added the inference fetching from model mappers

studio/backend/models/inference.py

@@ -43,6 +43,7 @@ class LoadResponse(BaseModel):
     display_name: str = Field(..., description="Display name of the model")
     is_vision: bool = Field(False, description="Whether model is a vision model")
     is_lora: bool = Field(False, description="Whether model is a LoRA adapter")
+    inference: dict = Field(..., description="Inference parameters (temperature, top_p, top_k, min_p)")
 
 
 class UnloadResponse(BaseModel):

studio/backend/routes/inference.py

@@ -22,12 +22,14 @@ if str(backend_path) not in sys.path:
 try:
     from core.inference import get_inference_backend
     from utils.models import ModelConfig
+    from utils.inference import load_inference_config
 except ImportError:
     parent_backend = backend_path.parent / "backend"
     if str(parent_backend) not in sys.path:
         sys.path.insert(0, str(parent_backend))
     from core.inference import get_inference_backend
     from utils.models import ModelConfig
+    from utils.inference import load_inference_config
 
 from models.inference import (
     LoadRequest,
@@ -64,6 +66,8 @@ async def load_model(request: LoadRequest):
     Load a model for inference.
     
     The model_path should be a clean identifier from GET /models/list.
+    Returns inference configuration parameters (temperature, top_p, top_k, min_p)
+    from the model's YAML config, falling back to default.yaml for missing values.
     """
     try:
         backend = get_inference_backend()
@@ -97,12 +101,16 @@ async def load_model(request: LoadRequest):
         
         logger.info(f"Loaded model: {config.identifier}")
         
+        # Load inference configuration parameters
+        inference_config = load_inference_config(config.identifier)
+        
         return LoadResponse(
             status="loaded",
             model=config.identifier,
             display_name=config.display_name,
             is_vision=config.is_vision,
             is_lora=config.is_lora,
+            inference=inference_config,
         )
         
     except HTTPException:

studio/backend/utils/inference/__init__.py

@@ -0,0 +1,7 @@
+"""
+Inference utility functions
+"""
+from utils.inference.inference_config import load_inference_config
+
+__all__ = ["load_inference_config"]
+

studio/backend/utils/inference/inference_config.py

@@ -0,0 +1,65 @@
+"""
+Inference configuration loading utilities.
+
+This module provides functions to load inference parameters (temperature, top_p, top_k, min_p)
+from model YAML configuration files, with fallback to default.yaml.
+"""
+from pathlib import Path
+from typing import Dict, Any
+import yaml
+import logging
+
+from utils.models.model_config import load_model_defaults
+
+logger = logging.getLogger(__name__)
+
+
+def load_inference_config(model_identifier: str) -> Dict[str, Any]:
+    """
+    Load inference configuration parameters for a model.
+    
+    This function loads inference parameters (temperature, top_p, top_k, min_p) from the
+    model's YAML configuration file using the same mapping logic as the /config endpoint.
+    If a parameter is missing from the model's config, it falls back to the value in
+    default.yaml.
+    
+    Args:
+        model_identifier: Model identifier (e.g., "unsloth/llama-3-8b-bnb-4bit")
+    
+    Returns:
+        Dictionary containing inference parameters:
+        {
+            "temperature": float,
+            "top_p": float,
+            "top_k": int,
+            "min_p": float
+        }
+    """
+    # Load model defaults to get inference parameters
+    model_defaults = load_model_defaults(model_identifier)
+    
+    # Load default.yaml for fallback values
+    script_dir = Path(__file__).parent.parent.parent
+    defaults_dir = script_dir / "assets" / "configs" / "model_defaults"
+    default_config_path = defaults_dir / "default.yaml"
+    
+    default_inference = {}
+    if default_config_path.exists():
+        try:
+            with open(default_config_path, 'r', encoding='utf-8') as f:
+                default_config = yaml.safe_load(f) or {}
+                default_inference = default_config.get("inference", {})
+        except Exception as e:
+            logger.warning(f"Failed to load default.yaml: {e}")
+    
+    # Extract inference parameters from model config, fallback to defaults
+    model_inference = model_defaults.get("inference", {})
+    inference_config = {
+        "temperature": model_inference.get("temperature", default_inference.get("temperature", 0.7)),
+        "top_p": model_inference.get("top_p", default_inference.get("top_p", 0.95)),
+        "top_k": model_inference.get("top_k", default_inference.get("top_k", -1)),
+        "min_p": model_inference.get("min_p", default_inference.get("min_p", 0.01)),
+    }
+    
+    return inference_config
+