Note: This document was originally created for the Therapeutic Text Adventure (TTA) game project. The model evaluation methodology and selection criteria remain valuable for general AI application development. For the historical TTA game context, see archive/legacy-tta-game.
This document outlines a comprehensive model selection strategy for AI applications. It details how models can be dynamically selected based on task requirements, performance metrics, and resource constraints to optimize both quality and efficiency.
Based on our comprehensive testing, we’ve identified the strengths and weaknesses of different models:
Strengths:
Weaknesses:
Performance Metrics:
Strengths:
Weaknesses:
Performance Metrics:
Strengths:
Weaknesses:
Performance Metrics:
Based on these evaluations, we’ll implement a task-based model selection strategy:
def select_model_for_task(
task_type: str,
structured_output: bool = False,
streaming: bool = False,
response_time_priority: bool = False
) -> str:
"""
Select the most appropriate model for a given task.
Args:
task_type: Type of task (narrative_generation, tool_selection, etc.)
structured_output: Whether structured output (like JSON) is required
streaming: Whether streaming is required
response_time_priority: Whether response time is a priority
Returns:
Name of the selected model
"""
# Fast response is highest priority
if response_time_priority:
return "qwen2.5-0.5b"
# Structured output (JSON) is required
if structured_output:
return "gemma-3-1b-it"
# Task-specific selection
if task_type == "tool_selection":
return "phi-4-mini-instruct"
elif task_type in ["knowledge_reasoning", "creative_writing"]:
return "phi-4-mini-instruct"
elif task_type == "narrative_generation" and streaming:
return "gemma-3-1b-it" # Supports streaming
elif task_type == "simple_question":
return "qwen2.5-0.5b" # Fastest for simple tasks
# Default to phi-4-mini-instruct for best quality
return "phi-4-mini-instruct"
Beyond static task-based selection, we’ll implement dynamic model selection based on runtime factors:
We’ll track performance metrics for each model:
class ModelPerformanceTracker:
"""Track performance metrics for models."""
def __init__(self):
"""Initialize the tracker."""
self.metrics = {}
def record_generation(
self,
model_name: str,
task_type: str,
tokens_generated: int,
generation_time: float,
success: bool
):
"""Record a generation event."""
# Implementation details...
def get_average_speed(self, model_name: str, task_type: str) -> float:
"""Get the average generation speed for a model and task."""
# Implementation details...
def get_success_rate(self, model_name: str, task_type: str) -> float:
"""Get the success rate for a model and task."""
# Implementation details...
def get_recommended_model(self, task_type: str, **kwargs) -> str:
"""Get the recommended model for a task based on metrics."""
# Implementation details...
We’ll consider available resources when selecting models:
def select_model_based_on_resources(
available_memory: int,
available_compute: float,
task_type: str,
**kwargs
) -> str:
"""
Select a model based on available resources.
Args:
available_memory: Available memory in MB
available_compute: Available compute (relative units)
task_type: Type of task
**kwargs: Additional parameters
Returns:
Name of the selected model
"""
# Implementation details...
We’ll adapt model selection based on user feedback and system performance:
class AdaptiveModelSelector:
"""Adaptively select models based on feedback and performance."""
def __init__(self, performance_tracker: ModelPerformanceTracker):
"""Initialize the selector."""
self.performance_tracker = performance_tracker
self.user_feedback = {}
def record_user_feedback(
self,
model_name: str,
task_type: str,
rating: float
):
"""Record user feedback for a generation."""
# Implementation details...
def select_model(
self,
task_type: str,
context: Dict[str, Any]
) -> str:
"""Select a model based on feedback and performance."""
