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LLM Fine-Tuning Strategies and Techniques

LLM Fine-Tuning Strategies and Techniques

LLM Fine-Tuning Strategies and Techniques

Fine-tuning adapts a pre-trained language model to specific tasks or domains. Different fine-tuning approaches offer trade-offs between customization, cost, and performance.

Full Fine-Tuning

Full fine-tuning updates all model parameters on domain-specific data. This achieves the highest task performance but requires significant computational resources. Full fine-tuning of a 7B parameter model requires 4-8 GPUs with 80GB memory each.

Full fine-tuning is appropriate for domain adaptation (legal, medical, code) where broad knowledge transfer is needed. Training data should be 10,000-100,000 high-quality examples.…