Merge branch 'main' into releases/v1.0.64

Use custom fork
2026-01-23 13:29:29 -05:00 · 2026-01-23 01:40:20 +00:00 · 2026-01-22 22:19:35 +00:00
17 changed files with 441 additions and 765 deletions
--- a/.mlx_typings/mlx_lm/models/cache.pyi
+++ b/.mlx_typings/mlx_lm/models/cache.pyi
@@ -2,14 +2,11 @@
 This type stub file was generated by pyright.
 """

+from typing import Any, Dict, List, Optional, Protocol, Literal, Self
+
 import mlx.nn as nn
-import mlx.core as mx
-from typing import Any, Dict, List, Literal, Optional, Protocol, Self
 from mlx.core import array
-
-"""
-This type stub file was generated by pyright.
-"""
+import mlx.core as mx

 class Cache(Protocol):
    keys: mx.array
@@ -35,7 +32,6 @@ def make_prompt_cache(
            ``make_cache`` method, a ``RotatingKVCache`` is used with a maximum
            size of ``max_kv_size``
    """
-    ...

 def save_prompt_cache(
    file_name: str, cache: List[Cache], metadata: Dict[str, str] = ...
@@ -49,7 +45,6 @@ def save_prompt_cache(
        metadata (Dict[str, str]): Optional metadata to save along with model
            state.
    """
-    ...

 def load_prompt_cache(file_name: str, return_metadata=...) -> array:
    """
@@ -64,15 +59,13 @@ def load_prompt_cache(file_name: str, return_metadata=...) -> array:
        List[Any] or Tuple[List[Any], Dict[str, str]]: The prompt cache and
            the metadata if requested.
    """
-    ...

-def can_trim_prompt_cache(cache: List[Any]) -> bool:
+def can_trim_prompt_cache(cache: List[Cache]) -> bool:
    """
    Check if model's cache can be trimmed.
    """
-    ...

-def trim_prompt_cache(cache: List[Any], num_tokens: int) -> int:
+def trim_prompt_cache(cache: List[Cache], num_tokens: int) -> List[Cache]:
    """
    Trim the model's cache by the given number of tokens.

@@ -86,7 +79,6 @@ def trim_prompt_cache(cache: List[Any], num_tokens: int) -> int:
    Returns:
        (int): The number of tokens that were trimmed.
    """
-    ...

 def create_attention_mask(
    N: int, offset: int, return_array: bool, window_size: Optional[int]
@@ -115,125 +107,164 @@ class ConcatenateKVCache(_BaseCache):
    KVCache with a larger step size before using this cache.
    """
    def __init__(self) -> None: ...
-    def update_and_fetch(self, keys, values): ...
+    def update_and_fetch(self, keys, values):  # -> tuple[Any | array, Any | array]:
+        ...
    @property
-    def state(self): ...
+    def state(self):  # -> tuple[Any | array | None, Any | array | None]:
+        ...
    @state.setter
-    def state(self, v): ...
-    def is_trimmable(self): ...
-    def trim(self, n): ...
-    def make_mask(self, *args, **kwargs): ...
+    def state(self, v):  # -> None:
+        ...
+    def is_trimmable(self):  # -> Literal[True]:
+        ...
+    def trim(self, n):  # -> int:
+        ...
+    def make_mask(self, *args, **kwargs):  # -> array | Literal['causal'] | None:
+        ...

 class QuantizedKVCache(_BaseCache):
    step = ...
-    offset: int
    def __init__(self, group_size: int = ..., bits: int = ...) -> None: ...
-    def update_and_fetch(self, keys, values): ...
+    def update_and_fetch(self, keys, values):  # -> Any:
+        ...
    @property
-    def state(self): ...
+    def state(
+        self,
+    ):  # -> tuple[Any | tuple[array, array, array] | None, Any | tuple[array, array, array] | None] | Any:
+        ...
    @state.setter
-    def state(self, v): ...
+    def state(self, v):  # -> None:
+        ...
    @property
-    def meta_state(self): ...
+    def meta_state(self):  # -> tuple[str, ...]:
+        ...
    @meta_state.setter
-    def meta_state(self, v): ...
-    def is_trimmable(self): ...
-    def trim(self, n): ...
-    def make_mask(self, *args, **kwargs): ...
+    def meta_state(self, v):  # -> None:
+        ...
+    def is_trimmable(self):  # -> Literal[True]:
+        ...
+    def trim(self, n):  # -> int:
+        ...
+    def make_mask(self, *args, **kwargs):  # -> array | Literal['causal'] | None:
+        ...

 class KVCache(_BaseCache):
    step = ...
-    offset: int
    def __init__(self) -> None: ...
-    def update_and_fetch(self, keys, values): ...
+    def update_and_fetch(self, keys, values):  # -> tuple[array | Any, array | Any]:
+        ...
    @property
-    def state(self) -> tuple[array, array]: ...
+    def state(
+        self,
+    ) -> tuple[array, array]: ...
    @state.setter
    def state(self, v) -> None: ...
-    def is_trimmable(self): ...
-    def trim(self, n): ...
+    def is_trimmable(self):  # -> Literal[True]:
+        ...
+    def trim(self, n):  # -> int:
+        ...
    def to_quantized(
        self, group_size: int = ..., bits: int = ...
    ) -> QuantizedKVCache: ...
-    def make_mask(self, *args, **kwargs): ...
+    def make_mask(self, *args, **kwargs):  # -> array | Literal['causal'] | None:
+        ...

 class RotatingKVCache(_BaseCache):
    step = ...
-    offset: int
    def __init__(self, max_size, keep=...) -> None: ...
-    def update_and_fetch(self, keys, values): ...
+    def update_and_fetch(
+        self, keys, values
+    ):  # -> tuple[array | Any, array | Any] | tuple[array | Any, array | Any | None]:
+        ...
    @property
-    def state(self): ...
+    def state(
+        self,
+    ):  # -> tuple[Any | array, Any | array] | tuple[Any | array | None, Any | array | None]:
+        ...
    @state.setter
-    def state(self, v): ...
+    def state(self, v):  # -> None:
+        ...
    @property
-    def meta_state(self): ...
+    def meta_state(self):  # -> tuple[str, ...]:
+        ...
    @meta_state.setter
-    def meta_state(self, v): ...
-    def is_trimmable(self): ...
-    def trim(self, n): ...
+    def meta_state(self, v):  # -> None:
+        ...
+    def is_trimmable(self):  # -> bool:
+        ...
+    def trim(self, n):  # -> int:
+        ...
    def to_quantized(
        self, group_size: int = ..., bits: int = ...
    ) -> QuantizedKVCache: ...
    def make_mask(
        self, N: int, window_size: Optional[int] = ..., return_array: bool = ...
-    ): ...
+    ):  # -> array | Literal['causal'] | None:
+        ...

 class ArraysCache(_BaseCache):
    def __init__(self, size, left_padding: Optional[List[int]] = ...) -> None: ...
-    def __setitem__(self, idx, value): ...
+    def __setitem__(self, idx, value):  # -> None:
+        ...
    def __getitem__(self, idx): ...
    @property
-    def state(self): ...
+    def state(self):  # -> list[Any | array] | list[array]:
+        ...
    @state.setter
-    def state(self, v): ...
-    def filter(self, batch_indices):
+    def state(self, v):  # -> None:
+        ...
+    def filter(self, batch_indices):  # -> None:
        """
        In-place filter to keep just the given indices in the cache.
        """
-        ...

-    def extend(self, other):
+    def extend(self, other):  # -> None:
        """
        In-place extend this cache with the other cache.
        """
-        ...

-    def make_mask(self, N: int): ...
+    def make_mask(self, N: int):  # -> array | None:
+        ...

 class MambaCache(ArraysCache):
    def __init__(self, left_padding: Optional[List[int]] = ...) -> None: ...

 class ChunkedKVCache(KVCache):
    def __init__(self, chunk_size) -> None: ...
-    def maybe_trim_front(self): ...
-    def update_and_fetch(self, keys, values): ...
-    def trim(self, n): ...
+    def maybe_trim_front(self):  # -> None:
+        ...
+    def update_and_fetch(self, keys, values):  # -> tuple[array, array]:
+        ...
+    def trim(self, n):  # -> int:
+        ...
    @property
-    def meta_state(self): ...
+    def meta_state(self):  # -> tuple[str, ...]:
+        ...
    @meta_state.setter
-    def meta_state(self, v): ...
+    def meta_state(self, v):  # -> None:
+        ...

 class CacheList(_BaseCache):
    def __init__(self, *caches) -> None: ...
    def __getitem__(self, idx): ...
-    def is_trimmable(self): ...
+    def is_trimmable(self):  # -> bool:
+        ...
    def trim(self, n): ...
    @property
-    def state(self): ...
+    def state(self):  # -> list[Any]:
+        ...
    @state.setter
-    def state(self, v): ...
-    def filter(self, batch_indices):
+    def state(self, v):  # -> None:
+        ...
+    def filter(self, batch_indices):  # -> None:
        """
        In-place filter to keep just the given indices in the cache.
        """
-        ...