# Implementation details...
We’ll manage model configurations in a structured format:
MODEL_CONFIGS = {
"phi-4-mini-instruct": {
"model_id": "microsoft/phi-4-mini-instruct",
"tokenizer_id": "microsoft/phi-4-mini-instruct",
"model_type": "causal_lm",
"quantization": "4bit",
"max_tokens": 512,
"temperature": 0.7,
"supports_streaming": False,
"memory_requirement": 4000, # MB
"task_mapping": {
"narrative_generation": {
"suitable": True,
"quality_score": 0.9,
"speed_score": 0.5
},
"tool_selection": {
"suitable": True,
"quality_score": 0.95,
"speed_score": 0.5
},
"knowledge_reasoning": {
"suitable": True,
"quality_score": 0.9,
"speed_score": 0.5
},
"structured_output": {
"suitable": False,
"quality_score": 0.3,
"speed_score": 0.5
}
}
},
"gemma-3-1b-it": {
"model_id": "google/gemma-3-1b-it",
"tokenizer_id": "google/gemma-3-1b-it",
"model_type": "causal_lm",
"quantization": "4bit",
"max_tokens": 1024,
"temperature": 0.7,
"supports_streaming": True,
"memory_requirement": 2000, # MB
"task_mapping": {
"narrative_generation": {
"suitable": True,
"quality_score": 0.8,
"speed_score": 0.4
},
"tool_selection": {
"suitable": False,
"quality_score": 0.2,
"speed_score": 0.4
},
"knowledge_reasoning": {
"suitable": True,
"quality_score": 0.7,
"speed_score": 0.4
},
"structured_output": {
"suitable": True,
"quality_score": 0.9,
"speed_score": 0.4
}
}
},
"qwen2.5-0.5b": {
"model_id": "Qwen/Qwen2.5-0.5B-Chat",
"tokenizer_id": "Qwen/Qwen2.5-0.5B-Chat",
"model_type": "causal_lm",
"quantization": "4bit",
"max_tokens": 512,
"temperature": 0.7,
"supports_streaming": True,
"memory_requirement": 1000, # MB
"task_mapping": {
"narrative_generation": {
"suitable": True,
"quality_score": 0.6,
"speed_score": 0.9
},
"tool_selection": {
"suitable": False,
"quality_score": 0.2,
"speed_score": 0.9
},
"knowledge_reasoning": {
"suitable": True,
"quality_score": 0.5,
"speed_score": 0.9
},
"structured_output": {
"suitable": False,
"quality_score": 0.3,
"speed_score": 0.9
}
}
}
}
We’ll implement fallback mechanisms for when the preferred model is unavailable or fails:
class ModelSelectionWithFallback:
"""Select models with fallback mechanisms."""
def __init__(self, model_configs: Dict[str, Any]):
"""Initialize the selector."""
self.model_configs = model_configs
self.fallback_chains = {
"phi-4-mini-instruct": ["gemma-3-1b-it", "qwen2.5-0.5b"],
"gemma-3-1b-it": ["phi-4-mini-instruct", "qwen2.5-0.5b"],
"qwen2.5-0.5b": ["gemma-3-1b-it", "phi-4-mini-instruct"]
}
def select_with_fallback(
self,
task_type: str,
preferred_model: str,
**kwargs
) -> str:
"""
Select a model with fallback options.
Args:
task_type: Type of task
preferred_model: Preferred model name
**kwargs: Additional parameters
Returns:
Name of the selected model
"""
# Check if preferred model is suitable
if self._is_model_suitable(preferred_model, task_type, **kwargs):
return preferred_model
# Try fallbacks
for fallback in self.fallback_chains.get(preferred_model, []):
if self._is_model_suitable(fallback, task_type, **kwargs):
return fallback
# Return the most general model as last resort
return "qwen2.5-0.5b" # Fastest and most reliable
def _is_model_suitable(
self,
model_name: str,
task_type: str,
**kwargs
) -> bool:
"""Check if a model is suitable for a task."""
# Implementation details...
We’ll implement task-specific optimizations for each model:
def optimize_for_narrative_generation(model_name: str) -> Dict[str, Any]:
"""
Get optimized parameters for narrative generation.
Args:
model_name: Name of the model
Returns:
Dictionary of optimized parameters
"""
if model_name == "phi-4-mini-instruct":
return {
"temperature": 0.8,
"top_p": 0.9,
"max_tokens": 512
}
elif model_name == "gemma-3-1b-it":
return {
"temperature": 0.7,
"top_p": 0.9,
"max_tokens": 1024
}
elif model_name == "qwen2.5-0.5b":
return {
"temperature": 0.9, # Higher temperature for creativity
"top_p": 0.95,
"max_tokens": 256 # Limit tokens for speed
}
else:
return {} # Default parameters
def optimize_for_structured_output(model_name: str) -> Dict[str, Any]:
"""
Get optimized parameters for structured output.
Args:
model_name: Name of the model
Returns:
Dictionary of optimized parameters
"""
if model_name == "phi-4-mini-instruct":
return {
"temperature": 0.2, # Lower temperature for consistency
"top_p": 0.8,
"max_tokens": 512
}
elif model_name == "gemma-3-1b-it":
return {
"temperature": 0.1, # Lowest temperature for JSON
"top_p": 0.8,
"max_tokens": 1024
}
elif model_name == "qwen2.5-0.5b":
return {
"temperature": 0.3,
"top_p": 0.8,
"max_tokens": 256
}
else:
return {} # Default parameters
The model selection strategy for the TTA project will leverage the strengths of each model while mitigating their weaknesses:
By implementing dynamic selection based on task requirements, performance metrics, and resource constraints, we can optimize both quality and efficiency across the system.
This approach will enable the TTA project to provide high-quality therapeutic content while maintaining good performance and resource efficiency.