replace model ID string matching with architecture-based checks in tokenizer loading

Use config.json model_type field instead of fragile substring matching on
model IDs for architecture detection in tokenizer loading. Falls back to
string matching when config.json is unavailable.

Closes #1371

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

app/EXO/EXO/ExoProcessController.swift

@@ -126,11 +126,37 @@ final class ExoProcessController: ObservableObject {
             return
         }
         process.terminationHandler = nil
-        if process.isRunning {
-            process.terminate()
-        }
-        self.process = nil
         status = .stopped
+
+        guard process.isRunning else {
+            self.process = nil
+            return
+        }
+
+        let proc = process
+        self.process = nil
+
+        Task.detached {
+            proc.interrupt()
+
+            for _ in 0..<50 {
+                if !proc.isRunning { return }
+                try? await Task.sleep(nanoseconds: 100_000_000)
+            }
+
+            if proc.isRunning {
+                proc.terminate()
+            }
+
+            for _ in 0..<30 {
+                if !proc.isRunning { return }
+                try? await Task.sleep(nanoseconds: 100_000_000)
+            }
+
+            if proc.isRunning {
+                kill(proc.processIdentifier, SIGKILL)
+            }
+        }
     }
 
     func restart() {

bench/bench.toml

@@ -0,0 +1,7 @@
+# Canary benchmark manifest
+#
+# Lists the suite files to include. Each file defines benchmarks
+# with shared constraints, topology, and default args.
+include = [
+    "single-m3-ultra.toml",
+]

bench/single-m3-ultra.toml

@@ -0,0 +1,189 @@
+# Single-node M3 Ultra benchmarks
+#
+# Shared constraints applied to ALL benchmarks in this file.
+constraints = [
+    "All(MacOsBuild(=25D125))",
+    "Hosts(=1)",
+    "All(Chip(m3_ultra))",
+    "All(GpuCores(=80))",
+]
+
+[topology]
+type = "none"
+
+# Default args merged into each benchmark's args (benchmark-level args win).
+[defaults]
+pp = [512, 2048, 8192, 16384]
+tg = 128
+
+[[benchmark]]
+model = "mlx-community/Meta-Llama-3.1-70B-Instruct-4bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/gpt-oss-120b-MXFP4-Q8"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/GLM-4.7-Flash-8bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Qwen3-Coder-Next-6bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Qwen3-30B-A3B-8bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Qwen3-0.6B-4bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Qwen3-0.6B-8bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Llama-3.2-1B-Instruct-4bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Llama-3.2-3B-Instruct-4bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Llama-3.2-3B-Instruct-8bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Meta-Llama-3.1-8B-Instruct-8bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/gpt-oss-20b-MXFP4-Q8"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Qwen3-30B-A3B-4bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/GLM-4.7-Flash-4bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/GLM-4.7-Flash-5bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/GLM-4.7-Flash-6bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Llama-3.3-70B-Instruct-4bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Qwen3-Coder-Next-4bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Qwen3-Coder-Next-5bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Qwen3-Coder-Next-8bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Qwen3-Next-80B-A3B-Thinking-8bit"
+extra_constraints = ["All(Memory(>=96GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Llama-3.3-70B-Instruct-8bit"
+extra_constraints = ["All(Memory(>=256GiB))"]
+
+[[benchmark]]
+model = "mlx-community/llama-3.3-70b-instruct-fp16"
+extra_constraints = ["All(Memory(>=256GiB))"]
+
+[[benchmark]]
+model = "mlx-community/GLM-4.5-Air-8bit"
+extra_constraints = ["All(Memory(>=256GiB))"]
+
+[[benchmark]]
+model = "mlx-community/GLM-4.5-Air-bf16"
+extra_constraints = ["All(Memory(>=256GiB))"]
+
+[[benchmark]]
+model = "mlx-community/GLM-4.7-4bit"
+extra_constraints = ["All(Memory(>=256GiB))"]
+
+[[benchmark]]
+model = "mlx-community/MiniMax-M2.1-3bit"
+extra_constraints = ["All(Memory(>=256GiB))"]
+
+[[benchmark]]
+model = "mlx-community/MiniMax-M2.1-8bit"
+extra_constraints = ["All(Memory(>=256GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit"
+extra_constraints = ["All(Memory(>=256GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Qwen3-Coder-Next-bf16"
+extra_constraints = ["All(Memory(>=256GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Step-3.5-Flash-4bit"
+extra_constraints = ["All(Memory(>=256GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Step-3.5-Flash-6bit"
+extra_constraints = ["All(Memory(>=256GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Step-3.5-Flash-8Bit"
+extra_constraints = ["All(Memory(>=256GiB))"]
+
+[[benchmark]]
+model = "mlx-community/DeepSeek-V3.1-4bit"
+extra_constraints = ["All(Memory(>=512GiB))"]
+
+[[benchmark]]
+model = "mlx-community/GLM-4.7-6bit"
+extra_constraints = ["All(Memory(>=512GiB))"]
+
+[[benchmark]]
+model = "mlx-community/GLM-4.7-8bit-gs32"
+extra_constraints = ["All(Memory(>=512GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit"
+extra_constraints = ["All(Memory(>=512GiB))"]
+
+[[benchmark]]
+model = "mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"
+extra_constraints = ["All(Memory(>=512GiB))"]