-    def extend(self, other):
+    def extend(self, other):  # -> None:
        """
        In-place extend this cache with the other cache.
        """
-        ...

 class BatchKVCache(_BaseCache):
    step = ...
@@ -256,56 +287,71 @@ class BatchKVCache(_BaseCache):
        And ``left_padding`` specifies the amount of padding for each.
        In this case, ``left_padding = [1, 3, 0]``.
        """
-        ...

-    def update_and_fetch(self, keys, values): ...
+    def update_and_fetch(self, keys, values):  # -> tuple[array | Any, array | Any]:
+        ...
    @property
-    def state(self): ...
+    def state(
+        self,
+    ):  # -> tuple[Any | array | None, Any | array | None, array | Any, array | Any]:
+        ...
    @state.setter
-    def state(self, v): ...
-    def is_trimmable(self): ...
-    def trim(self, n): ...
-    def make_mask(self, N: int, return_array: bool = ..., **kwargs): ...
-    def filter(self, batch_indices):
+    def state(self, v):  # -> None:
+        ...
+    def is_trimmable(self):  # -> Literal[True]:
+        ...
+    def trim(self, n):  # -> int | float:
+        ...
+    def make_mask(self, N: int, return_array: bool = ..., **kwargs):  # -> array:
+        ...
+    def filter(self, batch_indices):  # -> None:
        """
        In-place filter to keep just the given indices in the cache.
        """
-        ...

-    def extend(self, other):
+    def extend(self, other):  # -> None:
        """
        In-place extend this cache with the other cache.
        """
-        ...

 class BatchRotatingKVCache(_BaseCache):
    step = ...
    def __init__(self, max_size, left_padding: List[int]) -> None: ...
-    def update_and_fetch(self, keys, values): ...
+    def update_and_fetch(
+        self, keys, values
+    ):  # -> tuple[array | Any, array | Any] | tuple[array | Any, array | Any | None]:
+        ...
    @property
-    def state(self): ...
+    def state(
+        self,
+    ):  # -> tuple[Any | array | None, Any | array | None, array | Any, array | Any]:
+        ...
    @state.setter
-    def state(self, v): ...
+    def state(self, v):  # -> None:
+        ...
    @property
-    def meta_state(self): ...
+    def meta_state(self):  # -> tuple[str, ...]:
+        ...
    @meta_state.setter
-    def meta_state(self, v): ...
-    def is_trimmable(self): ...
-    def trim(self, n): ...
+    def meta_state(self, v):  # -> None:
+        ...
+    def is_trimmable(self):  # -> bool:
+        ...
+    def trim(self, n):  # -> int:
+        ...
    def to_quantized(
        self, group_size: int = ..., bits: int = ...
    ) -> QuantizedKVCache: ...
    def make_mask(
        self, N: int, window_size: Optional[int] = ..., return_array: bool = ...
-    ): ...
-    def filter(self, batch_indices):
+    ):  # -> array:
+        ...
+    def filter(self, batch_indices):  # -> None:
        """
        In-place filter to keep just the given indices in the cache.
        """
-        ...

-    def extend(self, other):
+    def extend(self, other):  # -> None:
        """
        In-place extend this cache with the other cache.
        """
-        ...
--- a/.mlx_typings/mlx_lm/tokenizer_utils.pyi
+++ b/.mlx_typings/mlx_lm/tokenizer_utils.pyi
@@ -8,10 +8,6 @@ from typing import Any

 from transformers import PreTrainedTokenizerFast

-"""
-This type stub file was generated by pyright.
-"""
-
 class StreamingDetokenizer:
    """The streaming detokenizer interface so that we can detokenize one token at a time.

@@ -49,7 +45,6 @@ class StreamingDetokenizer:
    @property
    def last_segment(self):
        """Return the last segment of readable text since last time this property was accessed."""
-        ...

 class NaiveStreamingDetokenizer(StreamingDetokenizer):
    """NaiveStreamingDetokenizer relies on the underlying tokenizer
@@ -59,11 +54,15 @@ class NaiveStreamingDetokenizer(StreamingDetokenizer):
    repeatedly detokenize the same tokens until a new line is generated.
    """
    def __init__(self, tokenizer) -> None: ...
-    def reset(self): ...
-    def add_token(self, token): ...
-    def finalize(self): ...
+    def reset(self):  # -> None:
+        ...
+    def add_token(self, token):  # -> None:
+        ...
+    def finalize(self):  # -> None:
+        ...
    @property
-    def text(self): ...
+    def text(self):  # -> str:
+        ...

 class SPMStreamingDetokenizer(StreamingDetokenizer):
    """A streaming detokenizer for SPM models.
@@ -72,9 +71,12 @@ class SPMStreamingDetokenizer(StreamingDetokenizer):
    underscore which results in linear complexity.
    """
    def __init__(self, tokenizer, trim_space=...) -> None: ...
-    def reset(self): ...
-    def add_token(self, token): ...
-    def finalize(self): ...
+    def reset(self):  # -> None:
+        ...
+    def add_token(self, token):  # -> None:
+        ...
+    def finalize(self):  # -> None:
+        ...

 class BPEStreamingDetokenizer(StreamingDetokenizer):
    """A streaming detokenizer for OpenAI style BPE models.
@@ -86,13 +88,15 @@ class BPEStreamingDetokenizer(StreamingDetokenizer):
    _byte_decoder = ...
    _space_matches = ...
    def __init__(self, tokenizer) -> None: ...
-    def reset(self): ...
-    def add_token(self, token): ...
-    def finalize(self): ...
-    @classmethod
-    def make_byte_decoder(cls):
-        """See https://github.com/openai/gpt-2/blob/master/src/encoder.py for the rationale."""
+    def reset(self):  # -> None:
        ...
+    def add_token(self, token):  # -> None:
+        ...
+    def finalize(self):  # -> None:
+        ...
+    @classmethod
+    def make_byte_decoder(cls):  # -> None:
+        """See https://github.com/openai/gpt-2/blob/master/src/encoder.py for the rationale."""

 class TokenizerWrapper:
    """A wrapper that combines an HF tokenizer and a detokenizer.
@@ -153,10 +157,13 @@ class TokenizerWrapper:
 class NewlineTokenizer(PreTrainedTokenizerFast):
    """A tokenizer that replaces newlines with <n> and <n> with new line."""
    def __init__(self, *args, **kwargs) -> None: ...
-    def encode(self, text, **kwargs): ...
+    def encode(self, text, **kwargs):  # -> list[int]:
+        ...
    def encode_batch(self, texts, **kwargs): ...
-    def decode(self, *args, **kwargs): ...
-    def batch_decode(self, *args, **kwargs): ...
+    def decode(self, *args, **kwargs):  # -> str:
+        ...
+    def batch_decode(self, *args, **kwargs):  # -> list[str]:
+        ...

 def load(
    model_path: Path,
@@ -169,7 +176,6 @@ def load(
    Note, to use a fast streaming tokenizer, pass a local file path rather than
    a Hugging Face repo ID.
    """
-    ...

 # Alias for backward compatibility
 load_tokenizer = load
--- a/dashboard/parts.nix
+++ b/dashboard/parts.nix
@@ -3,45 +3,6 @@
  perSystem =
    { pkgs, lib, ... }:
    let
-      # Stub source with lockfiles and minimal files for build to succeed
-      # This allows prettier-svelte to avoid rebuilding when dashboard source changes
-      dashboardStubSrc = pkgs.runCommand "dashboard-stub-src" { } ''
-        mkdir -p $out
-        cp ${inputs.self}/dashboard/package.json $out/
-        cp ${inputs.self}/dashboard/package-lock.json $out/
-        # Minimal files so vite build succeeds (produces empty output)
-        echo '<!DOCTYPE html><html><head></head><body></body></html>' > $out/index.html
-        mkdir -p $out/src
-        touch $out/src/app.html
-      '';
-
-      # Deps-only build using stub source (for prettier-svelte)
-      # Only rebuilds when package.json or package-lock.json change
-      dashboardDeps = inputs.dream2nix.lib.evalModules {
-        packageSets.nixpkgs = pkgs;
-        modules = [
-          ./dashboard.nix
-          {
-            paths.projectRoot = inputs.self;
-            paths.projectRootFile = "flake.nix";
-            paths.package = inputs.self + "/dashboard";
-          }
-          {
-            deps.dashboardSrc = lib.mkForce dashboardStubSrc;
-          }
-          # Override build phases to skip the actual build - just need node_modules
-          {
-            mkDerivation = {
-              buildPhase = lib.mkForce "true";
-              installPhase = lib.mkForce ''
-                runHook preInstall
-                runHook postInstall
-              '';
-            };
-          }
-        ];
-      };
-
      # Filter source to only include dashboard directory
      dashboardSrc = lib.cleanSourceWith {
        src = inputs.self;
@@ -81,12 +42,11 @@
      '';

