Curate Reasoning data with Claude-3.7 Sonnet

You can use Sonnet reasoning model in Curator to generate synthetic data. In this example, we will answer some questions with reasoning traces from claude sonnet 3.7, 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 reasoning on simple questions using curator."""

import os
from datasets import load_dataset
from bespokelabs import curator

class Reasoner(curator.LLM):
    return_completions_object = True

    def prompt(self, input):
        return input["question"]

    def parse(self, input, response):
        """Parse the LLM response to extract reasoning and solution."""
        content = response["content"]
        thinking = ""
        text = ""
        for content_block in content:
            if content_block["type"] == "thinking":
                thinking = content_block["thinking"]
            elif content_block["type"] == "text":
                text = content_block["text"]
            elif content_block["type"] == "redacted_thinking":
                print("Redacted thinking block! (notifying you for fun)")

        input["claude_thinking_trajectory"] = thinking
        input["claude_attempt"] = text
        return input

3. Configure the Anthropic model

llm = Reasoner(
    model_name="claude-3-7-sonnet-20250219",
    generation_params={"max_tokens": 20000, "thinking": {"type": "enabled", "budget_tokens": 18000}},
    batch=False,
    backend="anthropic",
    backend_params={"require_all_responses": False},
)

4. Generate Data

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

ds = llm([
    {"question": "How to solve for world peace?"},
    {"question": "What is the fifteenth prime number?"},
])
print(ds.dataset)
print(ds.dataset[0])

Example Output

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

question	claude_thinking_trajectory	claude_attempt
How to solve for world peace?	This is a question about solving for world pea...	The Path to World Peace\n\nWorld peace is on...
What is the fifteenth prime number?	Let me list out the prime numbers in order to ...	The fifteenth prime number is 47.\n\nThe seque...

Api Reference

• Check out complete configuration