pyproject.toml

@@ -132,7 +132,7 @@ markers = [
 env = [
   "EXO_TESTS=1"
 ]
-addopts = "-m 'not slow'"
+addopts = "-m 'not slow' --ignore=tests/start_distributed_test.py"
 filterwarnings = [
     "ignore:builtin type Swig:DeprecationWarning",
 ]

src/exo/download/coordinator.py

@@ -14,6 +14,7 @@ from exo.download.download_utils import (
     map_repo_download_progress_to_download_progress_data,
 )
 from exo.download.shard_downloader import ShardDownloader
+from exo.shared.constants import EXO_MODELS_DIR
 from exo.shared.models.model_cards import ModelId
 from exo.shared.types.commands import (
     CancelDownload,
@@ -63,6 +64,9 @@ class DownloadCoordinator:
         self.event_sender, self.event_receiver = channel[Event]()
         self.shard_downloader.on_progress(self._download_progress_callback)
 
+    def _model_dir(self, model_id: ModelId) -> str:
+        return str(EXO_MODELS_DIR / model_id.normalize())
+
     async def _download_progress_callback(
         self, callback_shard: ShardMetadata, progress: RepoDownloadProgress
     ) -> None:
@@ -74,6 +78,7 @@ class DownloadCoordinator:
                 shard_metadata=callback_shard,
                 node_id=self.node_id,
                 total_bytes=progress.total_bytes,
+                model_directory=self._model_dir(model_id),
             )
             self.download_status[model_id] = completed
             await self.event_sender.send(
@@ -93,6 +98,7 @@ class DownloadCoordinator:
                 download_progress=map_repo_download_progress_to_download_progress_data(
                     progress
                 ),
+                model_directory=self._model_dir(model_id),
             )
             self.download_status[model_id] = ongoing
             await self.event_sender.send(
@@ -170,7 +176,11 @@ class DownloadCoordinator:
                 return
 
         # Emit pending status
-        progress = DownloadPending(shard_metadata=shard, node_id=self.node_id)
+        progress = DownloadPending(
+            shard_metadata=shard,
+            node_id=self.node_id,
+            model_directory=self._model_dir(model_id),
+        )
         self.download_status[model_id] = progress
         await self.event_sender.send(NodeDownloadProgress(download_progress=progress))
 
@@ -184,6 +194,7 @@ class DownloadCoordinator:
                 shard_metadata=shard,
                 node_id=self.node_id,
                 total_bytes=initial_progress.total_bytes,
+                model_directory=self._model_dir(model_id),
             )
             self.download_status[model_id] = completed
             await self.event_sender.send(
@@ -206,6 +217,7 @@ class DownloadCoordinator:
             download_progress=map_repo_download_progress_to_download_progress_data(
                 initial_progress
             ),
+            model_directory=self._model_dir(model_id),
         )
         self.download_status[model_id] = status
         self.event_sender.send_nowait(NodeDownloadProgress(download_progress=status))
@@ -219,6 +231,7 @@ class DownloadCoordinator:
                     shard_metadata=shard,
                     node_id=self.node_id,
                     error_message=str(e),
+                    model_directory=self._model_dir(model_id),
                 )
                 self.download_status[model_id] = failed
                 await self.event_sender.send(
@@ -253,6 +266,7 @@ class DownloadCoordinator:
             pending = DownloadPending(
                 shard_metadata=current_status.shard_metadata,
                 node_id=self.node_id,
+                model_directory=self._model_dir(model_id),
             )
             await self.event_sender.send(
                 NodeDownloadProgress(download_progress=pending)
@@ -295,11 +309,18 @@ class DownloadCoordinator:
                             node_id=self.node_id,
                             shard_metadata=progress.shard,
                             total_bytes=progress.total_bytes,
+                            model_directory=self._model_dir(
+                                progress.shard.model_card.model_id
+                            ),
                         )
                     elif progress.status in ["in_progress", "not_started"]:
                         if progress.downloaded_bytes_this_session.in_bytes == 0:
                             status = DownloadPending(
-                                node_id=self.node_id, shard_metadata=progress.shard
+                                node_id=self.node_id,
+                                shard_metadata=progress.shard,
+                                model_directory=self._model_dir(
+                                    progress.shard.model_card.model_id
+                                ),
                             )
                         else:
                             status = DownloadOngoing(
@@ -308,6 +329,9 @@ class DownloadCoordinator:
                                 download_progress=map_repo_download_progress_to_download_progress_data(
                                     progress
                                 ),
+                                model_directory=self._model_dir(
+                                    progress.shard.model_card.model_id
+                                ),
                             )
                     else:
                         continue

src/exo/main.py

@@ -136,6 +136,8 @@ class Node:
 
     async def run(self):
         async with self._tg as tg:
+            signal.signal(signal.SIGINT, lambda _, __: self.shutdown())
+            signal.signal(signal.SIGTERM, lambda _, __: self.shutdown())
             tg.start_soon(self.router.run)
             tg.start_soon(self.election.run)
             if self.download_coordinator:
@@ -147,8 +149,6 @@ class Node:
             if self.api:
                 tg.start_soon(self.api.run)
             tg.start_soon(self._elect_loop)
-            signal.signal(signal.SIGINT, lambda _, __: self.shutdown())
-            signal.signal(signal.SIGTERM, lambda _, __: self.shutdown())
 
