EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hugging Face Blog·Sep 3·5 min read
Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps
TL;DR
A 350M model was fine-tuned to enhance its ability to produce structured outputs, achieving performance comparable to larger models. This approach emphasizes the importance of schema compliance for real-world applications.
✦ Why It Matters
Consider fine-tuning smaller models for specific tasks to enhance their structured output capabilities today.
Key Takeaways
- Fine-tune smaller models for better structured outputs.
- Use task-specific reward signals to enhance performance.
- Evaluate models on benchmarks like IFStruct for reliability.
- Leverage tools like llama.cpp for model serving.
How It Works
The fine-tuning process involved augmenting prompts and using a reward system that scores outputs based on their adherence to JSON schema, format, and field count. This targeted approach allowed the model to learn specific output structures, improving its performance on structured output tasks.