Fine-tuned LLMs

Fine-tuning transforms general-purpose models into specialized experts by retraining them on proprietary datasets for niche tasks like medical coding or legal drafting.

Fine-tuning moves beyond prompt engineering by updating a model's weights using techniques like LoRA (Low-Rank Adaptation) or QLoRA. This process slashes inference costs by allowing smaller models (e.g., Llama 3 8B or Mistral 7B) to outperform massive models like GPT-4 on specific domains. By training on 1,000 to 10,000 high-quality examples, developers lock in precise brand voices, complex JSON schemas, or industry-specific terminology. It is the gold standard for latency-sensitive applications where generalist models fail to meet strict accuracy or formatting requirements.

https://github.com/basecamp/omakase-llm