anthropic-evals.md•1.57 kB
---
description: Configure and run Anthropic for evals
---
# Anthropic Evals
### AnthropicModel
```python
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.
<pre class="language-python"><code class="lang-python"><strong>model = #Instantiate your Anthropic model here
</strong>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?"
</code></pre>