dspy-finetune-bootstrap
This skill should be used when the user asks to "fine-tune a DSPy model", "distill a program into weights", "use BootstrapFinetune", "create a student model", "reduce inference costs with fine-tuning", mentions "model distillation", "teacher-student training", or wants to deploy a DSPy program as fine-tuned weights for production efficiency.
npx skills add majiayu000/claude-skill-registry --skill dspy-finetune-bootstrap --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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: Enable Experimental Features & Generate Tra
- Goal
- When to Use
- Inputs
- Outputs
- Workflow
- Phase 1: Prepare Teacher Program
- Phase 2: Enable Experimental Features & Generate Training Traces
- Phase 3: Fine-tune Student Model
- Phase 4: Deploy
- Production Example
- Training Arguments Reference
- Best Practices
- Limitations
- Official Documentation
What does the dspy-finetune-bootstrap skill do?
This skill should be used when the user asks to "fine-tune a DSPy model", "distill a program into weights", "use BootstrapFinetune", "create a student model", "reduce inference costs with fine-tuning", mentions "model distillation", "teacher-student training", or wants to deploy a DSPy program as fine-tuned weights for production efficiency.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill dspy-finetune-bootstrap --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.