      # Prettier with svelte plugin for treefmt
-      # Uses dashboardDeps instead of dashboardFull to avoid rebuilding on source changes
      packages.prettier-svelte = pkgs.writeShellScriptBin "prettier-svelte" ''
-        export NODE_PATH="${dashboardDeps}/lib/node_modules/exo-dashboard/node_modules"
+        export NODE_PATH="${dashboardFull}/lib/node_modules/exo-dashboard/node_modules"
        exec ${pkgs.nodejs}/bin/node \
-          ${dashboardDeps}/lib/node_modules/exo-dashboard/node_modules/prettier/bin/prettier.cjs \
-          --plugin "${dashboardDeps}/lib/node_modules/exo-dashboard/node_modules/prettier-plugin-svelte/plugin.js" \
+          ${dashboardFull}/lib/node_modules/exo-dashboard/node_modules/prettier/bin/prettier.cjs \
+          --plugin "${dashboardFull}/lib/node_modules/exo-dashboard/node_modules/prettier-plugin-svelte/plugin.js" \
          "$@"
      '';
    };
--- a/dashboard/src/lib/components/ChatForm.svelte
+++ b/dashboard/src/lib/components/ChatForm.svelte
@@ -89,10 +89,7 @@

  const isImageModel = $derived(() => {
    if (!currentModel) return false;
-    return (
-      modelSupportsTextToImage(currentModel) ||
-      modelSupportsImageEditing(currentModel)
-    );
+    return modelSupportsTextToImage(currentModel);
  });

  const isEditOnlyWithoutImage = $derived(
@@ -649,23 +646,6 @@
            </svg>
            <span>EDIT</span>
          </span>
-        {:else if isEditOnlyWithoutImage}
-          <span class="inline-flex items-center gap-1.5">
-            <svg
-              class="w-3.5 h-3.5"
-              fill="none"
-              viewBox="0 0 24 24"
-              stroke="currentColor"
-              stroke-width="2"
-            >
-              <path
-                stroke-linecap="round"
-                stroke-linejoin="round"
-                d="M11 5H6a2 2 0 00-2 2v11a2 2 0 002 2h11a2 2 0 002-2v-5m-1.414-9.414a2 2 0 112.828 2.828L11.828 15H9v-2.828l8.586-8.586z"
-              />
-            </svg>
-            <span>EDIT</span>
-          </span>
        {:else if isImageModel()}
          <span class="inline-flex items-center gap-1.5">
            <svg
--- a/dashboard/src/lib/components/ImageParamsPanel.svelte
+++ b/dashboard/src/lib/components/ImageParamsPanel.svelte
@@ -110,36 +110,6 @@
    setImageGenerationParams({ negativePrompt: value || null });
  }

-  function handleNumImagesChange(event: Event) {
-    const input = event.target as HTMLInputElement;
-    const value = input.value.trim();
-    if (value === "") {
-      setImageGenerationParams({ numImages: 1 });
-    } else {
-      const num = parseInt(value, 10);
-      if (!isNaN(num) && num >= 1) {
-        setImageGenerationParams({ numImages: num });
-      }
-    }
-  }
-
-  function handleStreamChange(enabled: boolean) {
-    setImageGenerationParams({ stream: enabled });
-  }
-
-  function handlePartialImagesChange(event: Event) {
-    const input = event.target as HTMLInputElement;
-    const value = input.value.trim();
-    if (value === "") {
-      setImageGenerationParams({ partialImages: 0 });
-    } else {
-      const num = parseInt(value, 10);
-      if (!isNaN(num) && num >= 0) {
-        setImageGenerationParams({ partialImages: num });
-      }
-    }
-  }
-
  function clearSteps() {
    setImageGenerationParams({ numInferenceSteps: null });
  }
@@ -164,92 +134,90 @@
 <div class="border-b border-exo-medium-gray/30 px-3 py-2">
  <!-- Basic params row -->
  <div class="flex items-center gap-3 flex-wrap">
-    <!-- Size (hidden in edit mode - output size comes from input image) -->
-    {#if !isEditMode}
-      <div class="flex items-center gap-1.5">
-        <span class="text-xs text-exo-light-gray uppercase tracking-wider"
-          >SIZE:</span
+    <!-- Size -->
+    <div class="flex items-center gap-1.5">
+      <span class="text-xs text-exo-light-gray uppercase tracking-wider"
+        >SIZE:</span
+      >
+      <div class="relative">
+        <button
+          bind:this={sizeButtonRef}
+          type="button"
+          onclick={() => (isSizeDropdownOpen = !isSizeDropdownOpen)}
+          class="bg-exo-medium-gray/50 border border-exo-yellow/30 rounded pl-2 pr-6 py-1 text-xs font-mono text-exo-yellow cursor-pointer transition-all duration-200 hover:border-exo-yellow/50 focus:outline-none focus:border-exo-yellow/70 {isSizeDropdownOpen
+            ? 'border-exo-yellow/70'
+            : ''}"
        >
-        <div class="relative">
-          <button
-            bind:this={sizeButtonRef}
-            type="button"
-            onclick={() => (isSizeDropdownOpen = !isSizeDropdownOpen)}
-            class="bg-exo-medium-gray/50 border border-exo-yellow/30 rounded pl-2 pr-6 py-1 text-xs font-mono text-exo-yellow cursor-pointer transition-all duration-200 hover:border-exo-yellow/50 focus:outline-none focus:border-exo-yellow/70 {isSizeDropdownOpen
-              ? 'border-exo-yellow/70'
-              : ''}"
+          {params.size}
+        </button>
+        <div
+          class="absolute right-1.5 top-1/2 -translate-y-1/2 pointer-events-none transition-transform duration-200 {isSizeDropdownOpen
+            ? 'rotate-180'
+            : ''}"
+        >
+          <svg
+            class="w-3 h-3 text-exo-yellow/60"
+            fill="none"
+            viewBox="0 0 24 24"
+            stroke="currentColor"
          >
-            {params.size}
-          </button>
-          <div
-            class="absolute right-1.5 top-1/2 -translate-y-1/2 pointer-events-none transition-transform duration-200 {isSizeDropdownOpen
-              ? 'rotate-180'
-              : ''}"
-          >
-            <svg
-              class="w-3 h-3 text-exo-yellow/60"
-              fill="none"
-              viewBox="0 0 24 24"
-              stroke="currentColor"
-            >
-              <path
-                stroke-linecap="round"
-                stroke-linejoin="round"
-                stroke-width="2"
-                d="M19 9l-7 7-7-7"
-              />
-            </svg>
+            <path
+              stroke-linecap="round"
+              stroke-linejoin="round"
+              stroke-width="2"
+              d="M19 9l-7 7-7-7"
+            />
+          </svg>
+        </div>
+      </div>
+
+      {#if isSizeDropdownOpen}
+        <!-- Backdrop to close dropdown -->
+        <button
+          type="button"
+          class="fixed inset-0 z-[9998] cursor-default"
+          onclick={() => (isSizeDropdownOpen = false)}
+          aria-label="Close dropdown"
+        ></button>
+
+        <!-- Dropdown Panel - fixed positioning to escape overflow:hidden -->
+        <div
+          class="fixed bg-exo-dark-gray border border-exo-yellow/30 rounded shadow-lg shadow-black/50 z-[9999] max-h-48 overflow-y-auto min-w-max"
+          style="bottom: calc(100vh - {sizeDropdownPosition()
+            .top}px + 4px); left: {sizeDropdownPosition().left}px;"
+        >
+          <div class="py-1">
+            {#each sizeOptions as size}
+              <button
+                type="button"
+                onclick={() => selectSize(size)}
+                class="w-full px-3 py-1.5 text-left text-xs font-mono tracking-wide transition-colors duration-100 flex items-center gap-2 {params.size ===
+                size
+                  ? 'bg-transparent text-exo-yellow'
+                  : 'text-exo-light-gray hover:text-exo-yellow'}"
+              >
+                {#if params.size === size}
+                  <svg
+                    class="w-3 h-3 flex-shrink-0"
+                    fill="currentColor"
+                    viewBox="0 0 20 20"
+                  >
+                    <path
+                      fill-rule="evenodd"
+                      d="M16.707 5.293a1 1 0 010 1.414l-8 8a1 1 0 01-1.414 0l-4-4a1 1 0 011.414-1.414L8 12.586l7.293-7.293a1 1 0 011.414 0z"
+                      clip-rule="evenodd"
+                    />
+                  </svg>
+                {:else}
+                  <span class="w-3"></span>
+                {/if}
+                <span>{size}</span>
+              </button>
+            {/each}
          </div>
        </div>
-
-        {#if isSizeDropdownOpen}
-          <!-- Backdrop to close dropdown -->
-          <button
-            type="button"
-            class="fixed inset-0 z-[9998] cursor-default"
-            onclick={() => (isSizeDropdownOpen = false)}
-            aria-label="Close dropdown"
-          ></button>
-
-          <!-- Dropdown Panel - fixed positioning to escape overflow:hidden -->
-          <div
-            class="fixed bg-exo-dark-gray border border-exo-yellow/30 rounded shadow-lg shadow-black/50 z-[9999] max-h-48 overflow-y-auto min-w-max"
-            style="bottom: calc(100vh - {sizeDropdownPosition()
-              .top}px + 4px); left: {sizeDropdownPosition().left}px;"
-          >
-            <div class="py-1">
-              {#each sizeOptions as size}
-                <button
-                  type="button"
-                  onclick={() => selectSize(size)}
-                  class="w-full px-3 py-1.5 text-left text-xs font-mono tracking-wide transition-colors duration-100 flex items-center gap-2 {params.size ===
-                  size
-                    ? 'bg-transparent text-exo-yellow'
-                    : 'text-exo-light-gray hover:text-exo-yellow'}"
-                >
-                  {#if params.size === size}
-                    <svg
-                      class="w-3 h-3 flex-shrink-0"
-                      fill="currentColor"
-                      viewBox="0 0 20 20"
-                    >
-                      <path
-                        fill-rule="evenodd"
-                        d="M16.707 5.293a1 1 0 010 1.414l-8 8a1 1 0 01-1.414 0l-4-4a1 1 0 011.414-1.414L8 12.586l7.293-7.293a1 1 0 011.414 0z"
-                        clip-rule="evenodd"
-                      />
-                    </svg>
-                  {:else}
-                    <span class="w-3"></span>
-                  {/if}
-                  <span>{size}</span>
-                </button>
-              {/each}
-            </div>
-          </div>
-        {/if}
-      </div>
-    {/if}
+      {/if}
+    </div>

