+            const imageIndex = parsed.image_index ?? 0;
+
+            if (parsed.type === "partial") {
+              // Update with partial image and progress
+              const partialNum = (parsed.partial_index ?? 0) + 1;
+              const totalPartials = parsed.total_partials ?? 3;
+              const progressText =
+                numImages > 1
+                  ? `Generating image ${imageIndex + 1}/${numImages}... ${partialNum}/${totalPartials}`
+                  : `Generating... ${partialNum}/${totalPartials}`;
+
+              const partialAttachment: MessageAttachment = {
+                type: "generated-image",
+                name: `generated-image.${format}`,
+                preview: `data:${mimeType};base64,${imageData}`,
+                mimeType,
+              };
+
+              this.updateConversationMessage(
+                targetConversationId,
+                assistantMessage.id,
+                (msg) => {
+                  msg.content = progressText;
+                  if (imageIndex === 0) {
+                    // First image - safe to replace attachments with partial preview
+                    msg.attachments = [partialAttachment];
+                  } else {
+                    // Subsequent images - keep existing finals, show partial at current position
+                    const existingAttachments = msg.attachments || [];
+                    // Keep only the completed final images (up to current imageIndex)
+                    const finals = existingAttachments.slice(0, imageIndex);
+                    msg.attachments = [...finals, partialAttachment];
+                  }
+                },
+              );
+            } else if (parsed.type === "final") {
+              // Final image - replace partial at this position
+              const newAttachment: MessageAttachment = {
+                type: "generated-image",
+                name: `generated-image-${imageIndex + 1}.${format}`,
+                preview: `data:${mimeType};base64,${imageData}`,
+                mimeType,
+              };
+
+              this.updateConversationMessage(
+                targetConversationId,
+                assistantMessage.id,
+                (msg) => {
+                  if (imageIndex === 0) {
+                    // First final image - replace any partial preview
+                    msg.attachments = [newAttachment];
+                  } else {
+                    // Subsequent images - keep previous finals, replace partial at current position
+                    const existingAttachments = msg.attachments || [];
+                    // Slice keeps indices 0 to imageIndex-1 (the previous final images)
+                    const previousFinals = existingAttachments.slice(
+                      0,
+                      imageIndex,
+                    );
+                    msg.attachments = [...previousFinals, newAttachment];
+                  }
+
+                  // Update progress message for multiple images
+                  if (numImages > 1 && imageIndex < numImages - 1) {
+                    msg.content = `Generating image ${imageIndex + 2}/${numImages}...`;
+                  } else {
+                    msg.content = "";
+                  }
+                },
+              );
+            }
+
+            this.syncActiveMessagesIfNeeded(targetConversationId);
+          }
+        },
+      );
     } catch (error) {
       console.error("Error generating image:", error);
       this.handleStreamingError(
@@ -2378,98 +2343,69 @@ class AppStore {
         throw new Error(`API error: ${apiResponse.status} - ${errorText}`);
       }
 
-      // Streaming requires both stream=true AND partialImages > 0
-      const isStreaming = params.stream && params.partialImages > 0;
-
-      if (!isStreaming) {
-        // Non-streaming: parse JSON response directly
-        const jsonResponse = (await apiResponse.json()) as ImageApiResponse;
-        const format = params.outputFormat || "png";
-        const mimeType = `image/${format}`;
-        const attachments: MessageAttachment[] = jsonResponse.data
-          .filter((img) => img.b64_json)
-          .map((img) => ({
-            type: "generated-image" as const,
-            name: `edited-image.${format}`,
-            preview: `data:${mimeType};base64,${img.b64_json}`,
-            mimeType,
-          }));
-
-        this.updateConversationMessage(
-          targetConversationId,
-          assistantMessage.id,
-          (msg) => {
-            msg.content = "";
-            msg.attachments = attachments;
-          },
-        );
-        this.syncActiveMessagesIfNeeded(targetConversationId);
-      } else {
-        // Streaming mode: use SSE parser
-        const reader = apiResponse.body?.getReader();
-        if (!reader) {
-          throw new Error("No response body");
-        }
-
-        interface ImageEditChunk {
-          data?: { b64_json?: string };
-          format?: string;
-          type?: "partial" | "final";
-          partial_index?: number;
-          total_partials?: number;
-        }
-
-        await this.parseSSEStream<ImageEditChunk>(
-          reader,
-          targetConversationId,
-          (parsed) => {
-            const imageData = parsed.data?.b64_json;
-
-            if (imageData) {
-              const format = parsed.format || "png";
-              const mimeType = `image/${format}`;
-              if (parsed.type === "partial") {
-                // Update with partial image and progress
-                const partialNum = (parsed.partial_index ?? 0) + 1;
-                const totalPartials = parsed.total_partials ?? 3;
-                this.updateConversationMessage(
-                  targetConversationId,
-                  assistantMessage.id,
-                  (msg) => {
-                    msg.content = `Editing... ${partialNum}/${totalPartials}`;
-                    msg.attachments = [
-                      {
-                        type: "generated-image",
-                        name: `edited-image.${format}`,
-                        preview: `data:${mimeType};base64,${imageData}`,
-                        mimeType,
-                      },
-                    ];
-                  },
-                );
-              } else if (parsed.type === "final") {
-                // Final image
-                this.updateConversationMessage(
-                  targetConversationId,
-                  assistantMessage.id,
-                  (msg) => {
-                    msg.content = "";
-                    msg.attachments = [
-                      {
-                        type: "generated-image",
-                        name: `edited-image.${format}`,
-                        preview: `data:${mimeType};base64,${imageData}`,
-                        mimeType,
-                      },
-                    ];
-                  },
-                );
-              }
-              this.syncActiveMessagesIfNeeded(targetConversationId);
-            }
-          },
-        );
+      const reader = apiResponse.body?.getReader();
+      if (!reader) {
+        throw new Error("No response body");
       }
+
+      interface ImageEditChunk {
+        data?: { b64_json?: string };
+        format?: string;
+        type?: "partial" | "final";
+        partial_index?: number;
+        total_partials?: number;
+      }
+
+      await this.parseSSEStream<ImageEditChunk>(
+        reader,
+        targetConversationId,
+        (parsed) => {
+          const imageData = parsed.data?.b64_json;
+
+          if (imageData) {
+            const format = parsed.format || "png";
+            const mimeType = `image/${format}`;
+            if (parsed.type === "partial") {
+              // Update with partial image and progress
+              const partialNum = (parsed.partial_index ?? 0) + 1;
+              const totalPartials = parsed.total_partials ?? 3;
+              this.updateConversationMessage(
+                targetConversationId,
+                assistantMessage.id,
+                (msg) => {
+                  msg.content = `Editing... ${partialNum}/${totalPartials}`;
+                  msg.attachments = [
+                    {
+                      type: "generated-image",
+                      name: `edited-image.${format}`,
+                      preview: `data:${mimeType};base64,${imageData}`,
+                      mimeType,
+                    },
+                  ];
+                },
+              );
+            } else if (parsed.type === "final") {
+              // Final image
+              this.updateConversationMessage(
+                targetConversationId,
+                assistantMessage.id,
+                (msg) => {
+                  msg.content = "";
+                  msg.attachments = [
+                    {
+                      type: "generated-image",
+                      name: `edited-image.${format}`,
+                      preview: `data:${mimeType};base64,${imageData}`,
+                      mimeType,
+                    },
+                  ];
+                },
+              );
+            }
+            this.syncActiveMessagesIfNeeded(targetConversationId);
+          }
+        },
+      );
     } catch (error) {
       console.error("Error editing image:", error);
       this.handleStreamingError(

flake.lock

@@ -21,9 +21,7 @@
           "nixpkgs"
         ],
         "purescript-overlay": "purescript-overlay",
-        "pyproject-nix": [
-          "pyproject-nix"
-        ]
+        "pyproject-nix": "pyproject-nix"
       },
       "locked": {
         "lastModified": 1765953015,
@@ -151,44 +149,19 @@
         "type": "github"
       }
     },
-    "pyproject-build-systems": {
-      "inputs": {
-        "nixpkgs": [
-          "nixpkgs"
-        ],
-        "pyproject-nix": [
-          "pyproject-nix"
-        ],
-        "uv2nix": [
-          "uv2nix"
-        ]
-      },
-      "locked": {
-        "lastModified": 1763662255,
-        "narHash": "sha256-4bocaOyLa3AfiS8KrWjZQYu+IAta05u3gYZzZ6zXbT0=",
-        "owner": "pyproject-nix",
-        "repo": "build-system-pkgs",
-        "rev": "042904167604c681a090c07eb6967b4dd4dae88c",
-        "type": "github"
-      },
-      "original": {
-        "owner": "pyproject-nix",
-        "repo": "build-system-pkgs",
-        "type": "github"
-      }
-    },
     "pyproject-nix": {
       "inputs": {
         "nixpkgs": [
+          "dream2nix",
           "nixpkgs"
         ]
       },
       "locked": {
-        "lastModified": 1764134915,
-        "narHash": "sha256-xaKvtPx6YAnA3HQVp5LwyYG1MaN4LLehpQI8xEdBvBY=",
+        "lastModified": 1763017646,
+        "narHash": "sha256-Z+R2lveIp6Skn1VPH3taQIuMhABg1IizJd8oVdmdHsQ=",
         "owner": "pyproject-nix",
         "repo": "pyproject.nix",
-        "rev": "2c8df1383b32e5443c921f61224b198a2282a657",
+        "rev": "47bd6f296502842643078d66128f7b5e5370790c",
         "type": "github"
       },
       "original": {
@@ -205,10 +178,7 @@
         "flake-parts": "flake-parts",
         "nixpkgs": "nixpkgs",
         "nixpkgs-swift": "nixpkgs-swift",
-        "pyproject-build-systems": "pyproject-build-systems",
-        "pyproject-nix": "pyproject-nix",
-        "treefmt-nix": "treefmt-nix",
-        "uv2nix": "uv2nix"
+        "treefmt-nix": "treefmt-nix"
       }
     },
     "rust-analyzer-src": {
@@ -269,29 +239,6 @@
         "repo": "treefmt-nix",
         "type": "github"
       }
-    },
-    "uv2nix": {
-      "inputs": {
-        "nixpkgs": [
-          "nixpkgs"
-        ],
-        "pyproject-nix": [
-          "pyproject-nix"
-        ]
-      },
-      "locked": {
-        "lastModified": 1767701098,
-        "narHash": "sha256-CJhKZnWb3gumR9oTRjFvCg/6lYTGbZRU7xtvcyWIRwU=",
-        "owner": "pyproject-nix",
-        "repo": "uv2nix",
-        "rev": "9d357f0d2ce6f5f35ec7959d7e704452352eb4da",
-        "type": "github"
-      },
-      "original": {
-        "owner": "pyproject-nix",
-        "repo": "uv2nix",
-        "type": "github"
-      }
     }
   },
   "root": "root",

flake.nix

@@ -24,26 +24,6 @@
     dream2nix = {
       url = "github:nix-community/dream2nix";
       inputs.nixpkgs.follows = "nixpkgs";
-      inputs.pyproject-nix.follows = "pyproject-nix";
-    };
-
-    # Python packaging with uv2nix
-    pyproject-nix = {
-      url = "github:pyproject-nix/pyproject.nix";
-      inputs.nixpkgs.follows = "nixpkgs";
-    };
-
-    uv2nix = {
-      url = "github:pyproject-nix/uv2nix";
-      inputs.pyproject-nix.follows = "pyproject-nix";
-      inputs.nixpkgs.follows = "nixpkgs";
-    };
-
-    pyproject-build-systems = {
-      url = "github:pyproject-nix/build-system-pkgs";
-      inputs.pyproject-nix.follows = "pyproject-nix";
-      inputs.uv2nix.follows = "uv2nix";
-      inputs.nixpkgs.follows = "nixpkgs";
     };
 
     # Pinned nixpkgs for swift-format (swift is broken on x86_64-linux in newer nixpkgs)
@@ -68,7 +48,6 @@
         inputs.treefmt-nix.flakeModule
         ./dashboard/parts.nix
         ./rust/parts.nix
-        ./python/parts.nix
       ];
 
       perSystem =
@@ -109,6 +88,12 @@
             };
           };
 
