Format

justfile

@@ -1,7 +1,7 @@
 export NIX_CONFIG := "extra-experimental-features = nix-command flakes"
 
 fmt:
-    treefmt || nix fmt
+    nix fmt
 
 lint:
     uv run ruff check --fix

pyproject.toml

@@ -19,7 +19,7 @@ dependencies = [
     "anyio==4.11.0",
     "mlx==0.30.3; sys_platform == 'darwin'",
     "mlx[cpu]==0.30.3; sys_platform == 'linux'",
-    "mlx-lm==0.30.5",
+    "mlx-lm",
     "tiktoken>=0.12.0", # required for kimi k2 tokenizer
     "hypercorn>=0.18.0",
     "openai-harmony>=0.0.8",
@@ -63,6 +63,7 @@ members = [
 
 [tool.uv.sources]
 exo_pyo3_bindings = { workspace = true }
+mlx-lm = { git = "https://github.com/ml-explore/mlx-lm", branch = "main" }
 # Uncomment to use local mlx/mlx-lm development versions:
 # mlx = { path = "/Users/Shared/mlx", editable=true }
 # mlx-lm = { path = "/Users/Shared/mlx-lm", editable=true }

src/exo/shared/models/model_cards.py

@@ -121,6 +121,14 @@ MODEL_CARDS: dict[str, ModelCard] = {
         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"),

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

@@ -23,6 +23,7 @@ from mlx_lm.models.glm4_moe_lite import Glm4MoeLiteDecoderLayer, Glm4MoeLiteMLP
 from mlx_lm.models.glm4_moe_lite import Model as GLM4MoeLiteModel
 from mlx_lm.models.gpt_oss import GptOssMoeModel
 from mlx_lm.models.gpt_oss import Model as GptOssModel
+from mlx_lm.models.kimi_k25 import Model as KimiK25Model
 from mlx_lm.models.llama import Model as LlamaModel
 from mlx_lm.models.minimax import Model as MiniMaxModel
 from mlx_lm.models.ministral3 import Model as Ministral3Model
@@ -344,7 +345,7 @@ def tensor_auto_parallel(
             all_to_sharded_linear_in_place,
             sharded_to_all_linear_in_place,
         )
-    elif isinstance(model, (DeepseekV3Model, DeepseekV32Model)):
+    elif isinstance(model, (DeepseekV3Model, DeepseekV32Model, KimiK25Model)):
         tensor_parallel_sharding_strategy = DeepSeekShardingStrategy(
             group,
             all_to_sharded_linear,
@@ -453,7 +454,7 @@ def _set_layers(model: nn.Module, layers: list[_LayerCallable]) -> None:
 
         # Update DeepSeek V3 specific parameters when layers are shrunk
         if isinstance(
-            model, (DeepseekV3Model, DeepseekV32Model, Glm4MoeModel)
+            model, (DeepseekV3Model, DeepseekV32Model, Glm4MoeModel, KimiK25Model)
         ) and hasattr(inner_model_instance, "num_layers"):
             logger.info(
                 f"Setting num_layers to {len(layers)} for model {model.model.__class__.__name__}"

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

@@ -259,10 +259,10 @@ def shard_and_load(
 
     logger.info(f"Group size: {group.size()}, group rank: {group.rank()}")
 
-    # Estimate timeout based on model size
-    base_timeout = float(os.environ.get("EXO_MODEL_LOAD_TIMEOUT", "60"))
+    # Estimate timeout based on model size (5x default for large queued workloads)
+    base_timeout = float(os.environ.get("EXO_MODEL_LOAD_TIMEOUT", "300"))
     model_size_gb = get_weights_size(shard_metadata).in_bytes / (1024**3)
-    timeout_seconds = base_timeout + model_size_gb / 5
+    timeout_seconds = base_timeout + model_size_gb
     logger.info(
         f"Evaluating model parameters with timeout of {timeout_seconds:.0f}s "
         f"(model size: {model_size_gb:.1f}GB)"
@@ -339,8 +339,35 @@ def load_tokenizer_for_model_id(
 
     # Kimi uses a custom TikTokenTokenizer that transformers 5.x can't load via AutoTokenizer
     if "kimi-k2" in model_id_lower:
+        import importlib.util
+        import types
+
         sys.path.insert(0, str(model_path))
-        from tokenization_kimi import TikTokenTokenizer  # type: ignore[import-not-found]  # noqa: I001
+
+        # Load tool_declaration_ts first (tokenization_kimi imports it with relative import)
+        tool_decl_path = model_path / "tool_declaration_ts.py"
+        if tool_decl_path.exists():
+            spec = importlib.util.spec_from_file_location(
+                "tool_declaration_ts", tool_decl_path
+            )
+            if spec and spec.loader:
+                tool_decl_module = importlib.util.module_from_spec(spec)
+                sys.modules["tool_declaration_ts"] = tool_decl_module
+                spec.loader.exec_module(tool_decl_module)
+
+        # Load tokenization_kimi with patched source (convert relative to absolute import)
+        tok_path = model_path / "tokenization_kimi.py"
+        source = tok_path.read_text()
+        source = source.replace("from .tool_declaration_ts", "from tool_declaration_ts")
+        spec = importlib.util.spec_from_file_location("tokenization_kimi", tok_path)
+        if spec:
+            tok_module = types.ModuleType("tokenization_kimi")
+            tok_module.__file__ = str(tok_path)
+            sys.modules["tokenization_kimi"] = tok_module
+            exec(compile(source, tok_path, "exec"), tok_module.__dict__)  # noqa: S102
+            TikTokenTokenizer = tok_module.TikTokenTokenizer  # type: ignore[attr-defined]  # noqa: N806
+        else:
+            from tokenization_kimi import TikTokenTokenizer  # type: ignore[import-not-found]  # noqa: I001
 
