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6 Common LLM Customization Strategies Briefly Explained
- There are various specialized techniques for customizing LLMs, including LoRA fine-tuning, Chain of Thought, Retrieval Augmented Generation, ReAct, and Agent frameworks.
- Selecting the appropriate foundation models is the first step in customizing LLMs, with options from open-source platforms like Huggingface or proprietary models from cloud providers and AI companies.
- Factors to consider when choosing LLMs include open source vs. proprietary model, task compatibility, architecture, and size of the model.
- Six common strategies for LLM customization, based on increasing resource consumption, include prompt engineering, decoding strategy, RAG, Agent, fine-tuning, and RLHF.
- Prompt engineering involves crafting prompts strategically to control LLM responses, with techniques like zero shot, one shot, and few shot prompting.
- Techniques like Chain of Thought and ReAct aim to improve LLM performance on multi-step problems by breaking down reasoning tasks and integrating action spaces for decision-making.
- ReAct combines reasoning trajectories with an action space to strengthen LLM capabilities through interacting with the environment.
- Fine-tuning involves feeding specialized datasets to fine-tune LLMs, with approaches like full fine-tuning and parameter efficient fine-tuning to optimize model performance.
- RLHF is a reinforcement learning technique that fine tunes LLMs based on human preferences, utilizing a reward model and reinforcement learning policy.
- The article provides practical insights and examples on implementing these LLM customization strategies to improve model efficiency and performance.
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