+          checks.lint = pkgs.runCommand "lint-check" { } ''
+            export RUFF_CACHE_DIR="$TMPDIR/ruff-cache"
+            ${pkgs.ruff}/bin/ruff check ${inputs.self}/
+            touch $out
+          '';
+
           packages = lib.optionalAttrs pkgs.stdenv.hostPlatform.isDarwin (
             let
               uvLock = builtins.fromTOML (builtins.readFile ./uv.lock);

packaging/pyinstaller/exo.spec

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

python/parts.nix

@@ -1,93 +0,0 @@
-{ inputs, ... }:
-{
-  perSystem =
-    { config, self', pkgs, lib, system, ... }:
-    let
-      # Load workspace from uv.lock
-      workspace = inputs.uv2nix.lib.workspace.loadWorkspace {
-        workspaceRoot = inputs.self;
-      };
-
-      # Create overlay from workspace
-      # Use wheels from PyPI for most packages; we override mlx with our pure Nix Metal build
-      overlay = workspace.mkPyprojectOverlay { sourcePreference = "wheel"; };
-
-      # Override overlay to inject Nix-built components
-      exoOverlay = final: prev: {
-        # Replace workspace exo_pyo3_bindings with Nix-built wheel
-        exo-pyo3-bindings = pkgs.stdenv.mkDerivation {
-          pname = "exo-pyo3-bindings";
-          version = "0.1.0";
-          src = self'.packages.exo_pyo3_bindings;
-          # Install from pre-built wheel
-          nativeBuildInputs = [ final.pyprojectWheelHook ];
-          dontStrip = true;
-        };
-      };
-
-      python = pkgs.python313;
-
-      # Overlay to provide build systems and custom packages
-      buildSystemsOverlay = final: prev: {
-        # Use our pure Nix-built MLX with Metal support
-        mlx = self'.packages.mlx;
-
-        # mlx-lm is a git dependency that needs setuptools
-        mlx-lm = prev.mlx-lm.overrideAttrs (old: {
-          nativeBuildInputs = (old.nativeBuildInputs or [ ]) ++ [
-            final.setuptools
-          ];
-        });
-      };
-
-      pythonSet = (pkgs.callPackage inputs.pyproject-nix.build.packages {
-        inherit python;
-      }).overrideScope (
-        lib.composeManyExtensions [
-          inputs.pyproject-build-systems.overlays.default
-          overlay
-          exoOverlay
-          buildSystemsOverlay
-        ]
-      );
-      exoVenv = pythonSet.mkVirtualEnv "exo-env" workspace.deps.default;
-
-      # Virtual environment with dev dependencies for testing
-      testVenv = pythonSet.mkVirtualEnv "exo-test-env" (
-        workspace.deps.default // {
-          exo = [ "dev" ]; # Include pytest, pytest-asyncio, pytest-env
-        }
-      );
-
-      exoPackage = pkgs.runCommand "exo"
-        {
-          nativeBuildInputs = [ pkgs.makeWrapper ];
-        }
-        ''
-          mkdir -p $out/bin
-
-          # Create wrapper scripts
-          for script in exo exo-master exo-worker; do
-            makeWrapper ${exoVenv}/bin/$script $out/bin/$script \
-              --set DASHBOARD_DIR ${self'.packages.dashboard}
-          done
-        '';
-    in
-    {
-      # Python package only available on macOS (requires MLX/Metal)
-      packages = lib.optionalAttrs pkgs.stdenv.hostPlatform.isDarwin {
-        exo = exoPackage;
-        # Test environment for running pytest outside of Nix sandbox (needs GPU access)
-        exo-test-env = testVenv;
-      };
-
-      checks = {
-        # Ruff linting (works on all platforms)
-        lint = pkgs.runCommand "ruff-lint" { } ''
-          export RUFF_CACHE_DIR="$TMPDIR/ruff-cache"
-          ${pkgs.ruff}/bin/ruff check ${inputs.self}/
-          touch $out
-        '';
-      };
-    };
-}

resources/image_model_cards/exolabs--FLUX.1-Krea-dev-4bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-Krea-dev-4bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 15475325472
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 5950704160
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-Krea-dev-8bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-Krea-dev-8bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 21426029632
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 11901408320
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-Krea-dev.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-Krea-dev"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 33327437952
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 23802816640
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-dev-4bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-dev-4bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 15475325472
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 5950704160
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-dev-8bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-dev-8bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 21426029632
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 11901408320
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-dev.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-dev"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 33327437952
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 23802816640
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-schnell-4bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-schnell-4bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 15470210592
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 5945589280
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-schnell-8bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-schnell-8bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 21415799872
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 11891178560
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-schnell.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-schnell"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 33306978432
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 23782357120
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image-4bit.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image-4bit"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 26799533856
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 10215200544
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image-8bit.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image-8bit"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 37014734400
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 20430401088
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image-Edit-2509-4bit.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image-Edit-2509-4bit"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["ImageToImage"]
+
+[storage_size]
+in_bytes = 26799533856
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 10215200544
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image-Edit-2509-8bit.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image-Edit-2509-8bit"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["ImageToImage"]
+
+[storage_size]
+in_bytes = 37014734400
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 20430401088
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image-Edit-2509.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image-Edit-2509"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["ImageToImage"]
+
+[storage_size]
+in_bytes = 57445135488
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 40860802176
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 57445135488
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 40860802176
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/DeepSeek-V3.1-4bit"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 405874409472

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

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

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

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

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.5-Air-bf16"
+n_layers = 46
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 229780750336

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-4bit"
+n_layers = 91
+hidden_size = 5120
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 198556925568

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-6bit"
+n_layers = 91
+hidden_size = 5120
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 286737579648

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

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

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-Flash-4bit"
+n_layers = 47
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 19327352832

resources/inference_model_cards/mlx-community--GLM-4.7-Flash-5bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-Flash-5bit"
+n_layers = 47
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 22548578304

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-Flash-6bit"
+n_layers = 47
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 26843545600

resources/inference_model_cards/mlx-community--GLM-4.7-Flash-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-Flash-8bit"
+n_layers = 47
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 34359738368

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Kimi-K2-Instruct-4bit"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 620622774272

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Kimi-K2-Thinking"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 706522120192

resources/inference_model_cards/mlx-community--Kimi-K2.5.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Kimi-K2.5"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 662498705408

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Llama-3.2-1B-Instruct-4bit"
+n_layers = 16
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 729808896

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Llama-3.2-3B-Instruct-4bit"
+n_layers = 28
+hidden_size = 3072
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 1863319552

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

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

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Llama-3.3-70B-Instruct-4bit"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 40652242944

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

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

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Meta-Llama-3.1-70B-Instruct-4bit"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 40652242944

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

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

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

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

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

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

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/MiniMax-M2.1-3bit"
+n_layers = 61
+hidden_size = 3072
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 100086644736

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

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

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-0.6B-4bit"
+n_layers = 28
+hidden_size = 1024
+supports_tensor = false
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 342884352

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

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

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit"
+n_layers = 94
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 141733920768

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

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

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-30B-A3B-4bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 17612931072

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

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

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"
+n_layers = 62
+hidden_size = 6144
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 289910292480

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

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

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 46976204800

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

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

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 88814387200

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

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

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/gpt-oss-120b-MXFP4-Q8"
+n_layers = 36
+hidden_size = 2880
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 70652212224

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/gpt-oss-20b-MXFP4-Q8"
+n_layers = 24
+hidden_size = 2880
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 12025908224

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

@@ -0,0 +1,8 @@
+model_id = "mlx-community/llama-3.3-70b-instruct-fp16"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 144383672320

src/exo/download/impl_shard_downloader.py

@@ -7,7 +7,7 @@ from loguru import logger
 
 from exo.download.download_utils import RepoDownloadProgress, download_shard
 from exo.download.shard_downloader import ShardDownloader
-from exo.shared.models.model_cards import MODEL_CARDS, ModelCard, ModelId
+from exo.shared.models.model_cards import ModelCard, ModelId, get_model_cards
 from exo.shared.types.worker.shards import (
     PipelineShardMetadata,
     ShardMetadata,
@@ -21,7 +21,7 @@ def exo_shard_downloader(max_parallel_downloads: int = 8) -> ShardDownloader:
 
 
 async def build_base_shard(model_id: ModelId) -> ShardMetadata:
-    model_card = await ModelCard.from_hf(model_id)
+    model_card = await ModelCard.fetch_from_hf(model_id)
     return PipelineShardMetadata(
         model_card=model_card,
         device_rank=0,
@@ -160,7 +160,7 @@ class ResumableShardDownloader(ShardDownloader):
         # Kick off download status coroutines concurrently
         tasks = [
             asyncio.create_task(_status_for_model(model_card.model_id))
-            for model_card in MODEL_CARDS.values()
+            for model_card in await get_model_cards()
         ]
 
         for task in asyncio.as_completed(tasks):

src/exo/master/api.py

@@ -1,7 +1,6 @@
 import base64
 import contextlib
 import json
-import random
 import time
 from collections.abc import AsyncGenerator
 from http import HTTPStatus
@@ -23,16 +22,13 @@ from loguru import logger
 from exo.master.image_store import ImageStore
 from exo.master.placement import place_instance as get_instance_placements
 from exo.shared.apply import apply
-from exo.shared.constants import (
-    EXO_IMAGE_CACHE_DIR,
-    EXO_MAX_CHUNK_SIZE,
-)
+from exo.shared.constants import DASHBOARD_DIR, EXO_IMAGE_CACHE_DIR, EXO_MAX_CHUNK_SIZE
 from exo.shared.election import ElectionMessage
 from exo.shared.logging import InterceptLogger
 from exo.shared.models.model_cards import (
-    MODEL_CARDS,
     ModelCard,
     ModelId,
+    get_model_cards,
 )
 from exo.shared.types.api import (
     AdvancedImageParams,
@@ -105,7 +101,6 @@ from exo.shared.types.worker.instances import Instance, InstanceId, InstanceMeta
 from exo.shared.types.worker.shards import Sharding
 from exo.utils.banner import print_startup_banner
 from exo.utils.channels import Receiver, Sender, channel
-from exo.utils.dashboard_path import find_dashboard
 from exo.utils.event_buffer import OrderedBuffer
 
 
@@ -113,15 +108,6 @@ def _format_to_content_type(image_format: Literal["png", "jpeg", "webp"] | None)
     return f"image/{image_format or 'png'}"
 
 
-def _ensure_seed(params: AdvancedImageParams | None) -> AdvancedImageParams:
-    """Ensure advanced params has a seed set for distributed consistency."""
-    if params is None:
-        return AdvancedImageParams(seed=random.randint(0, 2**32 - 1))
-    if params.seed is None:
-        return params.model_copy(update={"seed": random.randint(0, 2**32 - 1)})
-    return params
-
-
 def chunk_to_response(
     chunk: TokenChunk | ToolCallChunk, command_id: CommandId
 ) -> ChatCompletionResponse:
@@ -151,18 +137,6 @@ def chunk_to_response(
     )
 
 
-async def resolve_model_card(model_id: ModelId) -> ModelCard:
-    if model_id in MODEL_CARDS:
-        model_card = MODEL_CARDS[model_id]
-        return model_card
-
-    for card in MODEL_CARDS.values():
-        if card.model_id == ModelId(model_id):
-            return card
-
-    return await ModelCard.from_hf(model_id)
-
-
 class API:
     def __init__(
         self,
@@ -200,7 +174,7 @@ class API:
         self.app.mount(
             "/",
             StaticFiles(
-                directory=find_dashboard(),
+                directory=DASHBOARD_DIR,
                 html=True,
             ),
             name="dashboard",
@@ -284,7 +258,7 @@ class API:
 
     async def place_instance(self, payload: PlaceInstanceParams):
         command = PlaceInstance(
-            model_card=await resolve_model_card(payload.model_id),
+            model_card=await ModelCard.load(payload.model_id),
             sharding=payload.sharding,
             instance_meta=payload.instance_meta,
             min_nodes=payload.min_nodes,
@@ -301,7 +275,7 @@ class API:
         self, payload: CreateInstanceParams
     ) -> CreateInstanceResponse:
         instance = payload.instance
-        model_card = await resolve_model_card(instance.shard_assignments.model_id)
+        model_card = await ModelCard.load(instance.shard_assignments.model_id)
         required_memory = model_card.storage_size
         available_memory = self._calculate_total_available_memory()
 
@@ -329,7 +303,7 @@ class API:
         instance_meta: InstanceMeta = InstanceMeta.MlxRing,
         min_nodes: int = 1,
     ) -> Instance:
-        model_card = await resolve_model_card(model_id)
+        model_card = await ModelCard.load(model_id)
 
         try:
             placements = get_instance_placements(
@@ -371,10 +345,7 @@ class API:
         if len(list(self.state.topology.list_nodes())) == 0:
             return PlacementPreviewResponse(previews=[])
 
-        cards = [card for card in MODEL_CARDS.values() if card.model_id == model_id]
-        if not cards:
-            raise HTTPException(status_code=404, detail=f"Model {model_id} not found")
-
+        model_card = await ModelCard.load(model_id)
         instance_combinations: list[tuple[Sharding, InstanceMeta, int]] = []
         for sharding in (Sharding.Pipeline, Sharding.Tensor):
             for instance_meta in (InstanceMeta.MlxRing, InstanceMeta.MlxJaccl):
@@ -389,96 +360,93 @@ class API:
         # TODO: PDD
         # instance_combinations.append((Sharding.PrefillDecodeDisaggregation, InstanceMeta.MlxRing, 1))
 