         hf_tokenizer: Any = TikTokenTokenizer.from_pretrained(model_path)  # pyright: ignore[reportUnknownVariableType,reportUnknownMemberType]
 

uv.lock

@@ -415,7 +415,7 @@ requires-dist = [
     { name = "mflux", specifier = "==0.15.4" },
     { name = "mlx", marker = "sys_platform == 'darwin'", specifier = "==0.30.3" },
     { name = "mlx", extras = ["cpu"], marker = "sys_platform == 'linux'", specifier = "==0.30.3" },
-    { name = "mlx-lm", specifier = "==0.30.5" },
+    { name = "mlx-lm", git = "https://github.com/ml-explore/mlx-lm?branch=main" },
     { name = "openai-harmony", specifier = ">=0.0.8" },
     { name = "pillow", specifier = ">=11.0,<12.0" },
     { name = "psutil", specifier = ">=7.0.0" },
@@ -1073,7 +1073,7 @@ wheels = [
 [[package]]
 name = "mlx-lm"
 version = "0.30.5"
-source = { registry = "https://pypi.org/simple" }
+source = { git = "https://github.com/ml-explore/mlx-lm?branch=main#96699e6dadb13b82b28285bb131a0741997d19ae" }
 dependencies = [
     { name = "jinja2", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "mlx", marker = "sys_platform == 'darwin'" },
@@ -1083,10 +1083,6 @@ dependencies = [
     { name = "sentencepiece", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "transformers", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/0b/90/4469d9f75f196e6255f59a89441abe0079925d30a001462e1c1c4bc4e6a1/mlx_lm-0.30.5.tar.gz", hash = "sha256:9e6cb258c65b766c6af25cb90958aef40acab67139f05839eef19864cb3154f6", size = 262367, upload-time = "2026-01-25T15:29:30.125Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/89/ba/66db6e1e5f1ef506655b562932f6bd8f72600116d5f31f92d71c1f200b3f/mlx_lm-0.30.5-py3-none-any.whl", hash = "sha256:a80bc8e3efdebe81813b0f6eb403fb66a7a15071e256f4e7102ada986acb75bb", size = 366716, upload-time = "2026-01-25T15:29:28.29Z" },
-]
 
 [[package]]
 name = "mlx-metal"
@@ -2285,7 +2281,7 @@ wheels = [
 
 [[package]]
 name = "transformers"
-version = "5.0.0rc3"
+version = "5.0.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "filelock", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
@@ -2294,15 +2290,14 @@ dependencies = [
     { name = "packaging", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "pyyaml", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "regex", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
-    { name = "requests", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "safetensors", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "tokenizers", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "tqdm", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "typer-slim", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/3f/a3/7c116a8d85f69ea7749cf4c2df79e64c35d028e5fc7ea0168f299d03b8c7/transformers-5.0.0rc3.tar.gz", hash = "sha256:a0315b92b7e087617ade42ec9e6e92ee7620541cc5d6a3331886c52cbe306f5c", size = 8388520, upload-time = "2026-01-14T16:49:02.952Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/bc/79/845941711811789c85fb7e2599cea425a14a07eda40f50896b9d3fda7492/transformers-5.0.0.tar.gz", hash = "sha256:5f5634efed6cf76ad068cc5834c7adbc32db78bbd6211fb70df2325a9c37dec8", size = 8424830, upload-time = "2026-01-26T10:46:46.813Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/1e/f2/ae2b8968764253bdf38a48dee3c299b8d0bedf7c8ffbe3449fca9bd95338/transformers-5.0.0rc3-py3-none-any.whl", hash = "sha256:383fad27f4f73092d330e45fae384681e5c8521e1dc1cf6cb1a297780e68bf2d", size = 10107087, upload-time = "2026-01-14T16:48:59.393Z" },
+    { url = "https://files.pythonhosted.org/packages/52/f3/ac976fa8e305c9e49772527e09fbdc27cc6831b8a2f6b6063406626be5dd/transformers-5.0.0-py3-none-any.whl", hash = "sha256:587086f249ce64c817213cf36afdb318d087f790723e9b3d4500b97832afd52d", size = 10142091, upload-time = "2026-01-26T10:46:43.88Z" },
 ]
 
 [[package]]