Information
Bespoke Curator
Bulk Inference and Scalable Data Curation for Post-Training
[](https://github.com/bespokelabsai/curator/) [](https://x.com/bespokelabsai) [](https://huggingface.co/bespokelabs) [](https://discord.gg/KqpXvpzVBS)
[](https://docs.bespokelabs.ai/bespoke-curator/getting-started) [](https://bespokelabs.ai/) [](https://pypi.org/project/bespokelabs-curator/)
[](https://docs.bespokelabs.ai/bespoke-curator/getting-started) [](https://bespokelabs.ai/) [](https://pypi.org/project/bespokelabs-curator/)
[ English | 中文 ]
## What's New
* **[2025.12.05]** [Launched OpenThoughts-Agents](https://www.open-thoughts.ai/blog/agent) whose data was curated using Curator.
* **[2025.04.09]** [Launching Reasoning Datasets Competition](https://huggingface.co/blog/bespokelabs/reasoning-datasets-competition) with HuggingFace and Together.ai. Win $5000 USD worth of prizes!
* **[2025.04.03]** We used Bespoke Curator to create [OpenThoughts2-1M](https://huggingface.co/datasets/open-thoughts/OpenThoughts2-1M) dataset, which was used to train OpenThinker2-32B that outperforms DeepSeek-R1-32B. The dataset started trending on HuggingFace.
* **[2025.03.12]** [Gemini Batch support added](https://www.bespokelabs.ai/blog/effortless-gemini-batch-processing-with-curator): Gemini batch API is extremely challenging, and we made it much simpler! :)
* **[2025.03.05]** [Claude 3.7 Sonnet Thinking and batch mode support added](https://www.bespokelabs.ai/blog/claude-3-7-sonnet-thinking-mode-in-curator).
* **[2025.02.26]** [Code Execution Support added](https://www.bespokelabs.ai/blog/launching-code-executor): You can now run code (generated by Curator) using CodeExecutor. We support four backends: local (called multiprocessing), Ray, Docker and e2b.
* **[2025.02.06]** We used Bespoke Curator to create [s1K-1.1]( https://huggingface.co/datasets/simplescaling/s1K-1.1), a high-quality sample-efficient reasoning dataset.
* **[2025.01.30]** [Batch Processing Support for OpenAI, Anthropic, and other compatible APIs](https://www.bespokelabs.ai/blog/batch-processing-with-curator): Cut Token Costs in Half . Through our partnership with kluster.ai, new users using Curator can access open-source models like DeepSeek-R1 and receive a **$25 credit** (limits apply). EDIT: Promotion has come to an end.
* **[2025.01.27]** We used Bespoke Curator to create [OpenThoughts-114k](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k), a high-quality reasoning dataset (trending on HuggingFace).
* **[2025.01.22]** We used Bespoke Curator to create [Bespoke-Stratos-17k](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k), a high-quality reasoning dataset (trending on HuggingFace).
* **[2025.01.15]** Curator launched
## Overview
Bespoke Curator makes it easy to create synthetic data pipelines. Whether you are training a model or extracting structured data, Curator will prepare high-quality data quickly and robustly.
* Rich Python based library for generating and curating synthetic data.
* Viewer to monitor data while it is being generated.
* First class support for structured outputs.
* Built-in performance optimizations for asynchronous operations, caching, and fault recovery at every scale.
* Support for a wide range of inference options via LiteLLM, vLLM, and popular batch APIs.
Check out our full documentation for [getting started](https://docs.bespokelabs.ai/bespoke-curator/getting-started), [tutorials](https://docs.bespokelabs.ai/bespoke-curator/tutorials), [guides](https://docs.bespokelabs.ai/bespoke-curator/how-to-guides) and detailed [reference](https://docs.bespokelabs.ai/bespoke-curator/api-reference/llm-api-documentation).
## ️ Installation
\`\`\`bash
pip install bespokelabs-curator
\`\`\`
## Examples
### Finetuning/Distillation
| **Task** | **Link(s)** | **Goal** |
|----------|--------------|-------------|
| **Product feature extraction** | 
| Finetuning a model to identify features of a product |
| **Sentiment analysis** | | Aspect-based sentiment analysis of restaurant reviews and finetuning using Together.ai |
| **RAFT for domain-specific RAG** | Code | Implement Retrieval Augmented Fine-Tuning (RAFT) that processes domain-specific documents, generates questions, and prepares data for fine-tuning LLMs. |
### Data Generation
| **Task** | **Link(s)** | **Goal** |
|----------|--------------|-------------|
| **Reasoning dataset generation (Bespoke Stratos)** | Code | Generate the Bespoke-Stratos-17k dataset, focusing on reasoning traces from math, coding, and problem-solving datasets. |
| **Reasoning dataset generation (Open Thoughts)** | Code | Generate the Open-Thoughts-114k dataset, focusing on reasoning traces from math, coding, and problem-solving datasets.|
| **Multimodal** | Code | Demonstrates multimodal capabilities by generating recipes from food images |
| **Ungrounded Question Answer generation** | Code | Generate diverse question-answer pairs using techniques similar to the CAMEL paper |
| **Code Execution** | | Execute code generated with Curator |
| **3Blue1Brown video generation** | Code | Generate videos similar to 3Blue1Brown and render them using code execution! |
| **Synthetic charts** | Code | Generate charts synthetically.