-        for model_card in cards:
-            for sharding, instance_meta, min_nodes in instance_combinations:
-                try:
-                    placements = get_instance_placements(
-                        PlaceInstance(
-                            model_card=model_card,
-                            sharding=sharding,
-                            instance_meta=instance_meta,
-                            min_nodes=min_nodes,
-                        ),
-                        node_memory=self.state.node_memory,
-                        node_network=self.state.node_network,
-                        topology=self.state.topology,
-                        current_instances=self.state.instances,
-                        required_nodes=required_nodes,
-                    )
-                except ValueError as exc:
-                    if (model_card.model_id, sharding, instance_meta, 0) not in seen:
-                        previews.append(
-                            PlacementPreview(
-                                model_id=model_card.model_id,
-                                sharding=sharding,
-                                instance_meta=instance_meta,
-                                instance=None,
-                                error=str(exc),
-                            )
-                        )
-                    seen.add((model_card.model_id, sharding, instance_meta, 0))
-                    continue
-
-                current_ids = set(self.state.instances.keys())
-                new_instances = [
-                    instance
-                    for instance_id, instance in placements.items()
-                    if instance_id not in current_ids
-                ]
-
-                if len(new_instances) != 1:
-                    if (model_card.model_id, sharding, instance_meta, 0) not in seen:
-                        previews.append(
-                            PlacementPreview(
-                                model_id=model_card.model_id,
-                                sharding=sharding,
-                                instance_meta=instance_meta,
-                                instance=None,
-                                error="Expected exactly one new instance from placement",
-                            )
-                        )
-                    seen.add((model_card.model_id, sharding, instance_meta, 0))
-                    continue
-
-                instance = new_instances[0]
-                shard_assignments = instance.shard_assignments
-                placement_node_ids = list(shard_assignments.node_to_runner.keys())
-
-                memory_delta_by_node: dict[str, int] = {}
-                if placement_node_ids:
-                    total_bytes = model_card.storage_size.in_bytes
-                    per_node = total_bytes // len(placement_node_ids)
-                    remainder = total_bytes % len(placement_node_ids)
-                    for index, node_id in enumerate(
-                        sorted(placement_node_ids, key=str)
-                    ):
-                        extra = 1 if index < remainder else 0
-                        memory_delta_by_node[str(node_id)] = per_node + extra
-
-                if (
-                    model_card.model_id,
-                    sharding,
-                    instance_meta,
-                    len(placement_node_ids),
-                ) not in seen:
+        for sharding, instance_meta, min_nodes in instance_combinations:
+            try:
+                placements = get_instance_placements(
+                    PlaceInstance(
+                        model_card=model_card,
+                        sharding=sharding,
+                        instance_meta=instance_meta,
+                        min_nodes=min_nodes,
+                    ),
+                    node_memory=self.state.node_memory,
+                    node_network=self.state.node_network,
+                    topology=self.state.topology,
+                    current_instances=self.state.instances,
+                    required_nodes=required_nodes,
+                )
+            except ValueError as exc:
+                if (model_card.model_id, sharding, instance_meta, 0) not in seen:
                     previews.append(
                         PlacementPreview(
                             model_id=model_card.model_id,
                             sharding=sharding,
                             instance_meta=instance_meta,
-                            instance=instance,
-                            memory_delta_by_node=memory_delta_by_node or None,
-                            error=None,
+                            instance=None,
+                            error=str(exc),
                         )
                     )
-                seen.add(
-                    (
-                        model_card.model_id,
-                        sharding,
-                        instance_meta,
-                        len(placement_node_ids),
+                seen.add((model_card.model_id, sharding, instance_meta, 0))
+                continue
+
+            current_ids = set(self.state.instances.keys())
+            new_instances = [
+                instance
+                for instance_id, instance in placements.items()
+                if instance_id not in current_ids
+            ]
+
+            if len(new_instances) != 1:
+                if (model_card.model_id, sharding, instance_meta, 0) not in seen:
+                    previews.append(
+                        PlacementPreview(
+                            model_id=model_card.model_id,
+                            sharding=sharding,
+                            instance_meta=instance_meta,
+                            instance=None,
+                            error="Expected exactly one new instance from placement",
+                        )
+                    )
+                seen.add((model_card.model_id, sharding, instance_meta, 0))
+                continue
+
+            instance = new_instances[0]
+            shard_assignments = instance.shard_assignments
+            placement_node_ids = list(shard_assignments.node_to_runner.keys())
+
+            memory_delta_by_node: dict[str, int] = {}
+            if placement_node_ids:
+                total_bytes = model_card.storage_size.in_bytes
+                per_node = total_bytes // len(placement_node_ids)
+                remainder = total_bytes % len(placement_node_ids)
+                for index, node_id in enumerate(sorted(placement_node_ids, key=str)):
+                    extra = 1 if index < remainder else 0
+                    memory_delta_by_node[str(node_id)] = per_node + extra
+
+            if (
+                model_card.model_id,
+                sharding,
+                instance_meta,
+                len(placement_node_ids),
+            ) not in seen:
+                previews.append(
+                    PlacementPreview(
+                        model_id=model_card.model_id,
+                        sharding=sharding,
+                        instance_meta=instance_meta,
+                        instance=instance,
+                        memory_delta_by_node=memory_delta_by_node or None,
+                        error=None,
                     )
                 )
+            seen.add(
+                (
+                    model_card.model_id,
+                    sharding,
+                    instance_meta,
+                    len(placement_node_ids),
+                )
+            )
 
         return PlacementPreviewResponse(previews=previews)
 
@@ -683,7 +651,7 @@ class API:
         self, payload: ChatCompletionTaskParams
     ) -> ChatCompletionResponse | StreamingResponse:
         """Handle chat completions, supporting both streaming and non-streaming responses."""
-        model_card = await resolve_model_card(ModelId(payload.model))
+        model_card = await ModelCard.load(ModelId(payload.model))
         payload.model = model_card.model_id
 
         if not any(
@@ -710,7 +678,7 @@ class API:
     async def bench_chat_completions(
         self, payload: BenchChatCompletionTaskParams
     ) -> BenchChatCompletionResponse:
-        model_card = await resolve_model_card(ModelId(payload.model))
+        model_card = await ModelCard.load(ModelId(payload.model))
         payload.model = model_card.model_id
 
         if not any(
@@ -735,7 +703,7 @@ class API:
 
         Raises HTTPException 404 if no instance is found for the model.
         """
-        model_card = await resolve_model_card(ModelId(model))
+        model_card = await ModelCard.load(ModelId(model))
         resolved_model = model_card.model_id
         if not any(
             instance.shard_assignments.model_id == resolved_model
@@ -782,9 +750,6 @@ class API:
         with SSE-formatted events for partial and final images.
         """
         payload.model = await self._validate_image_model(payload.model)
-        payload = payload.model_copy(
-            update={"advanced_params": _ensure_seed(payload.advanced_params)}
-        )
 
         command = ImageGeneration(
             request_params=payload,
@@ -1033,9 +998,6 @@ class API:
 
         payload.stream = False
         payload.partial_images = 0
-        payload = payload.model_copy(
-            update={"advanced_params": _ensure_seed(payload.advanced_params)}
-        )
 
         command = ImageGeneration(
             request_params=payload,
@@ -1067,7 +1029,6 @@ class API:
     ) -> ImageEdits:
         """Prepare and send an image edits command with chunked image upload."""
         resolved_model = await self._validate_image_model(model)
-        advanced_params = _ensure_seed(advanced_params)
 
         image_content = await image.read()
         image_data = base64.b64encode(image_content).decode("utf-8")
@@ -1248,7 +1209,7 @@ class API:
                     supports_tensor=card.supports_tensor,
                     tasks=[task.value for task in card.tasks],
                 )
-                for card in MODEL_CARDS.values()
+                for card in await get_model_cards()
             ]
         )
 

src/exo/master/placement_utils.py

@@ -94,35 +94,20 @@ def get_shard_assignments_for_pipeline_parallel(
     runner_to_shard: dict[RunnerId, ShardMetadata] = {}
     node_to_runner: dict[NodeId, RunnerId] = {}
 
-    # Determine CFG parallelism topology
-    # CFG parallel only for even node counts with CFG models (2+ nodes)
-    use_cfg_parallel = model_card.uses_cfg and world_size >= 2 and world_size % 2 == 0
-    cfg_world_size = 2 if use_cfg_parallel else 1
-    pipeline_world_size = world_size // cfg_world_size
-
-    # For CFG parallel, we only need to allocate layers for one pipeline group
-    # (both CFG groups run the same layers). Use the first pipeline group's nodes.
-    pipeline_node_ids = cycle.node_ids[:pipeline_world_size]
-    pipeline_memory = sum(
-        (node_memory[node_id].ram_available for node_id in pipeline_node_ids),
-        start=Memory(),
-    )
-
     layer_allocations = allocate_layers_proportionally(
         total_layers=total_layers,
         memory_fractions=[
-            node_memory[node_id].ram_available.in_bytes / pipeline_memory.in_bytes
-            for node_id in pipeline_node_ids
+            node_memory[node_id].ram_available.in_bytes / cycle_memory.in_bytes
+            for node_id in cycle.node_ids
         ],
     )
 
-    # Validate each pipeline node has sufficient memory for its assigned layers
-    # Use integer arithmetic to avoid floating point precision issues
-    total_storage_bytes = model_card.storage_size.in_bytes
-    for i, node_id in enumerate(pipeline_node_ids):
-        node_layers = layer_allocations[i]
-        # Integer division then multiply to get conservative estimate
-        required_memory = (total_storage_bytes * node_layers) // total_layers
+    # Validate each node has sufficient memory for its assigned layers
+    memory_per_layer = model_card.storage_size.in_bytes / total_layers
+    for i, (node_id, node_layers) in enumerate(
+        zip(cycle.node_ids, layer_allocations, strict=True)
+    ):
+        required_memory = node_layers * memory_per_layer
         available_memory = node_memory[node_id].ram_available.in_bytes
         if required_memory > available_memory:
             raise ValueError(
@@ -131,69 +116,24 @@ def get_shard_assignments_for_pipeline_parallel(
                 f"but only has {available_memory / (1024**3):.2f} GB available"
             )
 
-    # CFG group 0: pipeline ranks in ascending order (0, 1, 2, ...)
-    # CFG group 1: pipeline ranks in descending order (reversed)
-    # This places both "last stages" as ring neighbors for CFG exchange.
-    position_to_cfg_pipeline = [(0, r) for r in range(pipeline_world_size)] + [
-        (1, r) for r in reversed(range(pipeline_world_size))
-    ]
-
-    cfg_pipeline_to_device: dict[tuple[int, int], int] = {
-        (cfg_rank, pipeline_rank): i
-        for i, (cfg_rank, pipeline_rank) in enumerate(position_to_cfg_pipeline)
-    }
-
-    for i, node_id in enumerate(cycle.node_ids):
-        cfg_rank, pipeline_rank = position_to_cfg_pipeline[i]
-
-        layers_before = sum(layer_allocations[:pipeline_rank])
-        node_layers = layer_allocations[pipeline_rank]
-
-        is_first_stage = pipeline_rank == 0
-        is_last_stage = pipeline_rank == pipeline_world_size - 1
-
-        if is_last_stage:
-            next_pipeline_device = None
-        else:
-            next_pipeline_device = cfg_pipeline_to_device[(cfg_rank, pipeline_rank + 1)]
-
-        if is_first_stage:
-            prev_pipeline_device = None
-        else:
-            prev_pipeline_device = cfg_pipeline_to_device[(cfg_rank, pipeline_rank - 1)]
-
-        if is_last_stage and use_cfg_parallel:
-            other_cfg_rank = 1 - cfg_rank
-            cfg_peer_device = cfg_pipeline_to_device[(other_cfg_rank, pipeline_rank)]
-        else:
-            cfg_peer_device = None
-
-        first_pipeline_device = cfg_pipeline_to_device[(cfg_rank, 0)]
-        last_pipeline_device = cfg_pipeline_to_device[
-            (cfg_rank, pipeline_world_size - 1)
-        ]
-
+    layers_assigned = 0
+    for i, (node_id, node_layers) in enumerate(
+        zip(cycle.node_ids, layer_allocations, strict=True)
+    ):
         runner_id = RunnerId()
 
         shard = PipelineShardMetadata(
             model_card=model_card,
             device_rank=i,
             world_size=world_size,
-            start_layer=layers_before,
-            end_layer=layers_before + node_layers,
+            start_layer=layers_assigned,
+            end_layer=layers_assigned + node_layers,
             n_layers=total_layers,
-            cfg_rank=cfg_rank,
-            cfg_world_size=cfg_world_size,
-            explicit_pipeline_rank=pipeline_rank,
-            next_pipeline_device=next_pipeline_device,
-            prev_pipeline_device=prev_pipeline_device,
-            cfg_peer_device=cfg_peer_device,
-            first_pipeline_device=first_pipeline_device,
-            last_pipeline_device=last_pipeline_device,
         )
 
         runner_to_shard[runner_id] = shard
         node_to_runner[node_id] = runner_id
+        layers_assigned += node_layers
 
     shard_assignments = ShardAssignments(
         model_id=model_card.model_id,

src/exo/master/tests/test_placement_utils.py

@@ -5,7 +5,6 @@ from exo.master.placement_utils import (
     filter_cycles_by_memory,
     get_mlx_jaccl_coordinators,
     get_shard_assignments,
-    get_shard_assignments_for_pipeline_parallel,
     get_smallest_cycles,
 )
 from exo.master.tests.conftest import (
@@ -21,7 +20,7 @@ from exo.shared.types.profiling import (
     NodeNetworkInfo,
 )
 from exo.shared.types.topology import Connection, SocketConnection
-from exo.shared.types.worker.shards import PipelineShardMetadata, Sharding
+from exo.shared.types.worker.shards import Sharding
 
