Does this skill require specialized hardware?

Yes, unlike prompt-based optimization, fine-tuning model weights requires GPU resources to execute the training phases.

Can I distill any DSPy program?

Most DSPy modules can be distilled, provided you have a strong teacher model (like GPT-4 or Claude 3.5 Sonnet) and a student model where you can access and update the weights.

Is this skill compatible with the latest DSPy versions?

This skill is specifically designed for DSPy 3.1.2+ and correctly handles the experimental flags required for the BootstrapFinetune optimizer.

How much can I reduce costs using this skill?

By distilling a GPT-4 based program into a smaller model like Llama-3 or Mistral, users often see a 10x-50x reduction in per-token inference costs.

What is BootstrapFinetune in DSPy?

BootstrapFinetune is an optimizer that distills the logic of a DSPy program into the weights of a student model by using a teacher model to generate high-quality training examples.

DSPy Model Distillation & Fine-tuning

Name: DSPy Model Distillation & Fine-tuning
Author: OmidZamani

byOmidZamani

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데이터 과학 및 ML

Distills complex DSPy programs into optimized model weights to reduce inference costs and increase production performance.

This skill provides a comprehensive framework for applying the BootstrapFinetune optimizer to DSPy programs, enabling the distillation of high-performance teacher models into efficient student models. It guides users through the entire lifecycle of model optimization—from preparing teacher traces and configuring experimental settings to managing training hyperparameters and deploying the final fine-tuned weights. By automating the transition from expensive API-based models to smaller, task-specific models, this skill is essential for developers looking to scale DSPy applications while minimizing latency and operational expenses.

주요 기능

01Automated teacher-student distillation workflow

02Experimental BootstrapFinetune optimizer integration

03Model state persistence and deployment patterns

04Support for RAG and classification pipeline optimization

05Comprehensive hyperparameter management for fine-tuning

0631 GitHub stars

사용 사례

01Improving application latency for real-time DSPy-powered features

02Deploying DSPy programs to resource-constrained or on-premise environments

03Reducing LLM API costs by migrating from GPT-4 to fine-tuned open-source models

What are Skills?·How to Install

Install with 🐟 Skill.Fish

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

For use in Claude.ai and ChatGPT

Download Skill