Model: Auto detect model backend from config

* Use exllamav3 for exl3 models, exllamav2 otherwise

backends/exllamav3/model.py

@@ -161,9 +161,7 @@ class ExllamaV3Container(BaseModelContainer):
             self.draft_model_dir = draft_model_path
             self.draft_config = Config.from_directory(str(draft_model_path.resolve()))
             self.draft_model = Model.from_config(self.draft_config)
-            logger.info(
+            logger.info(f"Using draft model: {str(draft_model_path.resolve())}")
-                f'Using draft model: {str(draft_model_path.resolve())}'
-            )
         else:
             self.draft_model = None
             self.craft_cache = None
@@ -223,7 +221,7 @@ class ExllamaV3Container(BaseModelContainer):
 
         # Draft cache
         if self.use_draft_model:
-            self.draft_cache = Cache(self.draft_model, max_num_tokens = self.cache_size)
+            self.draft_cache = Cache(self.draft_model, max_num_tokens=self.cache_size)
 
         # Max batch size
         self.max_batch_size = unwrap(kwargs.get("max_batch_size"), 256)

common/config_models.py

@@ -166,7 +166,7 @@ class ModelConfig(BaseConfigModel):
     backend: Optional[str] = Field(
         None,
         description=(
-            "Backend to use for this model (default: exllamav2)\n"
+            "Backend to use for this model (auto-detect if not specified)\n"
             "Options: exllamav2, exllamav3"
         ),
     )

common/model.py

@@ -17,6 +17,7 @@ from common.logger import get_loading_progress_bar
 from common.networking import handle_request_error
 from common.tabby_config import config
 from common.optional_dependencies import dependencies
+from common.transformers_utils import HuggingFaceConfig
 from common.utils import unwrap
 
 # Global variables for model container
@@ -123,8 +124,24 @@ async def load_model_gen(model_path: pathlib.Path, **kwargs):
     kwargs = {**config.model_defaults, **kwargs}
     kwargs = await apply_inline_overrides(model_path, **kwargs)
 
+    # Read config.json and detect the quant method
+    hf_config_path = model_path / "config.json"
+    if hf_config_path.exists():
+        try:
+            hf_config = await HuggingFaceConfig.from_file(model_path)
+        except Exception as exc:
+            raise ValueError(
+                "Failed to read the model's config.json. "
+                f"Please check your model directory at {model_path}."
+            ) from exc
+    quant_method = hf_config.quant_method()
+    if quant_method == "exl3":
+        backend_name = "exllamav3"
+    else:
+        backend_name = "exllamav2"
+
     # Create a new container and check if the right dependencies are installed
-    backend_name = unwrap(kwargs.get("backend"), "exllamav2").lower()
+    backend_name = unwrap(kwargs.get("backend"), backend_name).lower()
     container_class = _BACKEND_REGISTRY.get(backend_name)
 
     if not container_class:

common/transformers_utils.py

@@ -1,7 +1,7 @@
 import aiofiles
 import json
 import pathlib
-from typing import List, Optional, Union
+from typing import Dict, List, Optional, Union
 from pydantic import BaseModel
 
 
@@ -42,6 +42,8 @@ class HuggingFaceConfig(BaseModel):
     Will be expanded as needed.
     """
 
+    quantization_config: Optional[Dict] = None
+
     @classmethod
     async def from_file(cls, model_directory: pathlib.Path):
         """Create an instance from a generation config file."""
@@ -54,6 +56,14 @@ class HuggingFaceConfig(BaseModel):
             hf_config_dict = json.loads(contents)
             return cls.model_validate(hf_config_dict)
 
+    def quant_method(self):
+        """Wrapper method to fetch quant type"""
+
+        if isinstance(self.quantization_config, Dict):
+            return self.quantization_config.get("quant_method")
+        else:
+            return None
+
 
 class TokenizerConfig(BaseModel):
     """

config_sample.yml

@@ -74,9 +74,9 @@ model:
   # Example: ['max_seq_len', 'cache_mode'].
   use_as_default: []
 
-  # Backend to use for the model (default: exllamav2)
+  # Backend to use for this model (auto-detect if not specified)
   # Options: exllamav2, exllamav3
-  backend: exllamav2
+  backend:
 
   # Max sequence length (default: Empty).
   # Fetched from the model's base sequence length in config.json by default.

endpoints/OAI/types/chat_completion.py

@@ -84,7 +84,7 @@ class ChatCompletionRequest(CommonCompletionRequest):
 
     # Chat completions requests do not have a BOS token preference. Backend
     # respects the tokenization config for the individual model.
-    add_bos_token: Optional[bool] = Field(default = None)
+    add_bos_token: Optional[bool] = Field(default=None)
 
     @field_validator("add_bos_token", mode="after")
     def force_bos_token(cls, v):