 
 def test_filter_cycles_by_memory():
@@ -488,195 +487,3 @@ def test_get_shard_assignments_insufficient_memory_raises():
         get_shard_assignments(
             model_card, selected_cycle, Sharding.Pipeline, node_memory
         )
-
-
-class TestCfgParallelPlacement:
-    def _create_ring_topology(self, node_ids: list[NodeId]) -> Topology:
-        topology = Topology()
-        for node_id in node_ids:
-            topology.add_node(node_id)
-
-        for i, node_id in enumerate(node_ids):
-            next_node = node_ids[(i + 1) % len(node_ids)]
-            conn = Connection(
-                source=node_id,
-                sink=next_node,
-                edge=create_socket_connection(i + 1),
-            )
-            topology.add_connection(conn)
-
-        return topology
-
-    def test_two_nodes_cfg_model_uses_cfg_parallel(self):
-        """Two nodes with CFG model should use CFG parallel (no pipeline)."""
-        node_a = NodeId()
-        node_b = NodeId()
-
-        topology = self._create_ring_topology([node_a, node_b])
-        cycles = [c for c in topology.get_cycles() if len(c) == 2]
-        cycle = cycles[0]
-
-        node_memory = {
-            node_a: create_node_memory(1000 * 1024),
-            node_b: create_node_memory(1000 * 1024),
-        }
-
-        model_card = ModelCard(
-            model_id=ModelId("qwen-image-test"),
-            n_layers=60,
-            storage_size=Memory.from_kb(1000),
-            hidden_size=1,
-            supports_tensor=False,
-            uses_cfg=True,
-            tasks=[ModelTask.TextToImage],
-        )
-
-        assignments = get_shard_assignments_for_pipeline_parallel(
-            model_card, cycle, node_memory
-        )
-
-        shards = list(assignments.runner_to_shard.values())
-        assert len(shards) == 2
-
-        # Both nodes should have all layers (no pipeline split)
-        for shard in shards:
-            assert isinstance(shard, PipelineShardMetadata)
-            assert shard.start_layer == 0
-            assert shard.end_layer == 60
-            assert shard.cfg_world_size == 2
-
-        cfg_ranks = sorted(
-            s.cfg_rank for s in shards if isinstance(s, PipelineShardMetadata)
-        )
-        assert cfg_ranks == [0, 1]
-
-    def test_four_nodes_cfg_model_uses_hybrid(self):
-        """Four nodes with CFG model should use 2 CFG groups x 2 pipeline stages."""
-        nodes = [NodeId() for _ in range(4)]
-
-        topology = self._create_ring_topology(nodes)
-        cycles = [c for c in topology.get_cycles() if len(c) == 4]
-        cycle = cycles[0]
-
-        node_memory = {n: create_node_memory(1000 * 1024) for n in nodes}
-
-        model_card = ModelCard(
-            model_id=ModelId("qwen-image-test"),
-            n_layers=60,
-            storage_size=Memory.from_kb(1000),
-            hidden_size=1,
-            supports_tensor=False,
-            uses_cfg=True,
-            tasks=[ModelTask.TextToImage],
-        )
-
-        assignments = get_shard_assignments_for_pipeline_parallel(
-            model_card, cycle, node_memory
-        )
-
-        shards = list(assignments.runner_to_shard.values())
-        assert len(shards) == 4
-
-        for shard in shards:
-            assert isinstance(shard, PipelineShardMetadata)
-            assert shard.cfg_world_size == 2
-            assert shard.pipeline_world_size == 2
-
-        # Check we have 2 nodes in each CFG group
-        cfg_0_shards = [
-            s
-            for s in shards
-            if isinstance(s, PipelineShardMetadata) and s.cfg_rank == 0
-        ]
-        cfg_1_shards = [
-            s
-            for s in shards
-            if isinstance(s, PipelineShardMetadata) and s.cfg_rank == 1
-        ]
-        assert len(cfg_0_shards) == 2
-        assert len(cfg_1_shards) == 2
-
-        # Both CFG groups should have the same layer assignments
-        cfg_0_layers = [(s.start_layer, s.end_layer) for s in cfg_0_shards]
-        cfg_1_layers = [(s.start_layer, s.end_layer) for s in cfg_1_shards]
-        assert sorted(cfg_0_layers) == sorted(cfg_1_layers)
-
-    def test_three_nodes_cfg_model_uses_sequential_cfg(self):
-        """Three nodes (odd) with CFG model should use sequential CFG."""
-        nodes = [NodeId() for _ in range(3)]
-
-        topology = self._create_ring_topology(nodes)
-        cycles = [c for c in topology.get_cycles() if len(c) == 3]
-        cycle = cycles[0]
-
-        node_memory = {n: create_node_memory(1000 * 1024) for n in nodes}
-
-        model_card = ModelCard(
-            model_id=ModelId("qwen-image-test"),
-            n_layers=60,
-            storage_size=Memory.from_kb(1000),
-            hidden_size=1,
-            supports_tensor=False,
-            uses_cfg=True,
-            tasks=[ModelTask.TextToImage],
-        )
-
-        assignments = get_shard_assignments_for_pipeline_parallel(
-            model_card, cycle, node_memory
-        )
-
-        shards = list(assignments.runner_to_shard.values())
-        assert len(shards) == 3
-
-        for shard in shards:
-            assert isinstance(shard, PipelineShardMetadata)
-            # cfg_world_size = 1 means sequential CFG
-            assert shard.cfg_world_size == 1
-            assert shard.cfg_rank == 0
-
-    def test_two_nodes_non_cfg_model_uses_pipeline(self):
-        """Two nodes with non-CFG model should use pure pipeline."""
-        node_a = NodeId()
-        node_b = NodeId()
-
-        topology = self._create_ring_topology([node_a, node_b])
-        cycles = [c for c in topology.get_cycles() if len(c) == 2]
-        cycle = cycles[0]
-
-        node_memory = {
-            node_a: create_node_memory(1000 * 1024),
-            node_b: create_node_memory(1000 * 1024),
-        }
-
-        model_card = ModelCard(
-            model_id=ModelId("flux-test"),
-            n_layers=57,
-            storage_size=Memory.from_kb(1000),
-            hidden_size=1,
-            supports_tensor=False,
-            uses_cfg=False,  # Non-CFG model
-            tasks=[ModelTask.TextToImage],
-        )
-
-        assignments = get_shard_assignments_for_pipeline_parallel(
-            model_card, cycle, node_memory
-        )
-
-        shards = list(assignments.runner_to_shard.values())
-        assert len(shards) == 2
-
-        for shard in shards:
-            assert isinstance(shard, PipelineShardMetadata)
-            # cfg_world_size = 1 means no CFG parallel
-            assert shard.cfg_world_size == 1
-            assert shard.cfg_rank == 0
-
-        # Should have actual layer sharding (pipeline)
-        layer_ranges = sorted(
-            (s.start_layer, s.end_layer)
-            for s in shards
-            if isinstance(s, PipelineShardMetadata)
-        )
-        # First shard starts at 0, last shard ends at 57
-        assert layer_ranges[0][0] == 0
-        assert layer_ranges[-1][1] == 57

src/exo/shared/constants.py

@@ -2,6 +2,8 @@ import os
 import sys
 from pathlib import Path
 
+from exo.utils.dashboard_path import find_dashboard, find_resources
+
 _EXO_HOME_ENV = os.environ.get("EXO_HOME", None)
 
 
@@ -31,6 +33,14 @@ EXO_MODELS_DIR = (
     if _EXO_MODELS_DIR_ENV is None
     else Path.home() / _EXO_MODELS_DIR_ENV
 )
+_RESOURCES_DIR_ENV = os.environ.get("EXO_RESOURCES_DIR", None)
+RESOURCES_DIR = (
+    find_resources() if _RESOURCES_DIR_ENV is None else Path.home() / _RESOURCES_DIR_ENV
+)
+_DASHBOARD_DIR_ENV = os.environ.get("EXO_DASHBOARD_DIR", None)
+DASHBOARD_DIR = (
+    find_dashboard() if _RESOURCES_DIR_ENV is None else Path.home() / _RESOURCES_DIR_ENV
+)
 
 # Log files (data/logs or cache)
 EXO_LOG = EXO_CACHE_HOME / "exo.log"

src/exo/shared/models/model_cards.py

@@ -12,16 +12,42 @@ from pydantic import (
     BaseModel,
     Field,
     PositiveInt,
+    ValidationError,
     field_validator,
     model_validator,
 )
+from tomlkit.exceptions import TOMLKitError
 
-from exo.shared.constants import EXO_ENABLE_IMAGE_MODELS
+from exo.shared.constants import EXO_ENABLE_IMAGE_MODELS, RESOURCES_DIR
 from exo.shared.types.common import ModelId
 from exo.shared.types.memory import Memory
 from exo.utils.pydantic_ext import CamelCaseModel
 
-_card_cache: dict[str, "ModelCard"] = {}
+# kinda ugly...
+# TODO: load search path from config.toml
+_csp = [Path(RESOURCES_DIR)]
+if EXO_ENABLE_IMAGE_MODELS:
+    _csp.append(Path(RESOURCES_DIR) / "image_models")
+
+CARD_SEARCH_PATH = _csp
+
+_card_cache: dict[ModelId, "ModelCard"] = {}
+
+
+async def _populate_card_cache():
+    for path in CARD_SEARCH_PATH:
+        async for toml_file in path.rglob("*.toml"):
+            try:
+                card = await ModelCard.load_from_path(toml_file)
+                _card_cache[card.model_id] = card
+            except (ValidationError, TOMLKitError):
+                pass
+
+
+async def get_model_cards() -> list["ModelCard"]:
+    if len(_card_cache) == 0:
+        await _populate_card_cache()
+    return list(_card_cache.values())
 
 
 class ModelTask(str, Enum):
@@ -47,7 +73,6 @@ class ModelCard(CamelCaseModel):
     supports_tensor: bool
     tasks: list[ModelTask]
     components: list[ComponentInfo] | None = None
-    uses_cfg: bool = False
 
     @field_validator("tasks", mode="before")
     @classmethod
@@ -56,28 +81,37 @@ class ModelCard(CamelCaseModel):
 
     async def save(self, path: Path) -> None:
         async with await open_file(path, "w") as f:
-            py = self.model_dump()
+            py = self.model_dump(exclude_none=True)
             data = tomlkit.dumps(py)  # pyright: ignore[reportUnknownMemberType]
             await f.write(data)
 
+    async def save_to_default_path(self):
+        await self.save(Path(RESOURCES_DIR) / (self.model_id.normalize() + ".toml"))
+
     @staticmethod
     async def load_from_path(path: Path) -> "ModelCard":
         async with await open_file(path, "r") as f:
             py = tomlkit.loads(await f.read())
             return ModelCard.model_validate(py)
 
+    # Is it okay that model card.load defaults to network access if the card doesn't exist? do we want to be more explicit here?
     @staticmethod
     async def load(model_id: ModelId) -> "ModelCard":
-        for card in MODEL_CARDS.values():
-            if card.model_id == model_id:
-                return card
-        return await ModelCard.from_hf(model_id)
-
-    @staticmethod
-    async def from_hf(model_id: ModelId) -> "ModelCard":
-        """Fetches storage size and number of layers for a Hugging Face model, returns Pydantic ModelMeta."""
+        if len(_card_cache) == 0:
+            await _populate_card_cache()
         if (mc := _card_cache.get(model_id)) is not None:
             return mc
+
+        return await ModelCard.fetch_from_hf(model_id)
+
+    @staticmethod
+    async def fetch_from_hf(model_id: ModelId) -> "ModelCard":
+        """Fetches storage size and number of layers for a Hugging Face model, returns Pydantic ModelMeta."""
+        if len(_card_cache) == 0:
+            await _populate_card_cache()
+        if (mc := _card_cache.get(model_id)) is not None:
+            return mc
+        # TODO: failure if files do not exist
         config_data = await get_config_data(model_id)
         num_layers = config_data.layer_count
         mem_size_bytes = await get_safetensors_size(model_id)
@@ -90,546 +124,13 @@ class ModelCard(CamelCaseModel):
             supports_tensor=config_data.supports_tensor,
             tasks=[ModelTask.TextGeneration],
         )
+        await mc.save_to_default_path()
         _card_cache[model_id] = mc
         return mc
 