     def shutdown(self):
         # if this is our second call to shutdown, just sys.exit

src/exo/master/adapters/responses.py

@@ -144,8 +144,8 @@ async def collect_responses_response(
             for tool in chunk.tool_calls:
                 function_call_items.append(
                     ResponseFunctionCallItem(
-                        id=f"fc_{tool.id}",
-                        call_id=f"call_{tool.id}",
+                        id=tool.id,
+                        call_id=tool.id,
                         name=tool.name,
                         arguments=tool.arguments,
                     )

src/exo/shared/apply.py

@@ -218,11 +218,6 @@ def apply_node_timed_out(event: NodeTimedOut, state: State) -> State:
         key: value for key, value in state.downloads.items() if key != event.node_id
     }
     # Clean up all granular node mappings
-    node_identities = {
-        key: value
-        for key, value in state.node_identities.items()
-        if key != event.node_id
-    }
     node_memory = {
         key: value for key, value in state.node_memory.items() if key != event.node_id
     }
@@ -263,7 +258,6 @@ def apply_node_timed_out(event: NodeTimedOut, state: State) -> State:
             "downloads": downloads,
             "topology": topology,
             "last_seen": last_seen,
-            "node_identities": node_identities,
             "node_memory": node_memory,
             "node_disk": node_disk,
             "node_system": node_system,

src/exo/shared/models/model_cards.py

@@ -165,6 +165,7 @@ def is_custom_card(model_id: ModelId) -> bool:
 class ConfigData(BaseModel):
     model_config = {"extra": "ignore"}  # Allow unknown fields
 
+    model_type: str | None = None
     architectures: list[str] | None = None
     hidden_size: Annotated[int, Field(ge=0)] | None = None
     layer_count: int = Field(
@@ -200,6 +201,7 @@ class ConfigData(BaseModel):
             return data
 
         for field in [
+            "model_type",
             "architectures",
             "hidden_size",
             "num_hidden_layers",

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

@@ -26,6 +26,7 @@ class DownloadProgressData(CamelCaseModel):
 class BaseDownloadProgress(TaggedModel):
     node_id: NodeId
     shard_metadata: ShardMetadata
+    model_directory: str = ""
 
 
 class DownloadPending(BaseDownloadProgress):

src/exo/utils/banner.py

@@ -1,5 +1,7 @@
+import sys
+
+
 def print_startup_banner(port: int) -> None:
-    """Print a prominent startup banner with API endpoint information."""
     dashboard_url = f"http://localhost:{port}"
     banner = f"""
 ╔═══════════════════════════════════════════════════════════════════════╗
@@ -27,4 +29,4 @@ def print_startup_banner(port: int) -> None:
 
 """
 
-    print(banner)
+    print(banner, file=sys.stderr)

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

@@ -306,7 +306,7 @@ def mlx_generate(
     max_stop_len = max((len(s) for s in stop_sequences), default=0)
 
     mx_barrier(group)
-    logger.info("Ready to prefill")
+    logger.info("Starting prefill")
 
     # Prefill cache with all tokens except the last one
     prefill_tps, prefill_tokens, ssm_snapshots_list = prefill(

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

@@ -269,19 +269,52 @@ def get_tokenizer(model_path: Path, shard_metadata: ShardMetadata) -> TokenizerW
     return load_tokenizer_for_model_id(shard_metadata.model_card.model_id, model_path)
 
 
-def get_eos_token_ids_for_model(model_id: ModelId) -> list[int] | None:
-    """
-    Get the EOS token IDs for a model based on its ID.
+def _read_model_type_from_config(model_path: Path) -> str | None:
+    """Read the model_type field from config.json at the given model path.
 
-    Some models require explicit EOS token configuration that isn't in their
-    tokenizer config. This function returns the known EOS token IDs for such models.
+    Returns None if config.json doesn't exist or doesn't contain model_type.
+    """
+    config_path = model_path / "config.json"
+    if not config_path.exists():
+        return None
+    try:
+        with open(config_path) as f:
+            config: dict[str, Any] = json.load(f)  # pyright: ignore[reportAny]
+        model_type: Any = config.get("model_type")
+        if model_type is None:
+            text_config: Any = config.get("text_config")
+            if isinstance(text_config, dict):
+                model_type = text_config.get("model_type")  # pyright: ignore[reportUnknownMemberType,reportUnknownVariableType]
+        return model_type if isinstance(model_type, str) else None
+    except (json.JSONDecodeError, OSError):
+        return None
+
+
+def get_eos_token_ids_for_model(
+    model_id: ModelId, model_type: str | None = None
+) -> list[int] | None:
+    """Get the EOS token IDs for a model based on its architecture type.
+
+    Uses model_type from config.json when available, falls back to model_id
+    string matching for backward compatibility.
 
