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>

src/exo/master/adapters/responses.py

@@ -26,7 +26,6 @@ from exo.shared.types.openai_responses import (
     ResponseOutputText,
     ResponsesRequest,
     ResponsesResponse,
-    ResponsesStreamEvent,
     ResponseTextDeltaEvent,
     ResponseTextDoneEvent,
     ResponseUsage,
@@ -34,11 +33,6 @@ from exo.shared.types.openai_responses import (
 from exo.shared.types.text_generation import InputMessage, TextGenerationTaskParams
 
 
-def _format_sse(event: ResponsesStreamEvent) -> str:
-    """Format a streaming event as an SSE message."""
-    return f"event: {event.type}\ndata: {event.model_dump_json()}\n\n"
-
-
 def _extract_content(content: str | list[ResponseContentPart]) -> str:
     """Extract plain text from a content field that may be a string or list of parts."""
     if isinstance(content, str):
@@ -213,13 +207,13 @@ async def generate_responses_stream(
     created_event = ResponseCreatedEvent(
         sequence_number=next(seq), response=initial_response
     )
-    yield _format_sse(created_event)
+    yield f"event: response.created\ndata: {created_event.model_dump_json()}\n\n"
 
     # response.in_progress
     in_progress_event = ResponseInProgressEvent(
         sequence_number=next(seq), response=initial_response
     )
-    yield _format_sse(in_progress_event)
+    yield f"event: response.in_progress\ndata: {in_progress_event.model_dump_json()}\n\n"
 
     # response.output_item.added
     initial_item = ResponseMessageItem(
@@ -230,7 +224,7 @@ async def generate_responses_stream(
     item_added = ResponseOutputItemAddedEvent(
         sequence_number=next(seq), output_index=0, item=initial_item
     )
-    yield _format_sse(item_added)
+    yield f"event: response.output_item.added\ndata: {item_added.model_dump_json()}\n\n"
 
     # response.content_part.added
     initial_part = ResponseOutputText(text="")
@@ -241,7 +235,7 @@ async def generate_responses_stream(
         content_index=0,
         part=initial_part,
     )
-    yield _format_sse(part_added)
+    yield f"event: response.content_part.added\ndata: {part_added.model_dump_json()}\n\n"
 
     accumulated_text = ""
     function_call_items: list[ResponseFunctionCallItem] = []
@@ -272,7 +266,7 @@ async def generate_responses_stream(
                     output_index=next_output_index,
                     item=fc_item,
                 )
-                yield _format_sse(fc_added)
+                yield f"event: response.output_item.added\ndata: {fc_added.model_dump_json()}\n\n"
 
                 # response.function_call_arguments.delta
                 args_delta = ResponseFunctionCallArgumentsDeltaEvent(
@@ -281,7 +275,7 @@ async def generate_responses_stream(
                     output_index=next_output_index,
                     delta=tool.arguments,
                 )
-                yield _format_sse(args_delta)
+                yield f"event: response.function_call_arguments.delta\ndata: {args_delta.model_dump_json()}\n\n"
 
                 # response.function_call_arguments.done
                 args_done = ResponseFunctionCallArgumentsDoneEvent(
@@ -291,7 +285,7 @@ async def generate_responses_stream(
                     name=tool.name,
                     arguments=tool.arguments,
                 )
-                yield _format_sse(args_done)
+                yield f"event: response.function_call_arguments.done\ndata: {args_done.model_dump_json()}\n\n"
 
                 # response.output_item.done
                 fc_done_item = ResponseFunctionCallItem(
@@ -306,7 +300,7 @@ async def generate_responses_stream(
                     output_index=next_output_index,
                     item=fc_done_item,
                 )
-                yield _format_sse(fc_item_done)
+                yield f"event: response.output_item.done\ndata: {fc_item_done.model_dump_json()}\n\n"
 
                 function_call_items.append(fc_done_item)
                 next_output_index += 1
@@ -322,7 +316,7 @@ async def generate_responses_stream(
             content_index=0,
             delta=chunk.text,
         )
-        yield _format_sse(delta_event)
+        yield f"event: response.output_text.delta\ndata: {delta_event.model_dump_json()}\n\n"
 
     # response.output_text.done
     text_done = ResponseTextDoneEvent(
@@ -332,7 +326,7 @@ async def generate_responses_stream(
         content_index=0,
         text=accumulated_text,
     )
-    yield _format_sse(text_done)
+    yield f"event: response.output_text.done\ndata: {text_done.model_dump_json()}\n\n"
 
     # response.content_part.done
     final_part = ResponseOutputText(text=accumulated_text)
@@ -343,7 +337,7 @@ async def generate_responses_stream(
         content_index=0,
         part=final_part,
     )
-    yield _format_sse(part_done)
+    yield f"event: response.content_part.done\ndata: {part_done.model_dump_json()}\n\n"
 
     # response.output_item.done
     final_message_item = ResponseMessageItem(
@@ -354,7 +348,7 @@ async def generate_responses_stream(
     item_done = ResponseOutputItemDoneEvent(
         sequence_number=next(seq), output_index=0, item=final_message_item
     )
-    yield _format_sse(item_done)
+    yield f"event: response.output_item.done\ndata: {item_done.model_dump_json()}\n\n"
 
     # Create usage from usage data if available
     usage = None
@@ -379,4 +373,4 @@ async def generate_responses_stream(
     completed_event = ResponseCompletedEvent(
         sequence_number=next(seq), response=final_response
     )
-    yield _format_sse(completed_event)
+    yield f"event: response.completed\ndata: {completed_event.model_dump_json()}\n\n"

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/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
 
@@ -367,7 +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:

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,
+    ]