dspy-finetune-bootstrap

Fine-tune a DSPy program into deployable model weights via BootstrapFinetune.

120|13|Updated Dec 21, 2025

Search Tags:#weights #bootstrap #production-deployment #dspy #finetune #model-training

Ready to run

npx skills add https://github.com/OmidZamani/dspy-skills --skill dspy-finetune-bootstrap

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dspy-finetune-bootstrap
Source: https://github.com/OmidZamani/dspy-skills/tree/main/skills/dspy-finetune-bootstrap
Command: npx skills add https://github.com/OmidZamani/dspy-skills --skill dspy-finetune-bootstrap

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill distills a DSPy program into fine-tuned model weights for efficient production deployment, reducing inference costs and latency.

Core Features & Use Cases

BootstrapFinetune workflow: Prepare a teacher, generate traces, and fine-tune a student model.
Production readiness: Output a finetuned program and a saved model path suitable for deployment.
Use Case: When you have a large teacher model and want a smaller, fast-serving version without API-only constraints.

Quick Start

Configure a strong teacher LM, instantiate a teacher DSPy module, and run BootstrapFinetune with your trainset and train_kwargs to produce a finetuned model that you can save and load for inference.

Frequently Asked Questions about dspy-finetune-bootstrap

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I fine-tune a DSPy program into smaller model weights for production?▼

Fine-tune a DSPy program using BootstrapFinetune by providing your teacher DSPy module, training examples, and training configuration. The workflow prepares the teacher, generates traces, fine-tunes a student model, and outputs a finetuned program and saved model path ready for deployment.

When should I use BootstrapFinetune instead of running my large model directly?▼

Use BootstrapFinetune when you need to reduce inference costs, lower latency, or deploy to resource-constrained environments. It distills a large teacher model into an efficient student model while preserving task performance.

What inputs do I need to run BootstrapFinetune?▼

You need a dspy.Module program, a list of dspy.Example training instances, an optional metric callable for evaluation, and train_kwargs dictionary with training parameters. These inputs flow through prepare, trace generation, and fine-tuning phases.

Can I use BootstrapFinetune with my existing DSPy modules and datasets?▼

Yes. BootstrapFinetune works with any dspy.Module and list of dspy.Example objects, making it compatible with existing DSPy pipelines and training data without requiring format conversion.

What do I get after BootstrapFinetune completes?▼

BootstrapFinetune outputs a finetuned dspy.Module ready for inference and a model_path string pointing to saved weights that you can load and deploy in production environments.

Does BootstrapFinetune work for reducing API call costs with large language models?▼

Yes. By distilling a large teacher model into fine-tuned weights, BootstrapFinetune eliminates reliance on expensive API-only inference and enables cost-effective self-hosted deployment.