dspy-finetune-bootstrap_skill

Overview

This skill distills a DSPy program into fine-tuned model weights using BootstrapFinetune to produce a smaller, faster student model for production. It targets cases where you want to reduce inference cost, speed up responses, or deploy DSPy programs in resource-constrained environments. The skill orchestrates teacher trace generation, student training, evaluation, and state-only saving for deployment.

How this skill works

Enable experimental features in DSPy, configure a strong teacher LM, and generate training traces from the teacher program. BootstrapFinetune compiles the teacher and a training dataset into a student model by running knowledge-distillation style training using supplied train_kwargs and an optional validation metric. After training you save state-only weights that can be loaded back into the program architecture for inference.

When to use it

• You have a working DSPy program running with a large teacher model and want to cut inference cost.
• You need faster latency or smaller model footprint for production or edge deployment.
• You want to convert a prompt/program behavior into reusable model weights.
• You plan to deploy a RAG or Chain-of-Thought pipeline more cheaply.
• You need an automated teacher-student training pipeline with evaluation and saving.

Best practices

• Use a strong teacher (GPT-4 / Claude) so generated traces are high quality.
• Prepare high-quality, representative training and dev sets; student quality follows data quality.
• Pass a validation metric and compare student vs teacher on a held-out set to ensure improvement.
• Start with 3–5 epochs and tune learning_rate (1e-5 to 5e-5) and batch sizes for stability.
• Monitor validation loss and watch for overfitting; use weight_decay and warmup_ratio as needed.

Example use cases

• Fine-tune a QA Chain-of-Thought DSPy module into a compact model for production API serving.
• Distill a RAG-based classifier (retriever + LM) into a lightweight student for lower-latency inference.
• Convert a prompt-optimized text classifier into saved weights to run without external LM API calls.
• Reduce cost by training a student from an expensive teacher and deploying the student on GPU/CPU.