    <!-- Quality -->
    <div class="flex items-center gap-1.5">
@@ -357,59 +325,6 @@
      </div>
    </div>

-    <!-- Number of Images (not in edit mode) -->
-    {#if !isEditMode}
-      <div class="flex items-center gap-1.5">
-        <span class="text-xs text-exo-light-gray uppercase tracking-wider"
-          >IMAGES:</span
-        >
-        <input
-          type="number"
-          min="1"
-          value={params.numImages}
-          oninput={handleNumImagesChange}
-          class="w-12 bg-exo-medium-gray/50 border border-exo-yellow/30 rounded px-2 py-1 text-xs font-mono text-exo-yellow text-center transition-all duration-200 hover:border-exo-yellow/50 focus:outline-none focus:border-exo-yellow/70"
-        />
-      </div>
-    {/if}
-
-    <!-- Stream toggle -->
-    <div class="flex items-center gap-1.5">
-      <span class="text-xs text-exo-light-gray uppercase tracking-wider"
-        >STREAM:</span
-      >
-      <button
-        type="button"
-        onclick={() => handleStreamChange(!params.stream)}
-        class="w-8 h-4 rounded-full transition-all duration-200 cursor-pointer relative {params.stream
-          ? 'bg-exo-yellow'
-          : 'bg-exo-medium-gray/50 border border-exo-yellow/30'}"
-        title={params.stream ? "Streaming enabled" : "Streaming disabled"}
-      >
-        <div
-          class="absolute top-0.5 w-3 h-3 rounded-full transition-all duration-200 {params.stream
-            ? 'right-0.5 bg-exo-black'
-            : 'left-0.5 bg-exo-light-gray'}"
-        ></div>
-      </button>
-    </div>
-
-    <!-- Partial Images (only when streaming) -->
-    {#if params.stream}
-      <div class="flex items-center gap-1.5">
-        <span class="text-xs text-exo-light-gray uppercase tracking-wider"
-          >PARTIALS:</span
-        >
-        <input
-          type="number"
-          min="0"
-          value={params.partialImages}
-          oninput={handlePartialImagesChange}
-          class="w-12 bg-exo-medium-gray/50 border border-exo-yellow/30 rounded px-2 py-1 text-xs font-mono text-exo-yellow text-center transition-all duration-200 hover:border-exo-yellow/50 focus:outline-none focus:border-exo-yellow/70"
-        />
-      </div>
-    {/if}
-
    <!-- Input Fidelity (edit mode only) -->
    {#if isEditMode}
      <div class="flex items-center gap-1.5">
--- a/dashboard/src/lib/stores/app.svelte.ts
+++ b/dashboard/src/lib/stores/app.svelte.ts
@@ -238,10 +238,6 @@ export interface ImageGenerationParams {
  size: "512x512" | "768x768" | "1024x1024" | "1024x768" | "768x1024";
  quality: "low" | "medium" | "high";
  outputFormat: "png" | "jpeg";
-  numImages: number;
-  // Streaming params
-  stream: boolean;
-  partialImages: number;
  // Advanced params
  seed: number | null;
  numInferenceSteps: number | null;
@@ -261,9 +257,6 @@ const DEFAULT_IMAGE_PARAMS: ImageGenerationParams = {
  size: "1024x1024",
  quality: "medium",
  outputFormat: "png",
-  numImages: 1,
-  stream: true,
-  partialImages: 3,
  seed: null,
  numInferenceSteps: null,
  guidance: null,
@@ -1816,13 +1809,12 @@ class AppStore {
      const requestBody: Record<string, unknown> = {
        model,
        prompt,
-        n: params.numImages,
        quality: params.quality,
        size: params.size,
        output_format: params.outputFormat,
        response_format: "b64_json",
-        stream: params.stream,
-        partial_images: params.partialImages,
+        stream: true,
+        partial_images: 3,
      };

      if (hasAdvancedParams) {
@@ -1886,74 +1878,31 @@ class AppStore {
              if (imageData && idx !== -1) {
                const format = parsed.format || "png";
                const mimeType = `image/${format}`;
-                const imageIndex = parsed.image_index ?? 0;
-                const numImages = params.numImages;
-
                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}`;
-                  this.messages[idx].content = progressText;
-
-                  const partialAttachment: MessageAttachment = {
-                    type: "generated-image",
-                    name: `generated-image.${format}`,
-                    preview: `data:${mimeType};base64,${imageData}`,
-                    mimeType,
-                  };
-
-                  if (imageIndex === 0) {
-                    // First image - safe to replace attachments with partial preview
-                    this.messages[idx].attachments = [partialAttachment];
-                  } else {
-                    // Subsequent images - keep existing finals, show partial at current position
-                    const existingAttachments =
-                      this.messages[idx].attachments || [];
-                    // Keep only the completed final images (up to current imageIndex)
-                    const finals = existingAttachments.slice(0, imageIndex);
-                    this.messages[idx].attachments = [
-                      ...finals,
-                      partialAttachment,
-                    ];
-                  }
+                  this.messages[idx].content =
+                    `Generating... ${partialNum}/${totalPartials}`;
+                  this.messages[idx].attachments = [
+                    {
+                      type: "generated-image",
+                      name: `generated-image.${format}`,
+                      preview: `data:${mimeType};base64,${imageData}`,
+                      mimeType,
+                    },
+                  ];
                } else if (parsed.type === "final") {
-                  // Final image - replace partial at this position
-                  const newAttachment: MessageAttachment = {
-                    type: "generated-image",
-                    name: `generated-image-${imageIndex + 1}.${format}`,
-                    preview: `data:${mimeType};base64,${imageData}`,
-                    mimeType,
-                  };
-
-                  if (imageIndex === 0) {
-                    // First final image - replace any partial preview
-                    this.messages[idx].attachments = [newAttachment];
-                  } else {
-                    // Subsequent images - keep previous finals, replace partial at current position
-                    const existingAttachments =
-                      this.messages[idx].attachments || [];
-                    // Slice keeps indices 0 to imageIndex-1 (the previous final images)
-                    const previousFinals = existingAttachments.slice(
-                      0,
-                      imageIndex,
-                    );
-                    this.messages[idx].attachments = [
-                      ...previousFinals,
-                      newAttachment,
-                    ];
-                  }
-
-                  // Update progress message for multiple images
-                  if (numImages > 1 && imageIndex < numImages - 1) {
-                    this.messages[idx].content =
-                      `Generating image ${imageIndex + 2}/${numImages}...`;
-                  } else {
-                    this.messages[idx].content = "";
-                  }
+                  // Final image
+                  this.messages[idx].content = "";
+                  this.messages[idx].attachments = [
+                    {
+                      type: "generated-image",
+                      name: `generated-image.${format}`,
+                      preview: `data:${mimeType};base64,${imageData}`,
+                      mimeType,
+                    },
+                  ];
                }
              }
            } catch {
@@ -2034,8 +1983,8 @@ class AppStore {
      formData.append("size", params.size);
      formData.append("output_format", params.outputFormat);
      formData.append("response_format", "b64_json");
-      formData.append("stream", params.stream ? "1" : "0");
-      formData.append("partial_images", params.partialImages.toString());
+      formData.append("stream", "1"); // Use "1" instead of "true" for reliable FastAPI boolean parsing
+      formData.append("partial_images", "3");
      formData.append("input_fidelity", params.inputFidelity);