     Args:
         model_id: The HuggingFace model ID
+        model_type: The model_type field from config.json (e.g., "kimi", "glm4")
 
     Returns:
         List of EOS token IDs, or None if the model uses standard tokenizer config
     """
+    if model_type is not None:
+        if model_type == "kimi":
+            return [163586]
+        elif model_type == "glm4_moe_lite":
+            # 154820: <|endoftext|>, 154827: <|user|>, 154829: <|observation|>
+            return [154820, 154827, 154829]
+        elif model_type.startswith("glm"):
+            return [151336, 151329, 151338]
+
+    # Fallback: string matching on model_id
     model_id_lower = model_id.lower()
     if "kimi-k2" in model_id_lower:
         return [163586]
@@ -296,11 +329,10 @@ def get_eos_token_ids_for_model(model_id: ModelId) -> list[int] | None:
 def load_tokenizer_for_model_id(
     model_id: ModelId, model_path: Path
 ) -> TokenizerWrapper:
-    """
-    Load tokenizer for a model given its ID and local path.
+    """Load tokenizer for a model given its ID and local path.
 
-    This is the core tokenizer loading logic, handling special cases for different
-    model families (Kimi, GLM, etc.) and transformers 5.x compatibility.
+    Uses model_type from config.json for architecture detection when available,
+    falling back to model_id string matching for backward compatibility.
 
     Args:
         model_id: The HuggingFace model ID (e.g., "moonshotai/Kimi-K2-Instruct")
@@ -309,11 +341,21 @@ def load_tokenizer_for_model_id(
     Returns:
         TokenizerWrapper instance configured for the model
     """
+    model_type = _read_model_type_from_config(model_path)
     model_id_lower = model_id.lower()
-    eos_token_ids = get_eos_token_ids_for_model(model_id)
+    eos_token_ids = get_eos_token_ids_for_model(model_id, model_type=model_type)
+
+    is_kimi = (
+        model_type == "kimi" if model_type is not None else "kimi-k2" in model_id_lower
+    )
+    is_gemma3 = (
+        model_type == "gemma3"
+        if model_type is not None
+        else "gemma-3" in model_id_lower
+    )
 
     # Kimi uses a custom TikTokenTokenizer that transformers 5.x can't load via AutoTokenizer
-    if "kimi-k2" in model_id_lower:
+    if is_kimi:
         import importlib.util
         import types
 
@@ -353,7 +395,13 @@ def load_tokenizer_for_model_id(
             return list(hf_tokenizer.model.encode(text, allowed_special="all"))  # pyright: ignore[reportUnknownMemberType,reportUnknownArgumentType]
 
         hf_tokenizer.encode = _patched_encode
-        return TokenizerWrapper(hf_tokenizer, eos_token_ids=eos_token_ids)
+        return TokenizerWrapper(
+            hf_tokenizer,
+            eos_token_ids=eos_token_ids,
+            tool_call_start="<|tool_calls_section_begin|>",
+            tool_call_end="<|tool_calls_section_end|>",
+            tool_parser=_parse_kimi_tool_calls,
+        )
 
     tokenizer = load_tokenizer(
         model_path,
@@ -361,7 +409,7 @@ def load_tokenizer_for_model_id(
         eos_token_ids=eos_token_ids,
     )
 
-    if "gemma-3" in model_id_lower:
+    if is_gemma3:
         gemma_3_eos_id = 1
         gemma_3_end_of_turn_id = 106
         if tokenizer.eos_token_ids is not None:
@@ -585,3 +633,41 @@ def mx_barrier(group: Group | None):
             mx.array(1.0), group=group, stream=mx.default_stream(mx.Device(mx.cpu))
         )
     )
+
+
+def _parse_kimi_tool_calls(text: str):
+    import regex as re
+
+    # kimi has a fixed function naming scheme, with a json formatted arg
+    #   functions.multiply:0<|tool_call_argument_begin|>{"a": 2, "b": 3}
+    _func_name_regex = re.compile(
+        r"^\s*((?:functions\.)?(.+?):\d+)\s*<\|tool_call_argument_begin\|>", re.DOTALL
+    )
+    _func_arg_regex = re.compile(r"<\|tool_call_argument_begin\|>\s*(.*)\s*", re.DOTALL)
+    _tool_call_split_regex = re.compile(
+        r"<\|tool_call_begin\|>(.*?)<\|tool_call_end\|>", re.DOTALL
+    )
+
+    def _parse_single_tool(text: str) -> dict[str, Any]:
+        func_name_match = _func_name_regex.search(text)
+        if func_name_match is None:
+            raise ValueError("No tool call found.")
+        tool_call_id = func_name_match.group(1)  # e.g. "functions.get_weather:0"
+        func_name = func_name_match.group(2)  # e.g. "get_weather"
+
+        func_args_match = _func_arg_regex.search(text)
+        if func_args_match is None:
+            raise ValueError("No tool call arguments found.")
+        func_args = func_args_match.group(1)
+        try:
+            arg_dct = json.loads(func_args)  # pyright: ignore[reportAny]
+        except Exception:
+            arg_dct = None
+
+        return dict(id=tool_call_id, name=func_name, arguments=arg_dct)
+
+    tool_matches = _tool_call_split_regex.findall(text)
+    if tool_matches:
+        return [_parse_single_tool(match) for match in tool_matches]  # pyright: ignore[reportAny]
+    else:
+        return [_parse_single_tool(text)]