 
-MODEL_CARDS: dict[str, ModelCard] = {
-    # deepseek v3
-    "deepseek-v3.1-4bit": ModelCard(
-        model_id=ModelId("mlx-community/DeepSeek-V3.1-4bit"),
-        storage_size=Memory.from_gb(378),
-        n_layers=61,
-        hidden_size=7168,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "deepseek-v3.1-8bit": ModelCard(
-        model_id=ModelId("mlx-community/DeepSeek-V3.1-8bit"),
-        storage_size=Memory.from_gb(713),
-        n_layers=61,
-        hidden_size=7168,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # kimi k2
-    "kimi-k2-instruct-4bit": ModelCard(
-        model_id=ModelId("mlx-community/Kimi-K2-Instruct-4bit"),
-        storage_size=Memory.from_gb(578),
-        n_layers=61,
-        hidden_size=7168,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "kimi-k2-thinking": ModelCard(
-        model_id=ModelId("mlx-community/Kimi-K2-Thinking"),
-        storage_size=Memory.from_gb(658),
-        n_layers=61,
-        hidden_size=7168,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "kimi-k2.5": ModelCard(
-        model_id=ModelId("mlx-community/Kimi-K2.5"),
-        storage_size=Memory.from_gb(617),
-        n_layers=61,
-        hidden_size=7168,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # llama-3.1
-    "llama-3.1-8b": ModelCard(
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"),
-        storage_size=Memory.from_mb(4423),
-        n_layers=32,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.1-8b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-8bit"),
-        storage_size=Memory.from_mb(8540),
-        n_layers=32,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.1-8b-bf16": ModelCard(
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"),
-        storage_size=Memory.from_mb(16100),
-        n_layers=32,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.1-70b": ModelCard(
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-70B-Instruct-4bit"),
-        storage_size=Memory.from_mb(38769),
-        n_layers=80,
-        hidden_size=8192,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # llama-3.2
-    "llama-3.2-1b": ModelCard(
-        model_id=ModelId("mlx-community/Llama-3.2-1B-Instruct-4bit"),
-        storage_size=Memory.from_mb(696),
-        n_layers=16,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.2-3b": ModelCard(
-        model_id=ModelId("mlx-community/Llama-3.2-3B-Instruct-4bit"),
-        storage_size=Memory.from_mb(1777),
-        n_layers=28,
-        hidden_size=3072,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.2-3b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Llama-3.2-3B-Instruct-8bit"),
-        storage_size=Memory.from_mb(3339),
-        n_layers=28,
-        hidden_size=3072,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # llama-3.3
-    "llama-3.3-70b": ModelCard(
-        model_id=ModelId("mlx-community/Llama-3.3-70B-Instruct-4bit"),
-        storage_size=Memory.from_mb(38769),
-        n_layers=80,
-        hidden_size=8192,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.3-70b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Llama-3.3-70B-Instruct-8bit"),
-        storage_size=Memory.from_mb(73242),
-        n_layers=80,
-        hidden_size=8192,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.3-70b-fp16": ModelCard(
-        model_id=ModelId("mlx-community/llama-3.3-70b-instruct-fp16"),
-        storage_size=Memory.from_mb(137695),
-        n_layers=80,
-        hidden_size=8192,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # qwen3
-    "qwen3-0.6b": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-0.6B-4bit"),
-        storage_size=Memory.from_mb(327),
-        n_layers=28,
-        hidden_size=1024,
-        supports_tensor=False,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-0.6b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-0.6B-8bit"),
-        storage_size=Memory.from_mb(666),
-        n_layers=28,
-        hidden_size=1024,
-        supports_tensor=False,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-30b": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-30B-A3B-4bit"),
-        storage_size=Memory.from_mb(16797),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-30b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-30B-A3B-8bit"),
-        storage_size=Memory.from_mb(31738),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-80b-a3B-4bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit"),
-        storage_size=Memory.from_mb(44800),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-80b-a3B-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit"),
-        storage_size=Memory.from_mb(84700),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-80b-a3B-thinking-4bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit"),
-        storage_size=Memory.from_mb(84700),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-80b-a3B-thinking-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Thinking-8bit"),
-        storage_size=Memory.from_mb(84700),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-235b-a22b-4bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit"),
-        storage_size=Memory.from_gb(132),
-        n_layers=94,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-235b-a22b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit"),
-        storage_size=Memory.from_gb(250),
-        n_layers=94,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-coder-480b-a35b-4bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"),
-        storage_size=Memory.from_gb(270),
-        n_layers=62,
-        hidden_size=6144,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-coder-480b-a35b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Coder-480B-A35B-Instruct-8bit"),
-        storage_size=Memory.from_gb(540),
-        n_layers=62,
-        hidden_size=6144,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # gpt-oss
-    "gpt-oss-120b-MXFP4-Q8": ModelCard(
-        model_id=ModelId("mlx-community/gpt-oss-120b-MXFP4-Q8"),
-        storage_size=Memory.from_kb(68_996_301),
-        n_layers=36,
-        hidden_size=2880,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "gpt-oss-20b-MXFP4-Q8": ModelCard(
-        model_id=ModelId("mlx-community/gpt-oss-20b-MXFP4-Q8"),
-        storage_size=Memory.from_kb(11_744_051),
-        n_layers=24,
-        hidden_size=2880,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # glm 4.5
-    "glm-4.5-air-8bit": ModelCard(
-        # Needs to be quantized g32 or g16 to work with tensor parallel
-        model_id=ModelId("mlx-community/GLM-4.5-Air-8bit"),
-        storage_size=Memory.from_gb(114),
-        n_layers=46,
-        hidden_size=4096,
-        supports_tensor=False,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.5-air-bf16": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.5-Air-bf16"),
-        storage_size=Memory.from_gb(214),
-        n_layers=46,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # glm 4.7
-    "glm-4.7-4bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-4bit"),
-        storage_size=Memory.from_bytes(198556925568),
-        n_layers=91,
-        hidden_size=5120,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.7-6bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-6bit"),
-        storage_size=Memory.from_bytes(286737579648),
-        n_layers=91,
-        hidden_size=5120,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.7-8bit-gs32": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-8bit-gs32"),
-        storage_size=Memory.from_bytes(396963397248),
-        n_layers=91,
-        hidden_size=5120,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # glm 4.7 flash
-    "glm-4.7-flash-4bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-Flash-4bit"),
-        storage_size=Memory.from_gb(18),
-        n_layers=47,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.7-flash-5bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-Flash-5bit"),
-        storage_size=Memory.from_gb(21),
-        n_layers=47,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.7-flash-6bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-Flash-6bit"),
-        storage_size=Memory.from_gb(25),
-        n_layers=47,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.7-flash-8bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-Flash-8bit"),
-        storage_size=Memory.from_gb(32),
-        n_layers=47,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # minimax-m2
-    "minimax-m2.1-8bit": ModelCard(
-        model_id=ModelId("mlx-community/MiniMax-M2.1-8bit"),
-        storage_size=Memory.from_bytes(242986745856),
-        n_layers=61,
-        hidden_size=3072,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "minimax-m2.1-3bit": ModelCard(
-        model_id=ModelId("mlx-community/MiniMax-M2.1-3bit"),
-        storage_size=Memory.from_bytes(100086644736),
-        n_layers=61,
-        hidden_size=3072,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-}
-
-_IMAGE_BASE_MODEL_CARDS: dict[str, ModelCard] = {
-    "flux1-schnell": ModelCard(
-        model_id=ModelId("exolabs/FLUX.1-schnell"),
-        storage_size=Memory.from_bytes(23782357120 + 9524621312),
-        n_layers=57,
-        hidden_size=1,
-        supports_tensor=False,
-        tasks=[ModelTask.TextToImage],
-        components=[
-            ComponentInfo(
-                component_name="text_encoder",
-                component_path="text_encoder/",
-                storage_size=Memory.from_kb(0),
-                n_layers=12,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-            ComponentInfo(
-                component_name="text_encoder_2",
-                component_path="text_encoder_2/",
-                storage_size=Memory.from_bytes(9524621312),
-                n_layers=24,
-                can_shard=False,
-                safetensors_index_filename="model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="transformer",
-                component_path="transformer/",
-                storage_size=Memory.from_bytes(23782357120),
-                n_layers=57,
-                can_shard=True,
-                safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="vae",
-                component_path="vae/",
-                storage_size=Memory.from_kb(0),
-                n_layers=None,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-        ],
-    ),
-    "flux1-dev": ModelCard(
-        model_id=ModelId("exolabs/FLUX.1-dev"),
-        storage_size=Memory.from_bytes(23782357120 + 9524621312),
-        n_layers=57,
-        hidden_size=1,
-        supports_tensor=False,
-        tasks=[ModelTask.TextToImage],
-        components=[
-            ComponentInfo(
-                component_name="text_encoder",
-                component_path="text_encoder/",
-                storage_size=Memory.from_kb(0),
-                n_layers=12,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-            ComponentInfo(
-                component_name="text_encoder_2",
-                component_path="text_encoder_2/",
-                storage_size=Memory.from_bytes(9524621312),
-                n_layers=24,
-                can_shard=False,
-                safetensors_index_filename="model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="transformer",
-                component_path="transformer/",
-                storage_size=Memory.from_bytes(23802816640),
-                n_layers=57,
-                can_shard=True,
-                safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="vae",
-                component_path="vae/",
-                storage_size=Memory.from_kb(0),
-                n_layers=None,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-        ],
-    ),
-    "flux1-krea-dev": ModelCard(
-        model_id=ModelId("exolabs/FLUX.1-Krea-dev"),
-        storage_size=Memory.from_bytes(23802816640 + 9524621312),  # Same as dev
-        n_layers=57,
-        hidden_size=1,
-        supports_tensor=False,
-        tasks=[ModelTask.TextToImage],
-        components=[
-            ComponentInfo(
-                component_name="text_encoder",
-                component_path="text_encoder/",
-                storage_size=Memory.from_kb(0),
-                n_layers=12,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-            ComponentInfo(
-                component_name="text_encoder_2",
-                component_path="text_encoder_2/",
-                storage_size=Memory.from_bytes(9524621312),
-                n_layers=24,
-                can_shard=False,
-                safetensors_index_filename="model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="transformer",
-                component_path="transformer/",
-                storage_size=Memory.from_bytes(23802816640),
-                n_layers=57,
-                can_shard=True,
-                safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="vae",
-                component_path="vae/",
-                storage_size=Memory.from_kb(0),
-                n_layers=None,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-        ],
-    ),
-    "qwen-image": ModelCard(
-        model_id=ModelId("exolabs/Qwen-Image"),
-        storage_size=Memory.from_bytes(16584333312 + 40860802176),
-        n_layers=60,
-        hidden_size=1,
-        supports_tensor=False,
-        tasks=[ModelTask.TextToImage],
-        uses_cfg=True,
-        components=[
-            ComponentInfo(
-                component_name="text_encoder",
-                component_path="text_encoder/",
-                storage_size=Memory.from_bytes(16584333312),
-                n_layers=12,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-            ComponentInfo(
-                component_name="transformer",
-                component_path="transformer/",
-                storage_size=Memory.from_bytes(40860802176),
-                n_layers=60,
-                can_shard=True,
-                safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="vae",
-                component_path="vae/",
-                storage_size=Memory.from_kb(0),
-                n_layers=None,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-        ],
-    ),
-    "qwen-image-edit-2509": ModelCard(
-        model_id=ModelId("exolabs/Qwen-Image-Edit-2509"),
-        storage_size=Memory.from_bytes(16584333312 + 40860802176),
-        n_layers=60,
-        hidden_size=1,
-        supports_tensor=False,
-        tasks=[ModelTask.ImageToImage],
-        uses_cfg=True,
-        components=[
-            ComponentInfo(
-                component_name="text_encoder",
-                component_path="text_encoder/",
-                storage_size=Memory.from_bytes(16584333312),
-                n_layers=12,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-            ComponentInfo(
-                component_name="transformer",
-                component_path="transformer/",
-                storage_size=Memory.from_bytes(40860802176),
-                n_layers=60,
-                can_shard=True,
-                safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="vae",
-                component_path="vae/",
-                storage_size=Memory.from_kb(0),
-                n_layers=None,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-        ],
-    ),
-}
-
-
-def _generate_image_model_quant_variants(
+# TODO: quantizing and dynamically creating model cards
+def _generate_image_model_quant_variants(  # pyright: ignore[reportUnusedFunction]
     base_name: str,
     base_card: ModelCard,
 ) -> dict[str, ModelCard]:
@@ -684,7 +185,6 @@ def _generate_image_model_quant_variants(
             hidden_size=base_card.hidden_size,
             supports_tensor=base_card.supports_tensor,
             tasks=base_card.tasks,
-            uses_cfg=base_card.uses_cfg,
             components=with_transformer_size(transformer_bytes),
         )
     }
@@ -704,22 +204,12 @@ def _generate_image_model_quant_variants(
             hidden_size=base_card.hidden_size,
             supports_tensor=base_card.supports_tensor,
             tasks=base_card.tasks,
-            uses_cfg=base_card.uses_cfg,
             components=with_transformer_size(quant_transformer_bytes),
         )
 
     return variants
 
 
-_image_model_cards: dict[str, ModelCard] = {}
-for _base_name, _base_card in _IMAGE_BASE_MODEL_CARDS.items():
-    _image_model_cards |= _generate_image_model_quant_variants(_base_name, _base_card)
-_IMAGE_MODEL_CARDS = _image_model_cards
-
-if EXO_ENABLE_IMAGE_MODELS:
-    MODEL_CARDS.update(_IMAGE_MODEL_CARDS)
-
-
 class ConfigData(BaseModel):
     model_config = {"extra": "ignore"}  # Allow unknown fields
 

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

@@ -57,62 +57,8 @@ class PipelineShardMetadata(BaseShardMetadata):
 
     Layers are represented as a half-open interval [start_layer, end_layer),
     where start_layer is inclusive and end_layer is exclusive.
-
-    CFG parallelism fields:
-    - cfg_rank: 0 = positive branch, 1 = negative branch (or 0 if no CFG parallel)
-    - cfg_world_size: 1 = sequential CFG, 2 = parallel CFG
-
-    Communication rank fields (explicit to support ring topology):
-    - next_pipeline_device: device to send to in pipeline forward pass
-    - prev_pipeline_device: device to receive from in pipeline forward pass
-    - cfg_peer_device: device for CFG exchange (last stage only)
-    - first_pipeline_device: device of first stage in same CFG group (for latent return)
     """
 
