Endpoints: Add props endpoint and add more values to model params

The props endpoint is a standard used by llamacpp APIs which returns
various properties of a model to a server. It's still recommended to
use /v1/model to get all the parameters a TabbyAPI model has.

Also include the contents of a prompt template when fetching the current
model.

Signed-off-by: kingbri <8082010+bdashore3@users.noreply.github.com>

backends/exllamav2/model.py

@@ -486,16 +486,18 @@ class ExllamaV2Container:
             "rope_scale": self.config.scale_pos_emb,
             "rope_alpha": self.config.scale_alpha_value,
             "max_seq_len": self.config.max_seq_len,
+            "max_batch_size": self.max_batch_size,
             "cache_size": self.cache_size,
             "cache_mode": self.cache_mode,
             "chunk_size": self.config.max_input_len,
             "num_experts_per_token": self.config.num_experts_per_token,
-            "prompt_template": self.prompt_template.name
-            if self.prompt_template
-            else None,
             "use_vision": self.use_vision,
         }
 
+        if self.prompt_template:
+            model_params["prompt_template"] = self.prompt_template.name
+            model_params["prompt_template_content"] = self.prompt_template.raw_template
+
         if self.draft_config:
             draft_model_params = {
                 "name": self.draft_model_dir.name,
@@ -759,6 +761,10 @@ class ExllamaV2Container:
                 max_batch_size=self.max_batch_size,
                 paged=self.paged,
             )
+
+            # Update the state of the container var
+            if self.max_batch_size is None:
+                self.max_batch_size = self.generator.generator.max_batch_size
         finally:
             # This means the generator is being recreated
             # The load lock is already released in the load function

endpoints/core/router.py

@@ -23,9 +23,11 @@ from endpoints.core.types.lora import LoraList, LoraLoadRequest, LoraLoadRespons
 from endpoints.core.types.model import (
     EmbeddingModelLoadRequest,
     ModelCard,
+    ModelDefaultGenerationSettings,
     ModelList,
     ModelLoadRequest,
     ModelLoadResponse,
+    ModelPropsResponse,
 )
 from endpoints.core.types.health import HealthCheckResponse
 from endpoints.core.types.sampler_overrides import (
@@ -131,6 +133,30 @@ async def current_model() -> ModelCard:
     return get_current_model()
 
 
+@router.get(
+    "/props", dependencies=[Depends(check_api_key), Depends(check_model_container)]
+)
+async def model_props() -> ModelPropsResponse:
+    """
+    Returns specific properties of a model for clients.
+
+    To get all properties, use /v1/model instead.
+    """
+
+    current_model_card = get_current_model()
+    resp = ModelPropsResponse(
+        total_slots=current_model_card.parameters.max_batch_size,
+        default_generation_settings=ModelDefaultGenerationSettings(
+            n_ctx=current_model_card.parameters.max_seq_len,
+        ),
+    )
+
+    if current_model_card.parameters.prompt_template_content:
+        resp.chat_template = current_model_card.parameters.prompt_template_content
+
+    return resp
+
+
 @router.get("/v1/model/draft/list", dependencies=[Depends(check_api_key)])
 async def list_draft_models(request: Request) -> ModelList:
     """

endpoints/core/types/model.py

@@ -16,10 +16,12 @@ class ModelCardParameters(BaseModel):
     max_seq_len: Optional[int] = None
     rope_scale: Optional[float] = 1.0
     rope_alpha: Optional[float] = 1.0
+    max_batch_size: Optional[int] = 1
     cache_size: Optional[int] = None
     cache_mode: Optional[str] = "FP16"
     chunk_size: Optional[int] = 2048
     prompt_template: Optional[str] = None
+    prompt_template_content: Optional[str] = None
     num_experts_per_token: Optional[int] = None
     use_vision: Optional[bool] = False
 
@@ -139,3 +141,17 @@ class ModelLoadResponse(BaseModel):
     module: int
     modules: int
     status: str
+
+
+class ModelDefaultGenerationSettings(BaseModel):
+    """Contains default generation settings for model props."""
+
+    n_ctx: int
+
+
+class ModelPropsResponse(BaseModel):
+    """Represents a model props response."""
+
+    total_slots: int = 1
+    chat_template: str = ""
+    default_generation_settings: ModelDefaultGenerationSettings