dspy-finetune-bootstrap

# DSPy BootstrapFinetune Optimizer ## Goal Distill a DSPy program into fine-tuned model weights for efficient production deployment. ## When to Use - You have a working DSPy program with a large model - Need to reduce inference costs - Want faster responses (smaller model) - Deploying to resource-constrained environments ## Inputs | Input | Type | Description | |-------|------|-------------| | `program` | `dspy.Module` | Teacher program to distill | | `trainset` | `list[dspy.Example]` | Training examples | | `metric` | `callable` | Validation metric (optional) | | `train_kwargs` | `dict` | Training hyperparameters | ## Outputs | Output | Type | Description | |--------|------|-------------| | `finetuned_program` | `dspy.Module` | Program with fine-tuned weights | | `model_path` | `str` | Path to saved model | ## Workflow ### Phase 1: Prepare Teacher Program ```python import dspy # Configure with strong teacher model dspy.configure(lm=dspy.LM("openai/gpt-4o")) class TeacherQA(dspy.Module): def __init__(self): self.cot = dspy.ChainOfThought("question -> answer") def forward(self, question): return self.cot(question=question) ``` ### Phase 2: Configure Fine-Tuning Assign the LM directly to predictors before fine-tuning: ```python import dspy from dspy.teleprompt import BootstrapFinetune optimizer = BootstrapFinetune( metric=lambda gold, pred, trace=None: gold.answer.lower() in pred.answer.lower(), train_kwargs={ 'learn...