Custom Model Evaluation

EvalScope supports the evaluation of various models through the ModelAPI abstract interface. This document will guide you on how to implement a custom model adapter by referencing MockLLM.

When Do You Need a Custom Model Adapter?

You might need to create a custom model adapter in the following scenarios:

Your model does not support the standard OpenAI API format.
You need to perform special processing on model inputs and outputs.
You require specific inference parameters or configurations.

ModelAPI Interface Definition

All models must implement the ModelAPI abstract base class:

from abc import ABC, abstractmethod
from typing import List, Optional
from evalscope.api.messages import ChatMessage
from evalscope.api.model import GenerateConfig, ModelOutput
from evalscope.api.tool import ToolChoice, ToolInfo

class ModelAPI(ABC):
    """Base interface for model API providers"""
    
    def __init__(
        self,
        model_name: str,
        base_url: Optional[str] = None,
        api_key: Optional[str] = None,
        config: GenerateConfig = GenerateConfig(),
        **kwargs
    ) -> None:
        self.model_name = model_name
        self.base_url = base_url
        self.api_key = api_key
        self.config = config

    @abstractmethod
    def generate(
        self,
        input: List[ChatMessage],
        tools: List[ToolInfo],
        tool_choice: ToolChoice,
        config: GenerateConfig,
    ) -> ModelOutput:
        """Generate model output (must be implemented)"""
        pass

MockLLM Reference Implementation

MockLLM is a simple test model implementation that demonstrates the basic structure of ModelAPI:

from typing import Any, Dict, List, Optional
from evalscope.api.messages import ChatMessage
from evalscope.api.model import GenerateConfig, ModelAPI, ModelOutput
from evalscope.api.tool import ToolChoice, ToolInfo

class MockLLM(ModelAPI):
    """Mock model implementation for testing purposes"""
    
    default_output = 'Default output from mockllm/model'

    def __init__(
        self,
        model_name: str,
        base_url: Optional[str] = None,
        api_key: Optional[str] = None,
        config: GenerateConfig = GenerateConfig(),
        custom_output: Optional[str] = None,
        **model_args: Dict[str, Any],
    ) -> None:
        super().__init__(model_name, base_url, api_key, config)
        self.model_args = model_args
        self.custom_output = custom_output

    def generate(
        self,
        input: List[ChatMessage],
        tools: List[ToolInfo],
        tool_choice: ToolChoice,
        config: GenerateConfig,
    ) -> ModelOutput:
        # Use custom output if provided, otherwise use default output
        output_text = self.custom_output if self.custom_output is not None else self.default_output
        
        return ModelOutput.from_content(
            model=self.model_name,
            content=output_text
        )

Custom Model Implementation

Create your model by referencing the structure of MockLLM:

from typing import List, Optional, Dict, Any
from evalscope.api.model import ModelAPI, GenerateConfig, ModelOutput
from evalscope.api.messages import ChatMessage
from evalscope.api.tool import ToolChoice, ToolInfo
from evalscope.api.registry import register_model_api

# 1. Register the model using register_model_api
@register_model_api(name='my_custom_model')
class MyCustomModel(ModelAPI):
    """Custom model implementation"""

    def __init__(
        self,
        model_name: str,
        base_url: Optional[str] = None,
        api_key: Optional[str] = None,
        config: GenerateConfig = GenerateConfig(),
        **model_args: Dict[str, Any],
    ) -> None:
        super().__init__(model_name, base_url, api_key, config)
        self.model_args = model_args
        print(self.model_args)
        
        # 2. Initialize your model here
        # For example: load model files, establish connections, etc.

    def generate(
        self,
        input: List[ChatMessage],
        tools: List[ToolInfo],
        tool_choice: ToolChoice,
        config: GenerateConfig,
    ) -> ModelOutput:
        # 3. Implement model inference logic

        # 3.1 Process input messages
        input_text = self._process_messages(input)
        
        # 3.2 Call your model
        response = self._call_model(input_text, config)
        
        # 3.3 Return standardized output
        return ModelOutput.from_content(
            model=self.model_name,
            content=response
        )

    def _process_messages(self, messages: List[ChatMessage]) -> str:
        """Convert chat messages to text"""
        text_parts = []
        for message in messages:
            role = getattr(message, 'role', 'user')
            content = getattr(message, 'content', str(message))
            text_parts.append(f"{role}: {content}")
        return '\n'.join(text_parts)

    def _call_model(self, input_text: str, config: GenerateConfig) -> str:
        """Invoke your model for inference"""
        # Implement your model invocation logic here
        # For example: API calls, local model inference, etc.
        return f"Response to: {input_text}"

Using the Custom Model

Direct Usage

from evalscope import run_task, TaskConfig

# Create a model instance
custom_model = MyCustomModel(
    model_name='my-model',
    model_args={'test': 'test'}
)

# Configure the evaluation task
task_config = TaskConfig(
    model=custom_model,
    datasets=['gsm8k'],
    limit=5
)

# Run the evaluation
results = run_task(task_cfg=task_config)

Usage Through Registration

from evalscope import run_task, TaskConfig
from xxx import MyCustomModel # Import the model beforehand for automatic registration

# Use the registered model
task_config = TaskConfig(
    model='my-model',
    eval_type='my_custom_model', # Name used in register_model_api
    datasets=['gsm8k'],
    model_args={'test': 'test'},
    limit=5
)

results = run_task(task_cfg=task_config)

Key Points

Inherit from ModelAPI and implement the generate method.
Return a ModelOutput object, which can be created using ModelOutput.from_content().
Handle exceptions to ensure the stability of model calls.
Register the model for use in configurations.

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