src/exo/worker/runner/runner.py

@@ -1,11 +1,10 @@
 import base64
-import json
 import math
 import resource
 import time
 from collections.abc import Generator
 from functools import cache
-from typing import Any, Callable, Literal
+from typing import Literal
 
 import mlx.core as mx
 from mlx_lm.models.gpt_oss import Model as GptOssModel
@@ -16,7 +15,6 @@ from openai_harmony import (  # pyright: ignore[reportMissingTypeStubs]
     StreamableParser,
     load_harmony_encoding,
 )
-from pydantic import ValidationError
 
 from exo.shared.constants import EXO_MAX_CHUNK_SIZE, EXO_TRACING_ENABLED
 from exo.shared.models.model_cards import ModelId, ModelTask
@@ -93,6 +91,8 @@ from exo.worker.engines.mlx.utils_mlx import (
 )
 from exo.worker.runner.bootstrap import logger
 
+from .tool_parsers import ToolParser, make_mlx_parser
+
 
 def _is_primary_output_node(shard_metadata: ShardMetadata) -> bool:
     """Check if this node is the primary output node for image generation.
@@ -138,6 +138,7 @@ def main(
     inference_model: Model | None = None
     image_model: DistributedImageModel | None = None
     tokenizer = None
+    tool_parser: ToolParser | None = None
     group = None
     kv_prefix_cache: KVPrefixCache | None = None
     check_for_cancel_every: int | None = None
@@ -203,8 +204,17 @@ def main(
                             bound_instance, group, on_timeout=on_model_load_timeout
                         )
                         logger.info(
-                            f"model has_tool_calling={tokenizer.has_tool_calling}"
+                            f"model has_tool_calling={tokenizer.has_tool_calling} using tokens {tokenizer.tool_call_start}, {tokenizer.tool_call_end}"
                         )
+                        if tokenizer.has_tool_calling:
+                            assert tokenizer.tool_call_start
+                            assert tokenizer.tool_call_end
+                            assert tokenizer.tool_parser  # pyright: ignore[reportAny]
+                            tool_parser = make_mlx_parser(
+                                tokenizer.tool_call_start,
+                                tokenizer.tool_call_end,
+                                tokenizer.tool_parser,  # pyright: ignore[reportAny]
+                            )
                         kv_prefix_cache = KVPrefixCache(group)
 
                     elif (
@@ -310,31 +320,11 @@ def main(
                                 mlx_generator, tokenizer
                             )
 
-                        # Kimi-K2 has tool call sections - we don't care about them
-                        if "kimi" in shard_metadata.model_card.model_id.lower():
-                            mlx_generator = filter_kimi_tokens(mlx_generator)
-                            patch_kimi_tokenizer(tokenizer)
-
-                        # GLM models need patched parser (upstream has bug with None regex match)
-                        elif "glm" in shard_metadata.model_card.model_id.lower():
-                            patch_glm_tokenizer(tokenizer)
-
                         # GPT-OSS specific parsing to match other model formats.
-                        elif isinstance(inference_model, GptOssModel):
+                        if isinstance(inference_model, GptOssModel):
                             mlx_generator = parse_gpt_oss(mlx_generator)
-
-                        if tokenizer.has_tool_calling and not isinstance(
-                            inference_model, GptOssModel
-                        ):
-                            assert tokenizer.tool_call_start
-                            assert tokenizer.tool_call_end
-                            assert tokenizer.tool_parser  # pyright: ignore[reportAny]
-                            mlx_generator = parse_tool_calls(
-                                mlx_generator,
-                                tokenizer.tool_call_start,
-                                tokenizer.tool_call_end,
-                                tokenizer.tool_parser,  # pyright: ignore[reportAny]
-                            )
+                        elif tool_parser:
+                            mlx_generator = parse_tool_calls(mlx_generator, tool_parser)
 
                         completion_tokens = 0
                         tokens_since_last_cancel_check = 0
@@ -587,21 +577,8 @@ def get_gpt_oss_encoding():
     return encoding
 
 
-def filter_kimi_tokens(
-    responses: Generator[GenerationResponse | ToolCallResponse],
-) -> Generator[GenerationResponse]:
-    for resp in responses:
-        assert isinstance(resp, GenerationResponse)
-        if (
-            resp.text == "<|tool_calls_section_begin|>"
-            or resp.text == "<|tool_calls_section_end|>"
-        ):
-            continue
-        yield resp
-
-
 def parse_gpt_oss(
-    responses: Generator[GenerationResponse | ToolCallResponse],
+    responses: Generator[GenerationResponse],
 ) -> Generator[GenerationResponse | ToolCallResponse]:
     encoding = get_gpt_oss_encoding()
     stream = StreamableParser(encoding, role=Role.ASSISTANT)
@@ -658,9 +635,9 @@ def parse_gpt_oss(
 
 
 def parse_thinking_models(
-    responses: Generator[GenerationResponse | ToolCallResponse],
+    responses: Generator[GenerationResponse],
     tokenizer: TokenizerWrapper,
-) -> Generator[GenerationResponse | ToolCallResponse]:
+) -> Generator[GenerationResponse]:
     """
     For models that inject thinking tags in the prompt (like GLM-4.7),
     prepend the thinking tag to the output stream so the frontend
@@ -781,221 +758,55 @@ def _process_image_response(
 