      // Advanced params
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,7 +17,7 @@ dependencies = [
    "loguru>=0.7.3",
    "exo_pyo3_bindings", # rust bindings
    "anyio==4.11.0",
-    "mlx==0.30.3; sys_platform == 'darwin'",
+    "mlx @ git+https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git; sys_platform == 'darwin'",
    "mlx[cpu]==0.30.3; sys_platform == 'linux'",
    "mlx-lm @ git+https://github.com/AlexCheema/mlx-lm.git@fix-transformers-5.0.0rc2",
    "tiktoken>=0.12.0", # required for kimi k2 tokenizer
--- a/src/exo/master/api.py
+++ b/src/exo/master/api.py
@@ -1,5 +1,4 @@
 import base64
-import contextlib
 import json
 import time
 from collections.abc import AsyncGenerator
@@ -34,7 +33,6 @@ from exo.shared.models.model_cards import (
    ModelId,
 )
 from exo.shared.types.api import (
-    AdvancedImageParams,
    BenchChatCompletionResponse,
    BenchChatCompletionTaskParams,
    BenchImageGenerationResponse,
@@ -837,7 +835,6 @@ class API:
                            # Yield partial image event (always use b64_json for partials)
                            event_data = {
                                "type": "partial",
-                                "image_index": chunk.image_index,
                                "partial_index": partial_idx,
                                "total_partials": total_partials,
                                "format": str(chunk.format),
@@ -1027,9 +1024,6 @@ class API:
        stream: bool,
        partial_images: int,
        bench: bool,
-        quality: Literal["high", "medium", "low"],
-        output_format: Literal["png", "jpeg", "webp"],
-        advanced_params: AdvancedImageParams | None,
    ) -> ImageEdits:
        """Prepare and send an image edits command with chunked image upload."""
        resolved_model = await self._validate_image_model(model)
@@ -1058,9 +1052,6 @@ class API:
                stream=stream,
                partial_images=partial_images,
                bench=bench,
-                quality=quality,
-                output_format=output_format,
-                advanced_params=advanced_params,
            ),
        )

@@ -1095,22 +1086,12 @@ class API:
        input_fidelity: Literal["low", "high"] = Form("low"),
        stream: str = Form("false"),
        partial_images: str = Form("0"),
-        quality: Literal["high", "medium", "low"] = Form("medium"),
-        output_format: Literal["png", "jpeg", "webp"] = Form("png"),
-        advanced_params: str | None = Form(None),
    ) -> ImageGenerationResponse | StreamingResponse:
        """Handle image editing requests (img2img)."""
        # Parse string form values to proper types
        stream_bool = stream.lower() in ("true", "1", "yes")
        partial_images_int = int(partial_images) if partial_images.isdigit() else 0

-        parsed_advanced_params: AdvancedImageParams | None = None
-        if advanced_params:
-            with contextlib.suppress(Exception):
-                parsed_advanced_params = AdvancedImageParams.model_validate_json(
-                    advanced_params
-                )
-
        command = await self._send_image_edits_command(
            image=image,
            prompt=prompt,
@@ -1122,9 +1103,6 @@ class API:
            stream=stream_bool,
            partial_images=partial_images_int,
            bench=False,
-            quality=quality,
-            output_format=output_format,
-            advanced_params=parsed_advanced_params,
        )

        if stream_bool and partial_images_int > 0:
@@ -1155,18 +1133,8 @@ class API:
        size: str = Form("1024x1024"),
        response_format: Literal["url", "b64_json"] = Form("b64_json"),
        input_fidelity: Literal["low", "high"] = Form("low"),
-        quality: Literal["high", "medium", "low"] = Form("medium"),
-        output_format: Literal["png", "jpeg", "webp"] = Form("png"),
-        advanced_params: str | None = Form(None),
    ) -> BenchImageGenerationResponse:
        """Handle benchmark image editing requests with generation stats."""
-        parsed_advanced_params: AdvancedImageParams | None = None
-        if advanced_params:
-            with contextlib.suppress(Exception):
-                parsed_advanced_params = AdvancedImageParams.model_validate_json(
-                    advanced_params
-                )
-
        command = await self._send_image_edits_command(
            image=image,
            prompt=prompt,
@@ -1178,9 +1146,6 @@ class API:
            stream=False,
            partial_images=0,
            bench=True,
-            quality=quality,
-            output_format=output_format,
-            advanced_params=parsed_advanced_params,
        )

        return await self._collect_image_generation_with_stats(
--- a/src/exo/shared/types/mlx.py
+++ b/src/exo/shared/types/mlx.py
@@ -1,11 +0,0 @@
-"""Shared types for MLX-related functionality."""
-
-from mlx_lm.models.cache import (
-    KVCache,
-    QuantizedKVCache,
-    RotatingKVCache,
-)
-
-# Type alias for KV cache - matches make_kv_cache return type
-# This list contains one cache entry per transformer layer
-KVCacheType = list[KVCache | RotatingKVCache | QuantizedKVCache]
--- a/src/exo/shared/types/worker/runner_response.py
+++ b/src/exo/shared/types/worker/runner_response.py
@@ -30,7 +30,6 @@ class ImageGenerationResponse(BaseRunnerResponse):
    image_data: bytes
    format: Literal["png", "jpeg", "webp"] = "png"
    stats: ImageGenerationStats | None = None
-    image_index: int = 0

    def __repr_args__(self) -> Generator[tuple[str, Any], None, None]:
        for name, value in super().__repr_args__():  # pyright: ignore[reportAny]
@@ -45,7 +44,6 @@ class PartialImageResponse(BaseRunnerResponse):
    format: Literal["png", "jpeg", "webp"] = "png"
    partial_index: int
    total_partials: int
-    image_index: int = 0

    def __repr_args__(self) -> Generator[tuple[str, Any], None, None]:
        for name, value in super().__repr_args__():  # pyright: ignore[reportAny]
--- a/src/exo/worker/engines/image/generate.py
+++ b/src/exo/worker/engines/image/generate.py
@@ -75,20 +75,19 @@ def generate_image(
    intermediate images, then ImageGenerationResponse for the final image.

    Yields:
-        PartialImageResponse for intermediate images (if partial_images > 0, first image only)
-        ImageGenerationResponse for final complete images
+        PartialImageResponse for intermediate images (if partial_images > 0)
+        ImageGenerationResponse for the final complete image
    """
    width, height = parse_size(task.size)
    quality: Literal["low", "medium", "high"] = task.quality or "medium"

    advanced_params = task.advanced_params
    if advanced_params is not None and advanced_params.seed is not None:
-        base_seed = advanced_params.seed
+        seed = advanced_params.seed
    else:
-        base_seed = random.randint(0, 2**32 - 1)
+        seed = random.randint(0, 2**32 - 1)

    is_bench = getattr(task, "bench", False)
-    num_images = task.n or 1

    generation_start_time: float = 0.0

@@ -96,11 +95,7 @@ def generate_image(
        mx.reset_peak_memory()
        generation_start_time = time.perf_counter()

-    partial_images = (
-        task.partial_images
-        if task.partial_images is not None
-        else (3 if task.stream else 0)
-    )
+    partial_images = task.partial_images or (3 if task.stream else 0)

    image_path: Path | None = None

@@ -110,81 +105,72 @@ def generate_image(
            image_path = Path(tmpdir) / "input.png"
            image_path.write_bytes(base64.b64decode(task.image_data))

-        for image_num in range(num_images):
-            # Increment seed for each image to ensure unique results
-            current_seed = base_seed + image_num
+        # Iterate over generator results
+        for result in model.generate(
+            prompt=task.prompt,
+            height=height,
+            width=width,
+            quality=quality,
+            seed=seed,
+            image_path=image_path,
+            partial_images=partial_images,
+            advanced_params=advanced_params,
+        ):
+            if isinstance(result, tuple):
+                # Partial image: (Image, partial_index, total_partials)
+                image, partial_idx, total_partials = result
+                buffer = io.BytesIO()
+                image_format = task.output_format.upper()
+                if image_format == "JPG":
+                    image_format = "JPEG"
+                if image_format == "JPEG" and image.mode == "RGBA":
+                    image = image.convert("RGB")
+                image.save(buffer, format=image_format)

