dspy-finetune-bootstrap

Files (2)

• SKILL.md
• example.py

SKILL.md (first lines)

# 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={
        'learning_rate': 5e-5,
        'num_train_epochs': 3,
        'per_device_train_batch_size': 4,
        'warmup_ratio': 0.1
    }
)
```

### Phase 3: Fine-tune Student Model

```python
teacher = TeacherQA()
teacher.set_lm(dspy.settings.lm)
finetuned = optimizer.compile(teacher, trainset=trainset)
```

### Phase 4: Deploy

```python
# Save the fine-tuned model (saves state-only by default)
finetuned.save("finetuned_qa_model.json")

# Load and use (must recreate architecture first)
loaded = TeacherQA()
loaded.load("finetuned_qa_model.json")
result = loaded(question="What is machine learning?")
```

## Production Example

```python
import dspy
from dspy.teleprompt import BootstrapFinetune
from dspy.evaluate import Evaluate
import logging
import os

logger = logging.getLogger(__name__)

class ClassificationSignature(dspy.Signature):
    """Classify text into categories."""
    text: str = dspy.InputField()
    label: str = dspy.OutputField(desc="Category: positive, negative, neutral")

class TextClassifier(dspy.Module):
    def __init__(self):
        self.classify = dspy.Predict(ClassificationSignature)
    
    def forward(self, text):
        return self.classify(text=text)

def classification_metric(gold, pred, trace=None):
    """Exact label match."""
    gold_label = gold.label.lower().strip()
    pred_label = pred.label.lower().strip() if pred.label else ""
    return gold_label == pred_label

def finetune_classifier(trainset, devset, output_dir="./finetuned_model"):
    """Full fine-tuning pipeline."""
    
    # Configure teacher (strong model)

From OmidZamani/dspy-skills, MIT.

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