 
 def parse_tool_calls(
-    responses: Generator[GenerationResponse | ToolCallResponse],
-    tool_call_start: str,
-    tool_call_end: str,
-    tool_parser: Callable[[str], dict[str, Any] | list[dict[str, Any]]],
+    responses: Generator[GenerationResponse], tool_parser: ToolParser
 ) -> Generator[GenerationResponse | ToolCallResponse]:
     in_tool_call = False
     tool_call_text_parts: list[str] = []
     for response in responses:
-        assert isinstance(response, GenerationResponse)
-        # assumption: the tool call start is one token
-        if response.text == tool_call_start:
+        if response.text.startswith(tool_parser.start_parsing):
             in_tool_call = True
-            continue
-        # assumption: the tool call end is one token
-        if in_tool_call and response.text == tool_call_end:
-            try:
-                # tool_parser returns an arbitrarily nested python dictionary
-                # we actually don't want the python dictionary, we just want to
-                # parse the top level { function: ..., arguments: ... } structure
-                # as we're just gonna hand it back to the api anyway
-                parsed = tool_parser("".join(tool_call_text_parts).strip())
-                logger.info(f"parsed {tool_call_text_parts=} into {parsed=}")
-                if isinstance(parsed, list):
-                    tools = [_validate_single_tool(tool) for tool in parsed]
-                else:
-                    tools = [_validate_single_tool(parsed)]
-                yield ToolCallResponse(
-                    tool_calls=tools, usage=response.usage, stats=response.stats
-                )
-
-            except (
-                json.JSONDecodeError,
-                ValidationError,
-                ValueError,
-                AttributeError,
-            ) as e:
-                # ValueError: our parsers raise this for malformed tool calls
-                # AttributeError: upstream parsers (e.g. glm47) may raise this when regex doesn't match
-                logger.opt(exception=e).warning("tool call parsing failed")
-                # assumption: talking about tool calls, not making a tool call
-                response.text = (
-                    tool_call_start + "".join(tool_call_text_parts) + tool_call_end
-                )
-                yield response
-
-            in_tool_call = False
-            tool_call_text_parts = []
-            continue
 
         if in_tool_call:
             tool_call_text_parts.append(response.text)
+            if response.text.endswith(tool_parser.end_parsing):
+                # parse the actual tool calls from the tool call text
+                parsed = tool_parser.parse_tool_calls(
+                    "".join(tool_call_text_parts).strip()
+                )
+                logger.info(f"parsed {tool_call_text_parts=} into {parsed=}")
+                if parsed is not None:
+                    yield ToolCallResponse(
+                        tool_calls=parsed, usage=response.usage, stats=response.stats
+                    )
+                else:
+                    logger.warning(
+                        f"tool call parsing failed for text {''.join(tool_call_text_parts)}"
+                    )
+                    response.text = "".join(tool_call_text_parts)
+                    yield response
+
+                in_tool_call = False
+                tool_call_text_parts = []
+                continue
+
             if response.finish_reason is not None:
                 logger.info(
-                    "toll call parsing interrupted, yield partial tool call as text"
+                    "tool call parsing interrupted, yield partial tool call as text"
                 )
-                yield GenerationResponse(
-                    text=tool_call_start + "".join(tool_call_text_parts),
-                    token=0,
-                    finish_reason=response.finish_reason,
-                    usage=response.usage,
-                    stats=response.stats,
+                response = response.model_copy(
+                    update={
+                        "text": "".join(tool_call_text_parts),
+                        "token": 0,
+                    }
                 )
+                yield response
+
             continue
+
         # fallthrough
         yield response
 