-            for result in model.generate(
-                prompt=task.prompt,
-                height=height,
-                width=width,
-                quality=quality,
-                seed=current_seed,
-                image_path=image_path,
-                partial_images=partial_images,
-                advanced_params=advanced_params,
-            ):
-                if isinstance(result, tuple):
-                    # Partial image: (Image, partial_index, total_partials)
-                    image, partial_idx, total_partials = result
-                    buffer = io.BytesIO()
-                    image_format = task.output_format.upper()
-                    if image_format == "JPG":
-                        image_format = "JPEG"
-                    if image_format == "JPEG" and image.mode == "RGBA":
-                        image = image.convert("RGB")
-                    image.save(buffer, format=image_format)
+                yield PartialImageResponse(
+                    image_data=buffer.getvalue(),
+                    format=task.output_format,
+                    partial_index=partial_idx,
+                    total_partials=total_partials,
+                )
+            else:
+                image = result

-                    yield PartialImageResponse(
-                        image_data=buffer.getvalue(),
-                        format=task.output_format,
-                        partial_index=partial_idx,
-                        total_partials=total_partials,
-                        image_index=image_num,
+                stats: ImageGenerationStats | None = None
+                if is_bench:
+                    generation_end_time = time.perf_counter()
+                    total_generation_time = generation_end_time - generation_start_time
+
+                    num_inference_steps = model.get_steps_for_quality(quality)
+
+                    seconds_per_step = (
+                        total_generation_time / num_inference_steps
+                        if num_inference_steps > 0
+                        else 0.0
                    )
-                else:
-                    image = result

-                    # Only include stats on the final image
-                    stats: ImageGenerationStats | None = None
-                    if is_bench and image_num == num_images - 1:
-                        generation_end_time = time.perf_counter()
-                        total_generation_time = (
-                            generation_end_time - generation_start_time
-                        )
+                    peak_memory_gb = mx.get_peak_memory() / (1024**3)

-                        num_inference_steps = model.get_steps_for_quality(quality)
-                        total_steps = num_inference_steps * num_images
-
-                        seconds_per_step = (
-                            total_generation_time / total_steps
-                            if total_steps > 0
-                            else 0.0
-                        )
-
-                        peak_memory_gb = mx.get_peak_memory() / (1024**3)
-
-                        stats = ImageGenerationStats(
-                            seconds_per_step=seconds_per_step,
-                            total_generation_time=total_generation_time,
-                            num_inference_steps=num_inference_steps,
-                            num_images=num_images,
-                            image_width=width,
-                            image_height=height,
-                            peak_memory_usage=Memory.from_gb(peak_memory_gb),
-                        )
-
-                    buffer = io.BytesIO()
-                    image_format = task.output_format.upper()
-                    if image_format == "JPG":
-                        image_format = "JPEG"
-                    if image_format == "JPEG" and image.mode == "RGBA":
-                        image = image.convert("RGB")
-                    image.save(buffer, format=image_format)
-
-                    yield ImageGenerationResponse(
-                        image_data=buffer.getvalue(),
-                        format=task.output_format,
-                        stats=stats,
-                        image_index=image_num,
+                    stats = ImageGenerationStats(
+                        seconds_per_step=seconds_per_step,
+                        total_generation_time=total_generation_time,
+                        num_inference_steps=num_inference_steps,
+                        num_images=task.n or 1,
+                        image_width=width,
+                        image_height=height,
+                        peak_memory_usage=Memory.from_gb(peak_memory_gb),
                    )
+
+                buffer = io.BytesIO()
+                image_format = task.output_format.upper()
+                if image_format == "JPG":
+                    image_format = "JPEG"
+                if image_format == "JPEG" and image.mode == "RGBA":
+                    image = image.convert("RGB")
+                image.save(buffer, format=image_format)
+
+                yield ImageGenerationResponse(
+                    image_data=buffer.getvalue(),
+                    format=task.output_format,
+                    stats=stats,
+                )
--- a/src/exo/worker/engines/mlx/auto_parallel.py
+++ b/src/exo/worker/engines/mlx/auto_parallel.py
@@ -145,6 +145,10 @@ class PipelineLastLayer(CustomMlxLayer):
            if cache is not None:
                cache.keys = mx.depends(cache.keys, output)  # type: ignore[reportUnknownMemberType]

+        output = mx.distributed.all_gather(output, group=self.group)[
+            -output.shape[0] :
+        ]  # type :ignore
+
        return output


@@ -252,10 +256,6 @@ def patch_pipeline_model[T](model: T, group: mx.distributed.Group) -> T:
        if cache is not None:
            cache[-1].state = mx.depends(cache[-1].state, logits)  # type: ignore

-        logits = mx.distributed.all_gather(logits, group=group)[
-            -logits.shape[0] :
-        ]  # type :ignore
-
        return logits

    cls.__call__ = patched_call
--- a/src/exo/worker/engines/mlx/cache.py
+++ b/src/exo/worker/engines/mlx/cache.py
@@ -1,53 +1,39 @@
+# type: ignore
+# TODO: Fix this file, including types!
 from copy import deepcopy
+from typing import Callable

 import mlx.core as mx
-from mlx_lm.models.cache import trim_prompt_cache
+from mlx_lm import stream_generate
+from mlx_lm.models.cache import _BaseCache, trim_prompt_cache
 from mlx_lm.tokenizer_utils import TokenizerWrapper

-from exo.shared.types.mlx import KVCacheType
 from exo.worker.engines.mlx import Model
+from exo.worker.engines.mlx.constants import KEEP_KV_SIZE, KV_BITS, KV_GROUP_SIZE
 from exo.worker.engines.mlx.utils_mlx import make_kv_cache
-from exo.worker.runner.bootstrap import logger


 class KVPrefixCache:
    def __init__(self):
        # Only one prefix cache per runner.
        self.prompts: list[mx.array] = []  # mx array of tokens (ints)
-        self.caches: list[KVCacheType] = []
-
-    def clear(self):
-        """Clear all cached prompts and caches."""
-        self.prompts.clear()
-        self.caches.clear()
+        self.caches: list[list[_BaseCache]] = []

    def add_kv_cache(
-        self, tokenizer: TokenizerWrapper, prompt: str, cache: KVCacheType
+        self, tokenizer: TokenizerWrapper, prompt: str, cache: list[_BaseCache]
    ):
-        tokenized_prompt = encode_prompt(tokenizer, prompt)
+        tokenized_prompt = self.encode_prompt(tokenizer, prompt)
        self.prompts.append(tokenized_prompt)
        self.caches.append(deepcopy(cache))
-        logger.info(f"KV cache saved: {len(tokenized_prompt)} tokens")

    def get_kv_cache(
        self,
        model: Model,
        tokenizer: TokenizerWrapper,
+        sampler: Callable[[mx.array], mx.array],
        prompt: str,
-    ) -> tuple[KVCacheType, mx.array]:
-        """Get KV cache for prompt, returning remaining tokens to prefill.
-
-        This method finds the best matching cached prefix and returns:
-        - A copy of the cache trimmed to the prefix length
-        - The remaining tokens that need to be prefilled before generation
-
-        The caller is responsible for prefilling the remaining tokens.
-
-        Returns:
-            Tuple of (cache, remaining_tokens) where remaining_tokens are the
-            tokens that still need to be prefilled/processed.
-        """
-        tokenized_prompt = encode_prompt(tokenizer, prompt)
+    ) -> list[_BaseCache]:
+        tokenized_prompt = self.encode_prompt(tokenizer, prompt)
        max_length = len(tokenized_prompt)

        best_snapshot_index, best_snapshot_length = None, 0
@@ -56,75 +42,63 @@ class KVPrefixCache:
            length = _get_prefix_length(tokenized_prompt, cached_prompt)

            if length == max_length:
-                # Exact match - cached prompt starts with our entire prompt
-                # Trim cache to prompt length - 1, return last token for stream_generate
-                prompt_cache = deepcopy(self.caches[i])
-                cached_length = _cache_length(self.caches[i])
-                tokens_to_trim = cached_length - (max_length - 1)
-                if tokens_to_trim > 0:
-                    trim_prompt_cache(prompt_cache, tokens_to_trim)
-                logger.info(f"KV cache exact match: {max_length} tokens (instant)")
-                return prompt_cache, tokenized_prompt[-1:]
+                return self.caches[i]

            if length > best_snapshot_length:
                best_snapshot_index, best_snapshot_length = i, length

        if best_snapshot_index is not None:
-            new_tokens = max_length - best_snapshot_length
-            logger.info(
-                f"KV cache prefix match: {best_snapshot_length}/{max_length} tokens "
-                f"(reusing {best_snapshot_length}, need to prefill {new_tokens})"
-            )
-
            prompt_cache = deepcopy(self.caches[best_snapshot_index])
-
-            # Trim removes tokens from the end, so we trim (cached_length - prefix_length) to keep the prefix
-            cached_length = _cache_length(self.caches[best_snapshot_index])
-            tokens_to_trim = cached_length - best_snapshot_length
-            if tokens_to_trim > 0:
-                trim_prompt_cache(prompt_cache, tokens_to_trim)
-
-            # Return remaining tokens for caller to prefill
-            remaining_tokens = tokenized_prompt[best_snapshot_length:]
-            return prompt_cache, remaining_tokens
+            trim_prompt_cache(prompt_cache, max_length - best_snapshot_length)
+            tokenized_prompt = tokenized_prompt[best_snapshot_index:]

        else:
-            prompt_cache = make_kv_cache(model)
-            if len(self.prompts) == 0:
-                logger.info(f"KV cache empty, need to prefill {max_length} tokens")
-            else:
-                logger.info(
-                    f"KV cache no prefix match, need to prefill {max_length} tokens"
-                )
+            prompt_cache = make_kv_cache(
+                model,
+                # max_kv_size=MAX_KV_SIZE,
+                # keep=KEEP_KV_SIZE
+            )

