class AnthropicModel(BaseModel):
model: str = "claude-2.1"
"""The model name to use."""
temperature: float = 0.0
"""What sampling temperature to use."""
max_tokens: int = 256
"""The maximum number of tokens to generate in the completion."""
top_p: float = 1
"""Total probability mass of tokens to consider at each step."""
top_k: int = 256
"""The cutoff where the model no longer selects the words."""
stop_sequences: List[str] = field(default_factory=list)
"""If the model encounters a stop sequence, it stops generating further tokens."""
extra_parameters: Dict[str, Any] = field(default_factory=dict)
"""Any extra parameters to add to the request body (e.g., countPenalty for a21 models)"""
max_content_size: Optional[int] = None
"""If you're using a fine-tuned model, set this to the maximum content size"""
Usage
In this section, we will showcase the methods and properties that our EvalModels have. First, instantiate your model from the. Once you've instantiated your model, you can get responses from the LLM by simply calling the model and passing a text string.
model = #Instantiate your Anthropic model here
model("Hello there, how are you?")
# Output: "As an artificial intelligence, I don't have feelings,
# but I'm here and ready to assist you. How can I help you today?"
