Is a teacher model required for bootstrapping?

While you can use the same model, using a stronger teacher model (like GPT-4o) to generate demonstrations for a student model (like GPT-4o-mini) typically yields better results.

How many training examples do I need for this skill?

This skill is most effective when you have a small dataset of 10-50 labeled examples, making it ideal for early-stage optimization.

What are reasoning traces in DSPy?

Reasoning traces are intermediate steps or 'Chain-of-Thought' logs that the teacher model generates before providing a final answer, which helps the student model learn the logic behind a task.

Can I use this for production environments?

Yes, the skill includes instructions for saving the compiled program's state to a JSON file, which can then be loaded in production without re-running the optimization.

How does this differ from the MIPROv2 optimizer?

BootstrapFewShot is designed for small datasets and quick compute, whereas MIPROv2 is intended for larger datasets (200+ examples) and more extensive hyperparameter exploration.

DSPy Bootstrap Few-Shot Optimizer

Name: DSPy Bootstrap Few-Shot Optimizer
Author: OmidZamani

byOmidZamani

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データサイエンスとML

Automates the generation and selection of high-quality few-shot demonstrations for DSPy programs using teacher models.

The dspy-bootstrap-fewshot skill empowers developers to optimize their DSPy pipelines by automatically synthesizing few-shot examples with reasoning traces, bridging the gap between raw data and high-performing models. It is specifically designed for scenarios with small labeled datasets (10-50 examples), allowing users to leverage teacher models to improve student model performance without manual prompt engineering. This skill streamlines the 'compile' phase of DSPy development, ensuring that models are primed with the most effective demonstrations for production tasks.

主な機能

01Automatic synthesis of few-shot demonstrations with reasoning traces

0231 GitHub stars

03Teacher-student model optimization workflow

04Configurable metric-based demonstration selection

05Built-in patterns for saving and deploying compiled programs

06Optimized for small training sets of 10-50 examples

ユースケース

01Automating the prompt engineering process for specialized domain-specific tasks

02Improving model accuracy for complex RAG pipelines through Chain-of-Thought demonstrations

03Optimizing smaller, cost-effective models by bootstrapping examples from a larger teacher model

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add omidzamani/dspy-skills dspy-bootstrap-fewshot

For use in Claude.ai and ChatGPT

Download Skill