-            # Return all tokens for caller to prefill
-            return prompt_cache, tokenized_prompt
+        prefill(model, tokenizer, sampler, tokenized_prompt, prompt_cache)

+        return prompt_cache

-def encode_prompt(tokenizer: TokenizerWrapper, prompt: str) -> mx.array:
-    """Encode a prompt string to token array.
-
-    For chat-templated prompts (which have their own structure markers like
-    <|im_user|>, <|im_middle|>, etc.), we should NOT add BOS/EOS tokens as
-    that would corrupt the prompt structure.
-    """
-    # Chat templates define their own structure - don't add BOS/EOS
-    tokenized_prompt = tokenizer.encode(prompt, add_special_tokens=False)
-    return mx.array(tokenized_prompt)
-
-
-def _cache_length(cache: KVCacheType) -> int:
-    """Get the number of tokens in a KV cache."""
-    # Use .offset attribute which all cache types have (len() not implemented in older QuantizedKVCache)
-    return max(c.offset for c in cache)
+    def encode_prompt(self, tokenizer: TokenizerWrapper, prompt: str) -> mx.array:
+        add_special_tokens = tokenizer.bos_token is None or not prompt.startswith(
+            tokenizer.bos_token
+        )
+        tokenized_prompt = tokenizer.encode(
+            prompt, add_special_tokens=add_special_tokens
+        )
+        return mx.array(tokenized_prompt)


 def _get_prefix_length(prompt: mx.array, cached_prompt: mx.array) -> int:
-    """Find the length of the common prefix between two token arrays."""
-    n = min(int(prompt.shape[0]), int(cached_prompt.shape[0]))
+    n = min(int(prompt.shape[0]), int(cached_prompt.shape[0]), KEEP_KV_SIZE)
    if n == 0:
        return 0

-    equal = mx.equal(prompt[:n], cached_prompt[:n]).astype(mx.int32)
+    equal = (prompt[:n] == cached_prompt[:n]).astype(mx.int32)
    prefix_mask = mx.cumprod(equal)  # stays 1 until first mismatch, then 0 forever
    return int(mx.sum(prefix_mask).item())
+
+
+def prefill(
+    model: Model,
+    tokenizer: TokenizerWrapper,
+    sampler: Callable[[mx.array], mx.array],
+    prompt: mx.array,
+    cache: list[_BaseCache],
+) -> None:
+    for _ in stream_generate(
+        model=model,
+        tokenizer=tokenizer,
+        prompt=prompt,
+        max_tokens=0,
+        sampler=sampler,
+        prompt_cache=cache,
+        prefill_step_size=2048,
+        kv_group_size=KV_GROUP_SIZE,
+        kv_bits=KV_BITS,
+    ):
+        pass
--- a/src/exo/worker/engines/mlx/generator/generate.py
+++ b/src/exo/worker/engines/mlx/generator/generate.py
@@ -1,12 +1,12 @@
-import time
-from typing import Callable, Generator, cast, get_args
+from typing import Any, Callable, Generator, cast, get_args

 import mlx.core as mx
 from mlx_lm.generate import stream_generate
-from mlx_lm.models.cache import trim_prompt_cache
+from mlx_lm.models.cache import KVCache
 from mlx_lm.sample_utils import make_sampler
 from mlx_lm.tokenizer_utils import TokenizerWrapper

+# from exo.engines.mlx.cache import KVPrefixCache
 from exo.shared.types.api import (
    BenchChatCompletionTaskParams,
    ChatCompletionMessage,
@@ -14,13 +14,11 @@ from exo.shared.types.api import (
    GenerationStats,
 )
 from exo.shared.types.memory import Memory
-from exo.shared.types.mlx import KVCacheType
 from exo.shared.types.tasks import ChatCompletionTaskParams
 from exo.shared.types.worker.runner_response import (
    GenerationResponse,
 )
 from exo.worker.engines.mlx import Model
-from exo.worker.engines.mlx.cache import KVPrefixCache, encode_prompt
 from exo.worker.engines.mlx.constants import KV_BITS, KV_GROUP_SIZE, MAX_TOKENS
 from exo.worker.engines.mlx.utils_mlx import (
    apply_chat_template,
@@ -32,59 +30,19 @@ from exo.worker.runner.bootstrap import logger
 generation_stream = mx.new_stream(mx.default_device())


-def prefill(
-    model: Model,
-    tokenizer: TokenizerWrapper,
-    sampler: Callable[[mx.array], mx.array],
-    prompt_tokens: mx.array,
-    cache: KVCacheType,
-) -> float:
-    """Prefill the KV cache with prompt tokens.
-
-    This runs the model over the prompt tokens to populate the cache,
-    then trims off the extra generated token.
-
-    Returns:
-        tokens_per_sec
-    """
-    num_tokens = len(prompt_tokens)
-    if num_tokens == 0:
-        return 0.0
-
-    logger.debug(f"Prefilling {num_tokens} tokens...")
-    start_time = time.perf_counter()
-
-    def progress_callback(processed: int, total: int) -> None:
-        elapsed = time.time() - start_time
-        tok_per_sec = processed / elapsed if elapsed > 0 else 0
-        logger.debug(
-            f"Prefill progress: {processed}/{total} tokens ({tok_per_sec:.1f} tok/s)"
-        )
-
-    # Use max_tokens=1 because max_tokens=0 does not work.
-    # We just throw away the generated token - we only care about filling the cache
-    for _ in stream_generate(
-        model=model,
-        tokenizer=tokenizer,
-        prompt=prompt_tokens,
-        max_tokens=1,
-        sampler=sampler,
-        prompt_cache=cache,
-        prefill_step_size=2048,
-        kv_group_size=KV_GROUP_SIZE,
-        kv_bits=KV_BITS,
-        prompt_progress_callback=progress_callback,
-    ):
-        break  # Stop after first iteration - cache is now filled
-    trim_prompt_cache(cache, 1)
-
-    elapsed = time.perf_counter() - start_time
-    tokens_per_sec = num_tokens / elapsed if elapsed > 0 else 0.0
-    logger.debug(
-        f"Prefill complete: {num_tokens} tokens in {elapsed:.2f}s "
-        f"({tokens_per_sec:.1f} tok/s)"
-    )
-    return tokens_per_sec
+def maybe_quantize_kv_cache(
+    prompt_cache: list[KVCache | Any],
+    quantized_kv_start: int,
+    kv_group_size: int,
+    kv_bits: int | None,
+) -> None:
+    if kv_bits is None:
+        return
+    for e, c in enumerate(prompt_cache):
+        if (
+            hasattr(c, "to_quantized") and c.offset >= quantized_kv_start  # type: ignore
+        ):
+            prompt_cache[e] = c.to_quantized(group_size=kv_group_size, bits=kv_bits)


 def warmup_inference(
@@ -162,7 +120,6 @@ def mlx_generate(
    tokenizer: TokenizerWrapper,
    task: ChatCompletionTaskParams,
    prompt: str,
-    kv_prefix_cache: KVPrefixCache | None = None,
 ) -> Generator[GenerationResponse]:
    # Ensure that generation stats only contains peak memory for this generation
    mx.reset_peak_memory()
@@ -174,16 +131,7 @@ def mlx_generate(
    if task.seed is not None:
        mx.random.seed(task.seed)

-    # Do not use the prefix cache if we are trying to do benchmarks.
-    if is_bench:
-        kv_prefix_cache = None
-
-    # Use prefix cache if available, otherwise create fresh cache
-    if kv_prefix_cache is None:
-        caches = make_kv_cache(model=model)
-        prompt_tokens = encode_prompt(tokenizer, prompt)
-    else:
-        caches, prompt_tokens = kv_prefix_cache.get_kv_cache(model, tokenizer, prompt)
+    caches = make_kv_cache(model=model)

    logits_processors: list[Callable[[mx.array, mx.array], mx.array]] = []
    if is_bench:
@@ -196,19 +144,11 @@ def mlx_generate(
        top_p=task.top_p if task.top_p is not None else 1.0,
    )

