The Distillation Pipeline
Instead of manually labeling 10,000 documents, write a highly detailed prompt for GPT-4 or Claude 3 Opus to annotate 100 documents. Verify the quality, then run the pipeline over the remaining 9,900 documents. Use this resulting dataset to fine-tune a local Llama-3-8B.
Avoiding Model Collapse
Ensure the seed data fed into the teacher model is highly diverse. Synthetic data generation works best for classification, extraction, and formatting tasks, rather than generating novel facts.
Internal Resources
- Inference Cost Calculator
- Latency Estimator
- Tabular vs Deep Learning
- Local LLMs vs Managed APIs
- Time Series Baselines
- Tabular Model Selector
- Vision Architecture Selector
- VRAM Calculator
- Choosing Embeddings
- CNN vs ViT in 2024
- RLHF vs DPO
- RAG Chunking Strategies
- RAG Chunk Size Calculator
- Token Ratio Estimator
- Synthetic Data Generation
- Quantization Methods Explained
- Quantization Memory Savings
- Fine-Tuning vs LoRA
- LoRA Rank Calculator
- Multimodal Model Landscape