-    cfg_rank: int = 0
-    cfg_world_size: int = 1
-
-    # Explicit pipeline position (CFG group 1 uses reversed pipeline order)
-    explicit_pipeline_rank: int | None = None
-
-    next_pipeline_device: int | None = None
-    prev_pipeline_device: int | None = None
-    cfg_peer_device: int | None = None
-    first_pipeline_device: int | None = None
-    last_pipeline_device: int | None = None
-
-    @property
-    def pipeline_world_size(self) -> int:
-        return self.world_size // self.cfg_world_size
-
-    @property
-    def pipeline_rank(self) -> int:
-        if self.explicit_pipeline_rank is not None:
-            return self.explicit_pipeline_rank
-        return self.device_rank % self.pipeline_world_size
-
-    @property
-    def is_pipeline_first(self) -> bool:
-        return self.pipeline_rank == 0
-
-    @property
-    def is_pipeline_last(self) -> bool:
-        return self.pipeline_rank == self.pipeline_world_size - 1
-
-    def __hash__(self) -> int:
-        return hash(
-            (
-                self.model_card.model_id,
-                self.start_layer,
-                self.end_layer,
-                self.n_layers,
-                self.device_rank,
-                self.world_size,
-                self.cfg_rank,
-                self.cfg_world_size,
-            )
-        )
-
 
 class TensorShardMetadata(BaseShardMetadata):
     pass

src/exo/utils/dashboard_path.py

@@ -1,31 +1,45 @@
-import os
 import sys
 from pathlib import Path
 from typing import cast
 
 
+def find_resources() -> Path:
+    resources = _find_resources_in_repo() or _find_resources_in_bundle()
+    if resources is None:
+        raise FileNotFoundError(
+            "Unable to locate resources. Did you clone the repo properly?"
+        )
+    return resources
+
+
+def _find_resources_in_repo() -> Path | None:
+    current_module = Path(__file__).resolve()
+    for parent in current_module.parents:
+        build = parent / "resources"
+        if build.is_dir():
+            return build
+    return None
+
+
+def _find_resources_in_bundle() -> Path | None:
+    frozen_root = cast(str | None, getattr(sys, "_MEIPASS", None))
+    if frozen_root is None:
+        return None
+    candidate = Path(frozen_root) / "resources"
+    if candidate.is_dir():
+        return candidate
+    return None
+
+
 def find_dashboard() -> Path:
-    dashboard = (
-        _find_dashboard_in_env()
-        or _find_dashboard_in_repo()
-        or _find_dashboard_in_bundle()
-    )
+    dashboard = _find_dashboard_in_repo() or _find_dashboard_in_bundle()
     if not dashboard:
         raise FileNotFoundError(
-            "Unable to locate dashboard assets - make sure the dashboard has been built, or export DASHBOARD_DIR if you've built the dashboard elsewhere."
+            "Unable to locate dashboard assets - you probably forgot to run `cd dashboard && npm install && npm run build && cd ..`"
         )
     return dashboard
 
 
-def _find_dashboard_in_env() -> Path | None:
-    env = os.environ.get("DASHBOARD_DIR")
-    if not env:
-        return None
-    resolved_env = Path(env).expanduser().resolve()
-
-    return resolved_env
-
-
 def _find_dashboard_in_repo() -> Path | None:
     current_module = Path(__file__).resolve()
     for parent in current_module.parents:

src/exo/worker/engines/image/distributed_model.py

@@ -37,12 +37,7 @@ class DistributedImageModel:
         config = get_config_for_model(model_id)
         adapter = create_adapter_for_model(config, model_id, local_path, quantize)
 
-        has_layer_sharding = (
-            shard_metadata.start_layer != 0
-            or shard_metadata.end_layer != shard_metadata.n_layers
-        )
-
-        if group is not None and has_layer_sharding:
+        if group is not None:
             adapter.slice_transformer_blocks(
                 start_layer=shard_metadata.start_layer,
                 end_layer=shard_metadata.end_layer,

src/exo/worker/engines/image/generate.py

@@ -98,8 +98,8 @@ def generate_image(
 
     partial_images = (
         task.partial_images
-        if task.partial_images is not None and task.stream is not None and task.stream
-        else 0
+        if task.partial_images is not None
+        else (3 if task.stream else 0)
     )
 
     image_path: Path | None = None

src/exo/worker/engines/image/models/base.py

@@ -86,27 +86,6 @@ class PromptData(ABC):
         """
         ...
 
-    @abstractmethod
-    def get_cfg_branch_data(
-        self, positive: bool
-    ) -> tuple[mx.array, mx.array | None, mx.array | None, mx.array | None]:
-        """Get embeddings for a single CFG branch (positive or negative).
-
-        Used for sequential CFG and CFG parallel modes where we process
-        one branch at a time instead of batching.
-
-        Args:
-            positive: True for positive prompt, False for negative prompt
-
-        Returns:
-            Tuple of:
-            - embeds: [1, seq, hidden] prompt embeddings
-            - mask: [1, seq] attention mask or None
-            - pooled: [1, hidden] pooled embeddings or None
-            - conditioning_latents: [1, latent_seq, latent_dim] or None
-        """
-        ...
-
 
 class ModelAdapter(ABC, Generic[ModelT, TransformerT]):
     _config: ImageModelConfig

src/exo/worker/engines/image/models/flux/adapter.py

@@ -64,12 +64,6 @@ class FluxPromptData(PromptData):
     ) -> tuple[mx.array, mx.array, mx.array | None, mx.array | None] | None:
         return None
 
-    def get_cfg_branch_data(
-        self, positive: bool
-    ) -> tuple[mx.array, mx.array | None, mx.array | None, mx.array | None]:
-        """Flux doesn't use CFG, but we return positive data for compatibility."""
-        return (self._prompt_embeds, None, self._pooled_prompt_embeds, None)
-
 
 class FluxModelAdapter(ModelAdapter[Flux1, Transformer]):
     def __init__(

src/exo/worker/engines/image/models/qwen/adapter.py

@@ -133,24 +133,6 @@ class QwenPromptData(PromptData):
 
         return batched_embeds, batched_mask, None, cond_latents
 
-    def get_cfg_branch_data(
-        self, positive: bool
-    ) -> tuple[mx.array, mx.array | None, mx.array | None, mx.array | None]:
-        if positive:
-            return (
-                self._prompt_embeds,
-                self._prompt_mask,
-                None,
-                self.conditioning_latents,
-            )
-        else:
-            return (
-                self._negative_prompt_embeds,
-                self._negative_prompt_mask,
-                None,
-                self.conditioning_latents,
-            )
-
 
 class QwenModelAdapter(ModelAdapter[QwenImage, QwenTransformer]):
     """Adapter for Qwen-Image model.

src/exo/worker/engines/image/models/qwen/edit_adapter.py

@@ -153,24 +153,6 @@ class QwenEditPromptData(PromptData):
 
         return batched_embeds, batched_mask, None, batched_cond_latents
 
-    def get_cfg_branch_data(
-        self, positive: bool
-    ) -> tuple[mx.array, mx.array | None, mx.array | None, mx.array | None]:
-        if positive:
-            return (
-                self._prompt_embeds,
-                self._prompt_mask,
-                None,
-                self._conditioning_latents,
-            )
-        else:
-            return (
-                self._negative_prompt_embeds,
-                self._negative_prompt_mask,
-                None,
-                self._conditioning_latents,
-            )
-
 
 class QwenEditModelAdapter(ModelAdapter[QwenImageEdit, QwenTransformer]):
     """Adapter for Qwen-Image-Edit model.

src/exo/worker/engines/image/pipeline/runner.py

@@ -1,7 +1,5 @@
-from collections.abc import Iterator
-from dataclasses import dataclass
 from math import ceil
-from typing import Any, Optional, final
+from typing import Any, Optional
 
 import mlx.core as mx
 from mflux.models.common.config.config import Config
@@ -22,16 +20,6 @@ from exo.worker.engines.image.pipeline.block_wrapper import (
 )
 
 
-@final
-@dataclass
-class CfgBranch:
-    positive: bool
-    embeds: mx.array
-    mask: mx.array | None
-    pooled: mx.array | None
-    cond_latents: mx.array | None
-
-
 def calculate_patch_heights(
     latent_height: int, num_patches: int
 ) -> tuple[list[int], int]:
@@ -84,11 +72,22 @@ class DiffusionRunner:
         self.adapter = adapter
         self.group = group
 
-        self._init_cfg_topology(shard_metadata)
+        if group is None:
+            self.rank = 0
+            self.world_size = 1
+            self.next_rank = 0
+            self.prev_rank = 0
+            self.start_layer = 0
+            self.end_layer = config.total_blocks
+        else:
+            self.rank = shard_metadata.device_rank
+            self.world_size = shard_metadata.world_size
+            self.next_rank = (self.rank + 1) % self.world_size
+            self.prev_rank = (self.rank - 1 + self.world_size) % self.world_size
+            self.start_layer = shard_metadata.start_layer
+            self.end_layer = shard_metadata.end_layer
 
-        self.num_patches = (
-            num_patches if num_patches else max(1, self.pipeline_world_size)
-        )
+        self.num_patches = num_patches if num_patches else max(1, self.world_size)
 
         self.total_joint = config.joint_block_count
         self.total_single = config.single_block_count
@@ -98,48 +97,6 @@ class DiffusionRunner:
 
         self._compute_assigned_blocks()
 
-    def _init_cfg_topology(self, shard_metadata: PipelineShardMetadata) -> None:
-        """Initialize CFG and pipeline topology from shard metadata."""
-        if self.group is None:
-            self.rank = 0
-            self.world_size = 1
-            self.start_layer = 0
-            self.end_layer = self.config.total_blocks
-
-            self.cfg_rank = 0
-            self.cfg_world_size = 1
-            self.cfg_parallel = False
-
-            self.pipeline_world_size = 1
-            self.pipeline_rank = 0
-
-            self.next_pipeline_rank: int | None = None
-            self.prev_pipeline_rank: int | None = None
-            self.cfg_peer_rank: int | None = None
-            self.first_pipeline_rank: int = 0
-            self.last_pipeline_rank: int = 0
-        else:
-            self.rank = shard_metadata.device_rank
-            self.world_size = shard_metadata.world_size
-            self.start_layer = shard_metadata.start_layer
-            self.end_layer = shard_metadata.end_layer
-
-            self.cfg_rank = shard_metadata.cfg_rank
-            self.cfg_world_size = shard_metadata.cfg_world_size
-            self.cfg_parallel = self.cfg_world_size > 1
-
-            self.pipeline_world_size = shard_metadata.pipeline_world_size
-            self.pipeline_rank = shard_metadata.pipeline_rank
-
-            self.next_pipeline_rank = shard_metadata.next_pipeline_device
-            self.prev_pipeline_rank = shard_metadata.prev_pipeline_device
-            self.cfg_peer_rank = shard_metadata.cfg_peer_device
-
-            assert shard_metadata.first_pipeline_device is not None
-            assert shard_metadata.last_pipeline_device is not None
-            self.first_pipeline_rank = shard_metadata.first_pipeline_device
-            self.last_pipeline_rank = shard_metadata.last_pipeline_device
-
     def _compute_assigned_blocks(self) -> None:
         """Determine which joint/single blocks this stage owns."""
         start = self.start_layer
@@ -176,11 +133,11 @@ class DiffusionRunner:
 
     @property
     def is_first_stage(self) -> bool:
-        return self.pipeline_rank == 0
+        return self.rank == 0
 
     @property
     def is_last_stage(self) -> bool:
-        return self.pipeline_rank == self.pipeline_world_size - 1
+        return self.rank == self.world_size - 1
 
     @property
     def is_distributed(self) -> bool:
@@ -191,97 +148,6 @@ class DiffusionRunner:
             return self._guidance_override
         return self.config.guidance_scale
 