| **Function calling** | Code | Generate data for finetuning for function calling. |
## Quickstart
### Using \`curator.LLM\` for Bulk Inference
\`\`\`python
from typing import Dict
from bespokelabs import curator
from datasets import Dataset
from pydantic import BaseModel, Field
from typing import Literal
class Sentiment(BaseModel):
sentiment: Literal["positive", "negative", "neutral"] = Field(
description="Sentiment of the review")
class SentimentAnalyzer(curator.LLM):
def prompt(self, product: Dict):
return f"Determine the sentiment of the product from the review: \{product['review']\}"
def parse(self, product: Dict, response: Sentiment):
return [\{"name": product["name"], "sentiment": response.sentiment\}]
# You can easily have a million rows here.
# Curator takes care of parallelism, retries, and caches responses.
dataset = [\{"name": "Curator", "review": "Already saved hours in one day of use."\},
\{"name": "Bespoke MiniCheck", "review": "Hallucination rates dropped by 90%."\}]
# You can set batch=True, and instantly uses batch mode to save 50% of the costs.
analyzer = SentimentAnalyzer(
model_name="gpt-4o-mini", response_format=Sentiment, batch=False)
reviews = analyzer(dataset)
print(reviews.to_pandas())
\`\`\`
Output:
\`\`\`
name sentiment
0 Curator positive
1 Bespoke MiniCheck positive
\`\`\`
In the \`SentimentAnalyzer\` class:
* \`prompt\` takes the input (\`product\`) and returns the prompt for the LLM.
* \`parse\` takes the input (\`product\`) and the structured output (\`response\`) and converts it to a list of dictionaries. This is so that we can easily convert the output to a HuggingFace Dataset object.
Instead of a list, you can pass a HuggingFace Dataset object as well (see below for more details).
### Using \`curator.LLM\` for data generation
Here's an example of using structured outputs and chaing together two curator.LLM blocks to generate diverse poems.
\`\`\`python
from typing import Dict, List
from bespokelabs import curator
from pydantic import BaseModel, Field
class Topics(BaseModel):
topics_list: List[str] = Field(description="A list of topics.")
class TopicGenerator(curator.LLM):
response_format = Topics
def prompt(self, subject):
return f"Return 3 topics related to \{subject\}"
def parse(self, input: str, response: Topics):
return [\{"topic": t\} for t in response.topics_list]
class Poem(BaseModel):
title: str = Field(description="The title of the poem.")
poem: str = Field(description="The content of the poem.")
class Poet(curator.LLM):
response_format = Poem
def prompt(self, input: Dict) -> str:
return f"Write two poems about \{input['topic']\}."
def parse(self, input: Dict, response: Poem) -> Dict:
return [\{"title": response.title, "poem": response.poem\}]
topic_generator = TopicGenerator(model_name="gpt-4o-mini")
poet = Poet(model_name="gpt-4o-mini")
# Start generation
topics = topic_generator("Mathematics")
poems = poet(topics)
\`\`\`
Output:
\`\`\`
title poem
0 The Language of Algebra In symbols and signs, truths intertwine,..
1 The Geometry of Space In the world around us, shapes do collide,..
2 The Language of Logic In circuits and wires where silence speaks,..
\`\`\`
You can see more examples in the [examples](examples) directory.
See the [docs](https://docs.bespokelabs.ai/) for more details as well as
for troubleshooting information.
> [!TIP]
> If you are generating large datasets, you may want to use [batch mode](https://docs.bespokelabs.ai/bespoke-curator/save-usdusdusd-on-llm-inference) to save costs. Currently batch APIs from [OpenAI](https://platform.openai.com/docs/guides/batch) and [Anthropic](https://docs.anthropic.com/en/docs/build-with-claude/message-batches) are supported. With curator this is as simple as setting \`batch=True\` in the \`LLM\` class.
> [!NOTE]
> Retries and caching are enabled by default to help you rapidly iterate your data pipelines.
> So now if you run the same prompt again, you will get the same response, pretty much instantly.
> You can delete the cache at \`~/.cache/curator\` or disable it with \`export CURATOR_DISABLE_CACHE=true\`.
> [!IMPORTANT]
> Make sure to set your API keys as environment variables for the model you are calling. For example running \`export OPENAI_API_KEY=sk-...\` and \`export ANTHROPIC_API_KEY=ant-...\` will allow you to run the previous two examples. A full list of supported models and their associated environment variable names can be found [in the litellm docs](https://docs.litellm.ai/docs/providers).
### Anonymized Telemetry
We collect minimal, anonymized usage telemetry to help prioritize new features and improvements that benefit the Curator community. You can opt out by setting the \`TELEMETRY_ENABLED\` environment variable to \`False\`.
## Providers
Curator supports a wide range of providers, including OpenAI, Anthropic, and many more.
### OpenAI backend
\`\`\`python
llm = curator.LLM(
model_name="gpt-4o-mini",
)
\`\`\`
For other models that support OpenAI-compatible APIs, you can use the \`openai\` backend:
\`\`\`python
llm = curator.LLM(
model_name="gpt-4o-mini",
backend="openai",
backend_params=\{
"base_url": "https://your-openai-compatible-api-url",
"api_key":