 
-def patch_kimi_tokenizer(tokenizer: TokenizerWrapper):
-    """
-    Version of to-be-upstreamed kimi-k2 tool parser
-    """
-    import ast
-    import json
-    from typing import Any
-
-    import regex as re
-
-    # kimi has a fixed function naming scheme, with a json formatted arg
-    #   functions.multiply:0 <|tool_call_argument_begin|> {"a": 2, "b": 3}
-    #   Also needs to handle tools like call_0<|tool_call_argument_begin|>{"filePath": "..."}
-    _func_name_regex = re.compile(
-        r"^\s*(.+)[:](\d+)\s*<\|tool_call_argument_begin\|>", re.DOTALL
-    )
-    _func_arg_regex = re.compile(r"<\|tool_call_argument_begin\|>\s*(.*)\s*", re.DOTALL)
-
-    # kimi has a tool_calls_section - we're leaving this up to the caller to handle
-    tool_call_start = "<|tool_call_begin|>"
-    tool_call_end = "<|tool_call_end|>"
-
-    def _deserialize(value: str) -> Any:  # pyright: ignore[reportAny]
-        try:
-            return json.loads(value)  # pyright: ignore[reportAny]
-        except Exception:
-            pass
-
-        try:
-            return ast.literal_eval(value)  # pyright: ignore[reportAny]
-        except Exception:
-            pass
-        return value
-
-    def parse_tool_call(text: str, tools: Any | None = None):
-        func_name_match = _func_name_regex.search(text)
-        if func_name_match is None:
-            raise ValueError(f"Could not parse function name from tool call: {text!r}")
-        original_func_name = func_name_match.group(1)
-        tool_id = func_name_match.group(2)
-        # strip off the `functions.` prefix, if it exists.
-        func_name = original_func_name[original_func_name.find(".") + 1 :]
-
-        func_args_match = _func_arg_regex.search(text)
-        if func_args_match is None:
-            raise ValueError(f"Could not parse function args from tool call: {text!r}")
-        func_args = func_args_match.group(1)
-        # the args should be valid json - no need to check against our tools to deserialize
-        arg_dct = _deserialize(func_args)  # pyright: ignore[reportAny]
-
-        return dict(
-            id=f"{original_func_name}:{tool_id}",
-            name=func_name,
-            arguments=arg_dct,  # pyright: ignore[reportAny]
-        )
-
-    tokenizer._tool_call_start = tool_call_start
-    tokenizer._tool_call_end = tool_call_end
-    tokenizer._tool_parser = parse_tool_call
-
-
-def patch_glm_tokenizer(tokenizer: TokenizerWrapper):
-    """
-    Fixed version of mlx_lm's glm47 tool parser that handles regex match failures.
-    """
-    import ast
-    import json
-    from typing import Any
-
-    import regex as re
-
-    _func_name_regex = re.compile(r"^(.*?)<arg_key>", re.DOTALL)
-    _func_arg_regex = re.compile(
-        r"<arg_key>(.*?)</arg_key>(?:\n|\s)*<arg_value>(.*?)(?:</arg_value>|(?=<arg_key>)|$)",
-        re.DOTALL,
-    )
-
-    tool_call_start = "<tool_call>"
-    tool_call_end = "</tool_call>"
-
-    def _is_string_type(
-        tool_name: str,
-        arg_name: str,
-        tools: list[Any] | None,
-    ) -> bool:
-        if tools is None:
-            return False
-        for tool in tools:  # pyright: ignore[reportAny]
-            func = tool["function"]  # pyright: ignore[reportAny]
-            if func["name"] == tool_name:
-                params = func["parameters"]  # pyright: ignore[reportAny]
-                if params is None:
-                    return False
-                props = params.get("properties", {})  # pyright: ignore[reportAny]
-                arg_props = props.get(arg_name, {})  # pyright: ignore[reportAny]
-                arg_type = arg_props.get("type", None)  # pyright: ignore[reportAny]
-                return arg_type == "string"  # pyright: ignore[reportAny]
-        return False
-
-    def _deserialize(value: str) -> Any:  # pyright: ignore[reportAny]
-        try:
-            return json.loads(value)  # pyright: ignore[reportAny]
-        except Exception:
-            pass
-        try:
-            return ast.literal_eval(value)  # pyright: ignore[reportAny]
-        except Exception:
-            pass
-        return value
-
-    def parse_tool_call(text: str, tools: list[Any] | None = None):
-        func_name_match = _func_name_regex.search(text)
-        if func_name_match is None:
-            raise ValueError(f"Could not parse function name from tool call: {text!r}")
-        func_name = func_name_match.group(1)
-
-        pairs = _func_arg_regex.findall(text)
-        arg_dct: dict[str, Any] = {}
-        for key, value in pairs:  # pyright: ignore[reportAny]
-            arg_key = key.strip()  # pyright: ignore[reportAny]
-            arg_val = value.strip()  # pyright: ignore[reportAny]
-            if not _is_string_type(func_name, arg_key, tools):  # pyright: ignore[reportAny]
-                arg_val = _deserialize(arg_val)  # pyright: ignore[reportAny]
-            arg_dct[arg_key] = arg_val
-        return dict(name=func_name, arguments=arg_dct)
-
-    tokenizer._tool_call_start = tool_call_start
-    tokenizer._tool_call_end = tool_call_end
-    tokenizer._tool_parser = parse_tool_call
-
-
-def _validate_single_tool(obj: dict[str, Any]) -> ToolCallItem:
-    if (
-        ((name := obj.get("name")) is not None)
-        and ((args := obj.get("arguments")) is not None)
-        and isinstance(name, str)
-    ):
-        raw_id: object = obj.get("id")
-        extra = {"id": str(raw_id)} if raw_id is not None else {}
-        return ToolCallItem(
-            **extra,
-            name=name,
-            arguments=json.dumps(args),
-        )
-    else:
-        raise ValidationError
-
-
 EXO_RUNNER_MUST_FAIL = "EXO RUNNER MUST FAIL"
 EXO_RUNNER_MUST_OOM = "EXO RUNNER MUST OOM"
 EXO_RUNNER_MUST_TIMEOUT = "EXO RUNNER MUST TIMEOUT"