-    def _get_cfg_branches(self, prompt_data: PromptData) -> Iterator[CfgBranch]:
-        """Yield the CFG branches this node should process.
-
-        - No CFG: yields one branch (positive)
-        - CFG parallel: yields one branch (our assigned branch)
-        - Sequential CFG: yields two branches (positive, then negative)
-        """
-        if not self.adapter.needs_cfg:
-            embeds, mask, pooled, cond = prompt_data.get_cfg_branch_data(positive=True)
-            yield CfgBranch(
-                positive=True,
-                embeds=embeds,
-                mask=mask,
-                pooled=pooled,
-                cond_latents=cond,
-            )
-        elif self.cfg_parallel:
-            positive = self.cfg_rank == 0
-            embeds, mask, pooled, cond = prompt_data.get_cfg_branch_data(positive)
-            yield CfgBranch(
-                positive=positive,
-                embeds=embeds,
-                mask=mask,
-                pooled=pooled,
-                cond_latents=cond,
-            )
-        else:
-            pos_embeds, pos_mask, pos_pooled, pos_cond = (
-                prompt_data.get_cfg_branch_data(positive=True)
-            )
-            yield CfgBranch(
-                positive=True,
-                embeds=pos_embeds,
-                mask=pos_mask,
-                pooled=pos_pooled,
-                cond_latents=pos_cond,
-            )
-            neg_embeds, neg_mask, neg_pooled, neg_cond = (
-                prompt_data.get_cfg_branch_data(positive=False)
-            )
-            yield CfgBranch(
-                positive=False,
-                embeds=neg_embeds,
-                mask=neg_mask,
-                pooled=neg_pooled,
-                cond_latents=neg_cond,
-            )
-
-    def _combine_cfg_results(self, results: list[tuple[bool, mx.array]]) -> mx.array:
-        if len(results) == 1:
-            positive, noise = results[0]
-            if self.cfg_parallel and self.is_last_stage:
-                # TODO(ciaran): try to remove
-                mx.eval(noise)
-                return self._exchange_and_apply_guidance(noise, positive)
-            return noise
-
-        noise_neg = next(n for p, n in results if not p)
-        noise_pos = next(n for p, n in results if p)
-        return self._apply_guidance(noise_pos, noise_neg)
-
-    def _exchange_and_apply_guidance(
-        self, noise: mx.array, is_positive: bool
-    ) -> mx.array:
-        assert self.group is not None
-        assert self.cfg_peer_rank is not None
-
-        if is_positive:
-            noise = mx.distributed.send(noise, self.cfg_peer_rank, group=self.group)
-            mx.async_eval(noise)
-            noise_neg = mx.distributed.recv_like(
-                noise, self.cfg_peer_rank, group=self.group
-            )
-            mx.eval(noise_neg)
-            noise_pos = noise
-        else:
-            noise_pos = mx.distributed.recv_like(
-                noise, self.cfg_peer_rank, group=self.group
-            )
-            mx.eval(noise_pos)
-            noise = mx.distributed.send(noise, self.cfg_peer_rank, group=self.group)
-            mx.async_eval(noise)
-            noise_neg = noise
-
-        return self._apply_guidance(noise_pos, noise_neg)
-
-    def _apply_guidance(self, noise_pos: mx.array, noise_neg: mx.array) -> mx.array:
-        scale = self._get_effective_guidance_scale()
-        assert scale is not None
-        return self.adapter.apply_guidance(noise_pos, noise_neg, scale)
-
     def _ensure_wrappers(
         self,
         text_seq_len: int,
@@ -482,7 +348,6 @@ class DiffusionRunner:
                 ctx.in_loop(  # pyright: ignore[reportAny]
                     t=t,
                     latents=latents,
-                    time_steps=time_steps,
                 )
 
                 mx.eval(latents)
@@ -598,9 +463,7 @@ class DiffusionRunner:
     ) -> mx.array:
         if self.group is None:
             return self._single_node_step(t, config, latents, prompt_data)
-        elif (
-            self.pipeline_world_size == 1 or t < config.init_time_step + num_sync_steps
-        ):
+        elif t < config.init_time_step + num_sync_steps:
             return self._sync_pipeline_step(
                 t,
                 config,
@@ -624,29 +487,42 @@ class DiffusionRunner:
         prompt_data: PromptData,
     ) -> mx.array:
         cond_image_grid = prompt_data.cond_image_grid
-        results: list[tuple[bool, mx.array]] = []
-
-        for branch in self._get_cfg_branches(prompt_data):
-            # Reset caches before each branch to ensure no state contamination
-            self._reset_all_caches()
+        needs_cfg = self.adapter.needs_cfg
 
+        if needs_cfg:
+            batched_data = prompt_data.get_batched_cfg_data()
+            assert batched_data is not None, "CFG model must provide batched data"
+            prompt_embeds, encoder_mask, batched_pooled, cond_latents = batched_data
             pooled_embeds = (
-                branch.pooled if branch.pooled is not None else branch.embeds
+                batched_pooled if batched_pooled is not None else prompt_embeds
+            )
+            step_latents = mx.concatenate([latents, latents], axis=0)
+        else:
+            prompt_embeds = prompt_data.prompt_embeds
+            pooled_embeds = prompt_data.pooled_prompt_embeds
+            encoder_mask = prompt_data.get_encoder_hidden_states_mask(positive=True)
+            cond_latents = prompt_data.conditioning_latents
+            step_latents = latents
+
+        noise = self._forward_pass(
+            step_latents,
+            prompt_embeds,
+            pooled_embeds,
+            t=t,
+            config=config,
+            encoder_hidden_states_mask=encoder_mask,
+            cond_image_grid=cond_image_grid,
+            conditioning_latents=cond_latents,
+        )
+
+        if needs_cfg:
+            noise_pos, noise_neg = mx.split(noise, 2, axis=0)
+            guidance_scale = self._get_effective_guidance_scale()
+            assert guidance_scale is not None
+            noise = self.adapter.apply_guidance(
+                noise_pos, noise_neg, guidance_scale=guidance_scale
             )
 
-            noise = self._forward_pass(
-                latents,
-                branch.embeds,
-                pooled_embeds,
-                t=t,
-                config=config,
-                encoder_hidden_states_mask=branch.mask,
-                cond_image_grid=cond_image_grid,
-                conditioning_latents=branch.cond_latents,
-            )
-            results.append((branch.positive, noise))
-
-        noise = self._combine_cfg_results(results)
         return config.scheduler.step(noise=noise, timestep=t, latents=latents)  # pyright: ignore[reportAny]
 
     def _create_patches(
@@ -697,7 +573,7 @@ class DiffusionRunner:
             )
 
         text_embeddings = self.adapter.compute_text_embeddings(
-            t, config, pooled_prompt_embeds, hidden_states=hidden_states
+            t, config, pooled_prompt_embeds
         )
         image_rotary_embeddings = self.adapter.compute_rotary_embeddings(
             prompt_embeds,
@@ -709,17 +585,16 @@ class DiffusionRunner:
 
         if self.has_joint_blocks:
             if not self.is_first_stage:
-                assert self.prev_pipeline_rank is not None
                 hidden_states = mx.distributed.recv(
                     (batch_size, num_img_tokens, hidden_dim),
                     dtype,
-                    self.prev_pipeline_rank,
+                    self.prev_rank,
                     group=self.group,
                 )
                 encoder_hidden_states = mx.distributed.recv(
                     (batch_size, text_seq_len, hidden_dim),
                     dtype,
-                    self.prev_pipeline_rank,
+                    self.prev_rank,
                     group=self.group,
                 )
                 mx.eval(hidden_states, encoder_hidden_states)
@@ -744,30 +619,27 @@ class DiffusionRunner:
             if self.has_single_blocks or self.is_last_stage:
                 hidden_states = concatenated
             else:
-                assert self.next_pipeline_rank is not None
                 concatenated = mx.distributed.send(
-                    concatenated, self.next_pipeline_rank, group=self.group
+                    concatenated, self.next_rank, group=self.group
                 )
                 mx.async_eval(concatenated)
 
         elif self.has_joint_blocks and not self.is_last_stage:
             assert encoder_hidden_states is not None
-            assert self.next_pipeline_rank is not None
             hidden_states = mx.distributed.send(
-                hidden_states, self.next_pipeline_rank, group=self.group
+                hidden_states, self.next_rank, group=self.group
             )
             encoder_hidden_states = mx.distributed.send(
-                encoder_hidden_states, self.next_pipeline_rank, group=self.group
+                encoder_hidden_states, self.next_rank, group=self.group
             )
             mx.async_eval(hidden_states, encoder_hidden_states)
 
         if self.has_single_blocks:
             if not self.owns_concat_stage and not self.is_first_stage:
-                assert self.prev_pipeline_rank is not None
                 hidden_states = mx.distributed.recv(
                     (batch_size, text_seq_len + num_img_tokens, hidden_dim),
                     dtype,
-                    self.prev_pipeline_rank,
+                    self.prev_rank,
                     group=self.group,
                 )
                 mx.eval(hidden_states)
@@ -782,9 +654,8 @@ class DiffusionRunner:
                 )
 
             if not self.is_last_stage:
-                assert self.next_pipeline_rank is not None
                 hidden_states = mx.distributed.send(
-                    hidden_states, self.next_pipeline_rank, group=self.group
+                    hidden_states, self.next_rank, group=self.group
                 )
                 mx.async_eval(hidden_states)
 
@@ -807,65 +678,75 @@ class DiffusionRunner:
         kontext_image_ids: mx.array | None = None,
     ) -> mx.array:
         prev_latents = hidden_states
+        needs_cfg = self.adapter.needs_cfg
         cond_image_grid = prompt_data.cond_image_grid
 
         scaled_hidden_states = config.scheduler.scale_model_input(hidden_states, t)  # pyright: ignore[reportAny]
         original_latent_tokens: int = scaled_hidden_states.shape[1]  # pyright: ignore[reportAny]
 
-        results: list[tuple[bool, mx.array]] = []
-
-        for branch in self._get_cfg_branches(prompt_data):
+        if needs_cfg:
+            batched_data = prompt_data.get_batched_cfg_data()
+            assert batched_data is not None, "CFG model must provide batched data"
+            prompt_embeds, encoder_mask, batched_pooled, cond_latents = batched_data
             pooled_embeds = (
-                branch.pooled if branch.pooled is not None else branch.embeds
+                batched_pooled if batched_pooled is not None else prompt_embeds
             )
-
-            cond_latents = branch.cond_latents
-            if cond_latents is not None:
-                num_img_tokens: int = original_latent_tokens + cond_latents.shape[1]
-            else:
-                num_img_tokens = original_latent_tokens
-
-            step_latents: mx.array = scaled_hidden_states  # pyright: ignore[reportAny]
-            if self.is_first_stage and cond_latents is not None:
-                step_latents = mx.concatenate([step_latents, cond_latents], axis=1)
-
-            text_seq_len = branch.embeds.shape[1]
-            self._ensure_wrappers(text_seq_len, branch.mask)
-
-            noise = self._run_sync_pass(
-                t,
-                config,
-                step_latents,
-                branch.embeds,
-                pooled_embeds,
-                branch.mask,
-                cond_image_grid,
-                kontext_image_ids,
-                num_img_tokens,
-                original_latent_tokens,
-                cond_latents,
+            step_latents = mx.concatenate(
+                [scaled_hidden_states, scaled_hidden_states], axis=0
             )
+        else:
+            prompt_embeds = prompt_data.prompt_embeds
+            pooled_embeds = prompt_data.pooled_prompt_embeds
+            encoder_mask = prompt_data.get_encoder_hidden_states_mask(positive=True)
+            cond_latents = prompt_data.conditioning_latents
+            step_latents = scaled_hidden_states  # pyright: ignore[reportAny]
 
-            if self.is_last_stage:
-                assert noise is not None
-                results.append((branch.positive, noise))
+        if cond_latents is not None:
+            num_img_tokens: int = original_latent_tokens + cond_latents.shape[1]
+        else:
+            num_img_tokens = original_latent_tokens
+
+        if self.is_first_stage and cond_latents is not None:
+            step_latents = mx.concatenate([step_latents, cond_latents], axis=1)
+
+        text_seq_len = prompt_embeds.shape[1]
+        self._ensure_wrappers(text_seq_len, encoder_mask)
+
+        noise = self._run_sync_pass(
+            t,
+            config,
+            step_latents,
+            prompt_embeds,
+            pooled_embeds,
+            encoder_mask,
+            cond_image_grid,
+            kontext_image_ids,
+            num_img_tokens,
+            original_latent_tokens,
+            cond_latents,
+        )
 
         if self.is_last_stage:
-            noise = self._combine_cfg_results(results)
+            assert noise is not None
+            if needs_cfg:
+                noise_pos, noise_neg = mx.split(noise, 2, axis=0)
+                guidance_scale = self._get_effective_guidance_scale()
+                assert guidance_scale is not None
+                noise = self.adapter.apply_guidance(
+                    noise_pos, noise_neg, guidance_scale
+                )
 
             hidden_states = config.scheduler.step(  # pyright: ignore[reportAny]
                 noise=noise, timestep=t, latents=prev_latents
             )
 
             if not self.is_first_stage:
-                hidden_states = mx.distributed.send(
-                    hidden_states, self.first_pipeline_rank, group=self.group
-                )
+                hidden_states = mx.distributed.send(hidden_states, 0, group=self.group)
                 mx.async_eval(hidden_states)
 
         elif self.is_first_stage:
             hidden_states = mx.distributed.recv_like(
-                prev_latents, src=self.last_pipeline_rank, group=self.group
+                prev_latents, src=self.world_size - 1, group=self.group
             )
             mx.eval(hidden_states)
 
@@ -884,10 +765,39 @@ class DiffusionRunner:
         kontext_image_ids: mx.array | None = None,
     ) -> mx.array:
         patch_latents, token_indices = self._create_patches(latents, config)
+        needs_cfg = self.adapter.needs_cfg
         cond_image_grid = prompt_data.cond_image_grid
 
-        prev_patch_latents = [p for p in patch_latents]
+        if needs_cfg:
+            batched_data = prompt_data.get_batched_cfg_data()
+            assert batched_data is not None, "CFG model must provide batched data"
+            prompt_embeds, encoder_mask, batched_pooled, _ = batched_data
+            pooled_embeds = (
+                batched_pooled if batched_pooled is not None else prompt_embeds
+            )
+        else:
+            prompt_embeds = prompt_data.prompt_embeds
+            pooled_embeds = prompt_data.pooled_prompt_embeds
+            encoder_mask = prompt_data.get_encoder_hidden_states_mask(positive=True)
 
