Using Anthropic for batch inference

You can use Gemini for batch inference in Curator to generate synthetic data. In this example, we will generate reannotation of wildchat dataset, but the approach can be adapted for any data generation task.

Prerequisites

• Python 3.10+

• Curator: Install via pip install bespokelabs-curator

• Anthropic: Anthropic API key

Steps

1. Setup environment vars

export ANTHROPIC_API_KEY=<your_api_key>

2. Create a curator.LLM subclass

Create a class that inherits from curator.LLM. Implement two key methods:

• prompt(): Generates the prompt for the LLM.

• parse(): Processes the LLM's response into your desired format.

Here’s the implementation:

"""Example of reannotating the WildChat dataset using curator."""

import logging
from bespokelabs import curator

# To see more detail about how batches are being processed
logger = logging.getLogger("bespokelabs.curator")
logger.setLevel(logging.INFO)


class WildChatReannotator(curator.LLM):
    """A reannotator for the WildChat dataset."""

    def prompt(self, input: dict) -> str:
        """Extract the first message from a conversation to use as the prompt."""
        return input["conversation"][0]["content"]

    def parse(self, input: dict, response: str) -> dict:
        """Parse the model response along with the input to the model into the desired output format.."""
        instruction = input["conversation"][0]["content"]
        return {"instruction": instruction, "new_response": response}

3. Configure the Anthropic model

distiller = WildChatReannotator(model_name="claude-3-5-haiku-20241022", 
                                batch=True
                                )

4. Generate Data

Generate the structured data and output the results as a pandas DataFrame:

from datasets import load_dataset
dataset = load_dataset("allenai/WildChat", split="train")
dataset = dataset.select(range(100))

distilled_dataset = distiller(dataset)
print(distilled_dataset)
print(distilled_dataset[0])

Example Output

Using the above example, the output might look like this:

instruction	new_response
Write a very long, elaborate, descriptive and ...	Scene: Omelette Apocalypse\n\n**INT. DINER...
what are you?	I am a large language model, trained by Anthropic

Batch Configuration

• Check out complete batch configuration