Pre-trained model
type: concept
A pre-trained model is a machine learning model trained on a large dataset for a general task (e.g., language understanding), which can be adapted for specific applications through fine-tuning.
- Efficiency: Avoids training from scratch, saving significant time and computational resources
- Transfer Learning: Leverages pre-existing knowledge to improve performance on target tasks with limited data
- Common in LLMs: Most state-of-the-art language models (e.g., GPT, BERT) are pre-trained
Fine-tuning process:
- Adapts a pre-trained model to a specific task by training on a smaller, task-specific dataset
- Analogy: Training an experienced chef (pre-trained model) on your restaurant’s recipes (fine-tuning) rather than teaching from scratch
Related concepts:
- large-language-model: A category of pre-trained models for language tasks
- fine-tuning: The adaptation process described above
- ollama: Tool for deploying locally hosted fine-tuned LLMs
Backlink: 2026 04 14 Fine tuning a LLM for use locally Tech with Tim
Source Notes
- 2026-04-07: Chroma Context 1 Self Editing Search Agent for Efficient RAG · ▶ source
- 2026-04-12: DreamDojo AI Bridging Robotics Sim2Real Gap for Complex Tasks · ▶ source
- 2026-04-26: DeepSeek V4: China
- 2026-04-27: Google Gemma · ▶ source