+        text_seq_len = prompt_embeds.shape[1]
+        self._ensure_wrappers(text_seq_len, encoder_mask)
+        self._set_text_seq_len(text_seq_len)
+
+        if self.joint_block_wrappers:
+            for wrapper in self.joint_block_wrappers:
+                wrapper.set_encoder_mask(encoder_mask)
+
+        text_embeddings = self.adapter.compute_text_embeddings(t, config, pooled_embeds)
+        image_rotary_embeddings = self.adapter.compute_rotary_embeddings(
+            prompt_embeds,
+            config,
+            encoder_hidden_states_mask=encoder_mask,
+            cond_image_grid=cond_image_grid,
+            kontext_image_ids=kontext_image_ids,
+        )
+
+        prev_patch_latents = [p for p in patch_latents]
         encoder_hidden_states: mx.array | None = None
 
         for patch_idx in range(len(patch_latents)):
@@ -899,52 +809,31 @@ class DiffusionRunner:
                 and not is_first_async_step
             ):
                 patch = mx.distributed.recv_like(
-                    patch, src=self.last_pipeline_rank, group=self.group
+                    patch, src=self.prev_rank, group=self.group
                 )
                 mx.eval(patch)
 
-            results: list[tuple[bool, mx.array]] = []
+            step_patch = mx.concatenate([patch, patch], axis=0) if needs_cfg else patch
 
-            for branch in self._get_cfg_branches(prompt_data):
-                pooled_embeds = (
-                    branch.pooled if branch.pooled is not None else branch.embeds
-                )
-
-                text_seq_len = branch.embeds.shape[1]
-                self._ensure_wrappers(text_seq_len, branch.mask)
-                self._set_text_seq_len(text_seq_len)
-
-                if self.joint_block_wrappers:
-                    for wrapper in self.joint_block_wrappers:
-                        wrapper.set_encoder_mask(branch.mask)
-
-                text_embeddings = self.adapter.compute_text_embeddings(
-                    t, config, pooled_embeds
-                )
-                image_rotary_embeddings = self.adapter.compute_rotary_embeddings(
-                    branch.embeds,
-                    config,
-                    encoder_hidden_states_mask=branch.mask,
-                    cond_image_grid=cond_image_grid,
-                    kontext_image_ids=kontext_image_ids,
-                )
-
-                noise, encoder_hidden_states = self._run_single_patch_pass(
-                    patch=patch,
-                    patch_idx=patch_idx,
-                    token_indices=token_indices[patch_idx],
-                    prompt_embeds=branch.embeds,
-                    text_embeddings=text_embeddings,
-                    image_rotary_embeddings=image_rotary_embeddings,
-                    encoder_hidden_states=encoder_hidden_states,
-                )
-
-                if self.is_last_stage:
-                    assert noise is not None
-                    results.append((branch.positive, noise))
+            noise, encoder_hidden_states = self._run_single_patch_pass(
+                patch=step_patch,
+                patch_idx=patch_idx,
+                token_indices=token_indices[patch_idx],
+                prompt_embeds=prompt_embeds,
+                text_embeddings=text_embeddings,
+                image_rotary_embeddings=image_rotary_embeddings,
+                encoder_hidden_states=encoder_hidden_states,
+            )
 
             if self.is_last_stage:
-                noise = self._combine_cfg_results(results)
+                assert noise is not None
+                if needs_cfg:
+                    noise_pos, noise_neg = mx.split(noise, 2, axis=0)
+                    guidance_scale = self._get_effective_guidance_scale()
+                    assert guidance_scale is not None
+                    noise = self.adapter.apply_guidance(
+                        noise_pos, noise_neg, guidance_scale
+                    )
 
                 patch_latents[patch_idx] = config.scheduler.step(  # pyright: ignore[reportAny]
                     noise=noise,
@@ -954,9 +843,7 @@ class DiffusionRunner:
 
                 if not self.is_first_stage and t != config.num_inference_steps - 1:
                     patch_latents[patch_idx] = mx.distributed.send(
-                        patch_latents[patch_idx],
-                        self.first_pipeline_rank,
-                        group=self.group,
+                        patch_latents[patch_idx], self.next_rank, group=self.group
                     )
                     mx.async_eval(patch_latents[patch_idx])
 
@@ -996,12 +883,11 @@ class DiffusionRunner:
 
         if self.has_joint_blocks:
             if not self.is_first_stage:
-                assert self.prev_pipeline_rank is not None
                 patch_len = patch.shape[1]
                 patch = mx.distributed.recv(
                     (batch_size, patch_len, hidden_dim),
                     patch.dtype,
-                    self.prev_pipeline_rank,
+                    self.prev_rank,
                     group=self.group,
                 )
                 mx.eval(patch)
@@ -1010,7 +896,7 @@ class DiffusionRunner:
                     encoder_hidden_states = mx.distributed.recv(
                         (batch_size, text_seq_len, hidden_dim),
                         patch.dtype,
-                        self.prev_pipeline_rank,
+                        self.prev_rank,
                         group=self.group,
                     )
                     mx.eval(encoder_hidden_states)
@@ -1038,34 +924,29 @@ class DiffusionRunner:
             if self.has_single_blocks or self.is_last_stage:
                 patch = patch_concat
             else:
-                assert self.next_pipeline_rank is not None
                 patch_concat = mx.distributed.send(
-                    patch_concat, self.next_pipeline_rank, group=self.group
+                    patch_concat, self.next_rank, group=self.group
                 )
                 mx.async_eval(patch_concat)
 
         elif self.has_joint_blocks and not self.is_last_stage:
-            assert self.next_pipeline_rank is not None
-            patch = mx.distributed.send(
-                patch, self.next_pipeline_rank, group=self.group
-            )
+            patch = mx.distributed.send(patch, self.next_rank, group=self.group)
             mx.async_eval(patch)
 
             if patch_idx == 0:
                 assert encoder_hidden_states is not None
                 encoder_hidden_states = mx.distributed.send(
-                    encoder_hidden_states, self.next_pipeline_rank, group=self.group
+                    encoder_hidden_states, self.next_rank, group=self.group
                 )
                 mx.async_eval(encoder_hidden_states)
 
         if self.has_single_blocks:
             if not self.owns_concat_stage and not self.is_first_stage:
-                assert self.prev_pipeline_rank is not None
                 patch_len = patch.shape[1]
                 patch = mx.distributed.recv(
                     (batch_size, text_seq_len + patch_len, hidden_dim),
                     patch.dtype,
-                    self.prev_pipeline_rank,
+                    self.prev_rank,
                     group=self.group,
                 )
                 mx.eval(patch)
@@ -1080,10 +961,7 @@ class DiffusionRunner:
                 )
 
             if not self.is_last_stage:
-                assert self.next_pipeline_rank is not None
-                patch = mx.distributed.send(
-                    patch, self.next_pipeline_rank, group=self.group
-                )
+                patch = mx.distributed.send(patch, self.next_rank, group=self.group)
                 mx.async_eval(patch)
 
         noise: mx.array | None = None

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

@@ -201,9 +201,6 @@ def pipeline_auto_parallel(
     device_rank, world_size = model_shard_meta.device_rank, model_shard_meta.world_size
 
     layers = layers[start_layer:end_layer]
-    for layer in layers:
-        mx.eval(layer)  # type: ignore
-
     layers[0] = PipelineFirstLayer(layers[0], device_rank, group=group)
     layers[-1] = PipelineLastLayer(
         layers[-1],

src/exo/worker/runner/runner.py

@@ -61,7 +61,7 @@ from exo.shared.types.worker.runners import (
     RunnerStatus,
     RunnerWarmingUp,
 )
-from exo.shared.types.worker.shards import PipelineShardMetadata, ShardMetadata
+from exo.shared.types.worker.shards import ShardMetadata
 from exo.utils.channels import MpReceiver, MpSender
 from exo.worker.engines.image import (
     DistributedImageModel,
@@ -360,9 +360,8 @@ def main(
                         image_index = 0
                         for response in generate_image(model=model, task=task_params):
                             if (
-                                isinstance(shard_metadata, PipelineShardMetadata)
-                                and shard_metadata.is_pipeline_last
-                                and shard_metadata.cfg_rank == 0
+                                shard_metadata.device_rank
+                                == shard_metadata.world_size - 1
                             ):
                                 match response:
                                     case PartialImageResponse():
@@ -388,11 +387,7 @@ def main(
                                         image_index += 1
                     # can we make this more explicit?
                     except Exception as e:
-                        if (
-                            isinstance(shard_metadata, PipelineShardMetadata)
-                            and shard_metadata.is_pipeline_last
-                            and shard_metadata.cfg_rank == 0
-                        ):
+                        if shard_metadata.device_rank == shard_metadata.world_size - 1:
                             event_sender.send(
                                 ChunkGenerated(
                                     command_id=command_id,
@@ -424,9 +419,8 @@ def main(
                         image_index = 0
                         for response in generate_image(model=model, task=task_params):
                             if (
-                                isinstance(shard_metadata, PipelineShardMetadata)
-                                and shard_metadata.is_pipeline_last
-                                and shard_metadata.cfg_rank == 0
+                                shard_metadata.device_rank
+                                == shard_metadata.world_size - 1
                             ):
                                 match response:
                                     case PartialImageResponse():
@@ -451,11 +445,7 @@ def main(
                                         )
                                         image_index += 1
                     except Exception as e:
-                        if (
-                            isinstance(shard_metadata, PipelineShardMetadata)
-                            and shard_metadata.is_pipeline_last
-                            and shard_metadata.cfg_rank == 0
-                        ):
+                        if shard_metadata.device_rank == shard_metadata.world_size - 1:
                             event_sender.send(
                                 ChunkGenerated(
                                     command_id=command_id,

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

@@ -16,7 +16,7 @@ from exo.download.download_utils import (
     ensure_models_dir,
     fetch_file_list_with_cache,
 )
-from exo.shared.models.model_cards import MODEL_CARDS, ModelCard, ModelId
+from exo.shared.models.model_cards import ModelCard, ModelId, get_model_cards
 from exo.worker.engines.mlx.utils_mlx import (
     get_eos_token_ids_for_model,
     load_tokenizer_for_model_id,
@@ -76,7 +76,7 @@ def get_test_models() -> list[ModelCard]:
     """Get a representative sample of models to test."""
     # Pick one model from each family to test
     families: dict[str, ModelCard] = {}
-    for card in MODEL_CARDS.values():
+    for card in asyncio.run(get_model_cards()):
         # Extract family name (e.g., "llama-3.1" from "llama-3.1-8b")
         parts = card.model_id.short().split("-")
         family = "-".join(parts[:2]) if len(parts) >= 2 else parts[0]
@@ -296,7 +296,7 @@ async def test_tokenizer_special_tokens(model_card: ModelCard) -> None:
 async def test_kimi_tokenizer_specifically():
     """Test Kimi tokenizer with its specific patches and quirks."""
     kimi_models = [
-        card for card in MODEL_CARDS.values() if "kimi" in card.model_id.lower()
+        card for card in await get_model_cards() if "kimi" in card.model_id.lower()
     ]
 
     if not kimi_models:
@@ -343,7 +343,7 @@ async def test_kimi_tokenizer_specifically():
 async def test_glm_tokenizer_specifically():
     """Test GLM tokenizer with its specific EOS tokens."""
     glm_model_cards = [
-        card for card in MODEL_CARDS.values() if "glm" in card.model_id.lower()
+        card for card in await get_model_cards() if "glm" in card.model_id.lower()
     ]
 
     if not glm_model_cards:

tests/headless_runner.py

@@ -16,7 +16,7 @@ from exo.download.impl_shard_downloader import (
     exo_shard_downloader,
 )
 from exo.shared.logging import InterceptLogger, logger_setup
-from exo.shared.models.model_cards import MODEL_CARDS, ModelId
+from exo.shared.models.model_cards import ModelId
 from exo.shared.types.api import ChatCompletionMessage, ChatCompletionTaskParams
 from exo.shared.types.commands import CommandId
 from exo.shared.types.common import Host, NodeId
@@ -89,22 +89,26 @@ async def tb_detection():
 
 async def assert_downloads():
     sd = exo_shard_downloader()
-    # await sd.ensure_shard(await build_full_shard(MODEL_CARDS["qwen3-0.6b"].model_id))
     await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["llama-3.1-8b-bf16"].model_id)
-    )
-    await sd.ensure_shard(await build_full_shard(MODEL_CARDS["qwen3-30b"].model_id))
-    await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["gpt-oss-120b-MXFP4-Q8"].model_id)
+        await build_full_shard(ModelId("mlx-community/Qwen3-0.6B-4bit"))
     )
     await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["gpt-oss-20b-4bit"].model_id)
+        await build_full_shard(ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"))
     )
     await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["glm-4.7-8bit-gs32"].model_id)
+        await build_full_shard(ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"))
     )
     await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["minimax-m2.1-8bit"].model_id)
+        await build_full_shard(ModelId("mlx-community/gpt-oss-120b-MXFP4-Q8"))
+    )
+    await sd.ensure_shard(
+        await build_full_shard(ModelId("mlx-community/gpt-oss-20b-MXFP4-Q8"))
+    )
+    await sd.ensure_shard(
+        await build_full_shard(ModelId("mlx-community/GLM-4.7-8bit-gs32"))
+    )
+    await sd.ensure_shard(
+        await build_full_shard(ModelId("mlx-community/MiniMax-M2.1-8bit"))
     )