src/exo/worker/runner/tool_parsers.py

@@ -0,0 +1,72 @@
+import json
+from dataclasses import dataclass
+from typing import Any, Callable
+
+from exo.shared.types.api import ToolCallItem
+
+
+@dataclass
+class ToolParser:
+    start_parsing: str
+    end_parsing: str
+    parse_tool_calls: Callable[[str], list[ToolCallItem] | None]
+
+
+def make_mlx_parser(
+    tool_call_start: str,
+    tool_call_end: str,
+    tool_parser: Callable[[str], dict[str, Any] | list[dict[str, Any]]],
+) -> ToolParser:
+    def parse_tool_calls(text: str) -> list[ToolCallItem] | None:
+        try:
+            text = text.removeprefix(tool_call_start)
+            text = text.removesuffix(tool_call_end)
+            parsed = tool_parser(text)
+            if isinstance(parsed, list):
+                return [ToolCallItem.model_validate(_flatten(p)) for p in parsed]
+            else:
+                return [ToolCallItem.model_validate(_flatten(parsed))]
+
+        except Exception:
+            return None
+
+    return ToolParser(
+        start_parsing=tool_call_start,
+        end_parsing=tool_call_end,
+        parse_tool_calls=parse_tool_calls,
+    )
+
+
+# TODO / example code:
+def _parse_json_calls(text: str) -> list[ToolCallItem] | None:
+    try:
+        text = text.removeprefix("<tool_call>")
+        text = text.removesuffix("</tool_call>")
+        top_level = {
+            k: json.dumps(v) if isinstance(v, (dict, list)) else v
+            for k, v in json.loads(text).items()  # pyright: ignore[reportAny]
+        }
+        return [ToolCallItem.model_validate(top_level)]
+    except Exception:
+        return None
+
+
+def _flatten(p: dict[str, Any]) -> dict[str, str]:
+    return {
+        k: json.dumps(v) if isinstance(v, (dict, list)) else str(v)  # pyright: ignore[reportAny]
+        for k, v in p.items()  # pyright: ignore[reportAny]
+    }
+
+
+json_tool_parser = ToolParser(
+    start_parsing="<tool_call>",
+    end_parsing="</tool_call>",
+    parse_tool_calls=_parse_json_calls,
+)
+
+
+def infer_tool_parser(chat_template: str) -> ToolParser | None:
+    """Attempt to auto-infer a tool parser from the chat template."""
+    if "<tool_call>" in chat_template and "tool_call.name" in chat_template:
+        return json_tool_parser
+    return None

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

@@ -24,6 +24,7 @@ from exo.worker.engines.mlx.utils_mlx import (
 
 # Files needed for tokenizer functionality
 TOKENIZER_FILE_PATTERNS = [
+    "config.json",
     "tokenizer.json",
     "tokenizer_config.json",
     "special_tokens_map.json",
@@ -338,6 +339,9 @@ async def test_kimi_tokenizer_specifically():
     # Verify EOS token is set
     assert eos_token_ids == [163586], "Kimi EOS token should be [163586]"
 
+    # Verify architecture-based detection gives same result
+    assert get_eos_token_ids_for_model(model_id, model_type="kimi") == [163586]
+
 
 # Test GLM tokenizer since it also has special handling
 @pytest.mark.asyncio
@@ -378,3 +382,10 @@ async def test_glm_tokenizer_specifically():
         151329,
         151338,
     ], "GLM EOS tokens should be correct"
+
+    # Verify architecture-based detection gives same result
+    assert get_eos_token_ids_for_model(model_id, model_type="glm4") == [
+        151336,
+        151329,
+        151338,
+    ]

src/exo/worker/tests/unittests/test_runner/test_parse_tool_calls.py

@@ -5,12 +5,13 @@ from typing import Any
 
 from exo.shared.types.worker.runner_response import GenerationResponse, ToolCallResponse
 from exo.worker.runner.runner import parse_tool_calls
+from exo.worker.runner.tool_parsers import make_mlx_parser
 
 
 def _make_responses(
     texts: list[str],
     finish_on_last: bool = True,
-) -> Generator[GenerationResponse | ToolCallResponse]:
+) -> Generator[GenerationResponse]:
     """Create a sequence of GenerationResponses from text strings."""
     for i, text in enumerate(texts):
         is_last = i == len(texts) - 1
@@ -22,10 +23,13 @@ def _make_responses(
         )
 
 
-def _dummy_parser(text: str) -> dict[str, Any]:
+def _dummier_parser(text: str) -> dict[str, Any]:
     return {"name": "test_fn", "arguments": {"arg": text}}
 
 
+_dummy_parser = make_mlx_parser("<tool_call>", "</tool_call>", _dummier_parser)
+
+
 class TestParseToolCalls:
     """Tests for parse_tool_calls generator."""
 
@@ -35,8 +39,6 @@ class TestParseToolCalls:
         results = list(
             parse_tool_calls(
                 _make_responses(texts, finish_on_last=False),
-                "<tool_call>",
-                "</tool_call>",
                 _dummy_parser,
             )
         )
@@ -50,8 +52,6 @@ class TestParseToolCalls:
         results = list(
             parse_tool_calls(
                 _make_responses(texts),
-                "<tool_call>",
-                "</tool_call>",
                 _dummy_parser,
             )
         )
@@ -76,9 +76,7 @@ class TestParseToolCalls:
         results = list(
             parse_tool_calls(
                 _make_responses(texts, finish_on_last=False),
-                "<tool_call>",
-                "</tool_call>",
-                _failing_parser,
+                make_mlx_parser("<tool_call>", "</tool_call>", _failing_parser),
             )
         )