-    # Prefill cache with all tokens except the last one
-    prefill_tps = prefill(model, tokenizer, sampler, prompt_tokens[-1:], caches)
-
-    # stream_generate starts from the last token
-    last_token = prompt_tokens[-1:]
-
    max_tokens = task.max_tokens or MAX_TOKENS
-    generated_text_parts: list[str] = []
-    generation_start_time = time.perf_counter()
    for out in stream_generate(
        model=model,
        tokenizer=tokenizer,
-        prompt=last_token,
+        prompt=prompt,
        max_tokens=max_tokens,
        sampler=sampler,
        logits_processors=logits_processors,
@@ -218,13 +158,12 @@ def mlx_generate(
        kv_group_size=KV_GROUP_SIZE,
        kv_bits=KV_BITS,
    ):
-        generated_text_parts.append(out.text)
        logger.info(out.text)

        stats: GenerationStats | None = None
        if out.finish_reason is not None:
            stats = GenerationStats(
-                prompt_tps=float(prefill_tps or out.prompt_tps),
+                prompt_tps=float(out.prompt_tps),
                generation_tps=float(out.generation_tps),
                prompt_tokens=int(out.prompt_tokens),
                generation_tokens=int(out.generation_tokens),
@@ -246,22 +185,6 @@ def mlx_generate(
        )

        if out.finish_reason is not None:
-            # Log generation stats
-            generation_elapsed = time.perf_counter() - generation_start_time
-            generated_tokens = len(generated_text_parts)
-            generation_tps = (
-                generated_tokens / generation_elapsed if generation_elapsed > 0 else 0.0
-            )
-            logger.debug(
-                f"Generation complete: prefill {prompt_tokens} tokens @ "
-                f"{prefill_tps:.1f} tok/s, generated {generated_tokens} tokens @ "
-                f"{generation_tps:.1f} tok/s"
-            )
-            # Save cache for future prefix matching (clear first to keep only the last one)
-            if kv_prefix_cache is not None:
-                kv_prefix_cache.clear()
-                full_prompt = prompt + "".join(generated_text_parts)
-                kv_prefix_cache.add_kv_cache(tokenizer, full_prompt, caches)
            break

        # TODO: Do we want an mx_barrier?
--- a/src/exo/worker/engines/mlx/utils_mlx.py
+++ b/src/exo/worker/engines/mlx/utils_mlx.py
@@ -170,10 +170,10 @@ def mlx_distributed_init(

                # TODO: update once upstream fixes
                logger.info(
-                    f"rank {rank} MLX_JACCL_DEVICES: {coordination_file} with devices: {jaccl_devices_json}"
+                    f"rank {rank} MLX_IBV_DEVICES: {coordination_file} with devices: {jaccl_devices_json}"
                )
                logger.info(f"rank {rank} MLX_JACCL_COORDINATOR: {jaccl_coordinator}")
-                os.environ["MLX_JACCL_DEVICES"] = coordination_file
+                os.environ["MLX_IBV_DEVICES"] = coordination_file
                os.environ["MLX_RANK"] = str(rank)
                os.environ["MLX_JACCL_COORDINATOR"] = jaccl_coordinator
                group = mx.distributed.init(backend="jaccl", strict=True)
--- a/src/exo/worker/runner/runner.py
+++ b/src/exo/worker/runner/runner.py
@@ -70,7 +70,6 @@ from exo.worker.engines.image import (
    warmup_image_generator,
 )
 from exo.worker.engines.mlx import Model
-from exo.worker.engines.mlx.cache import KVPrefixCache
 from exo.worker.engines.mlx.generator.generate import mlx_generate, warmup_inference
 from exo.worker.engines.mlx.utils_mlx import (
    apply_chat_template,
@@ -104,7 +103,6 @@ def main(
    model: Model | DistributedImageModel | None = None
    tokenizer = None
    group = None
-    kv_prefix_cache: KVPrefixCache | None = None

    current_status: RunnerStatus = RunnerIdle()
    logger.info("runner created")
@@ -173,9 +171,6 @@ def main(
                            f"Unknown model task(s): {shard_metadata.model_card.tasks}"
                        )

-                    if ModelTask.TextGeneration in shard_metadata.model_card.tasks:
-                        kv_prefix_cache = KVPrefixCache()
-
                    current_status = RunnerLoaded()
                    logger.info("runner loaded")
                case StartWarmup() if isinstance(current_status, RunnerLoaded):
@@ -243,7 +238,6 @@ def main(
                            tokenizer=tokenizer,
                            task=task_params,
                            prompt=prompt,
-                            kv_prefix_cache=kv_prefix_cache,
                        )

                        # GPT-OSS specific parsing to match other model formats.
@@ -618,7 +612,7 @@ def _process_image_response(
        command_id=command_id,
        model_id=shard_metadata.model_card.model_id,
        event_sender=event_sender,
-        image_index=response.image_index,
+        image_index=response.partial_index if is_partial else image_index,
        is_partial=is_partial,
        partial_index=response.partial_index if is_partial else None,
        total_partials=response.total_partials if is_partial else None,
--- a/uv.lock
+++ b/uv.lock
@@ -376,8 +376,8 @@ dependencies = [
    { name = "hypercorn", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
    { name = "loguru", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
    { name = "mflux", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
-    { name = "mlx", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
-    { name = "mlx", extra = ["cpu"], marker = "sys_platform == 'linux'" },
+    { name = "mlx", version = "0.30.3", source = { registry = "https://pypi.org/simple" }, extra = ["cpu"], marker = "sys_platform == 'linux'" },
+    { name = "mlx", version = "0.30.4.dev20260121+fbe306f9", source = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git#fbe306f92a47d9b887ee7af2e3af6f1b9e28e663" }, marker = "sys_platform == 'darwin'" },
    { name = "mlx-lm", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
    { name = "openai-harmony", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
    { name = "pillow", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
@@ -413,7 +413,7 @@ requires-dist = [
    { name = "hypercorn", specifier = ">=0.18.0" },
    { name = "loguru", specifier = ">=0.7.3" },
    { name = "mflux", specifier = ">=0.14.2" },
-    { name = "mlx", marker = "sys_platform == 'darwin'", specifier = "==0.30.3" },
+    { name = "mlx", marker = "sys_platform == 'darwin'", git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git" },
    { name = "mlx", extras = ["cpu"], marker = "sys_platform == 'linux'", specifier = "==0.30.3" },
    { name = "mlx-lm", git = "https://github.com/AlexCheema/mlx-lm.git?rev=fix-transformers-5.0.0rc2" },
    { name = "openai-harmony", specifier = ">=0.0.8" },
@@ -458,16 +458,6 @@ dev = [
    { name = "pytest-asyncio", specifier = ">=1.0.0" },
 ]

-[[package]]
-name = "tomlkit"
-version = "0.14.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/c3/af/14b24e41977adb296d6bd1fb59402cf7d60ce364f90c890bd2ec65c43b5a/tomlkit-0.14.0.tar.gz", hash = "sha256:cf00efca415dbd57575befb1f6634c4f42d2d87dbba376128adb42c121b87064", size = 187167 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/b5/11/87d6d29fb5d237229d67973a6c9e06e048f01cf4994dee194ab0ea841814/tomlkit-0.14.0-py3-none-any.whl", hash = "sha256:592064ed85b40fa213469f81ac584f67a4f2992509a7c3ea2d632208623a3680", size = 39310 },
-]
-
-
 [[package]]
 name = "fastapi"
 version = "0.128.0"
@@ -1004,8 +994,8 @@ dependencies = [
    { name = "fonttools", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
    { name = "huggingface-hub", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
    { name = "matplotlib", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
-    { name = "mlx", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
-    { name = "mlx", extra = ["cuda13"], marker = "sys_platform == 'linux'" },
+    { name = "mlx", version = "0.30.3", source = { registry = "https://pypi.org/simple" }, extra = ["cuda13"], marker = "sys_platform == 'linux'" },
+    { name = "mlx", version = "0.30.4.dev20260121+fbe306f9", source = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git#fbe306f92a47d9b887ee7af2e3af6f1b9e28e663" }, marker = "sys_platform == 'darwin'" },
    { name = "numpy", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
    { name = "opencv-python", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
    { name = "piexif", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
@@ -1032,18 +1022,12 @@ wheels = [
 name = "mlx"
 version = "0.30.3"
 source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "mlx-metal", marker = "sys_platform == 'darwin'" },
+resolution-markers = [
+    "sys_platform == 'linux'",
 ]
 wheels = [
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 version = "2.9.1"
Author	SHA1	Message	Date
Alex Cheema	55b67e2be2	Merge branch 'main' into releases/v1.0.64	2026-01-23 01:40:20 +00:00
Ryuichi Leo Takashige	30cfad9b68	Use custom fork	2026-01-22 22:19:35 +00:00