Streamline Your AI Development: The 3 Essential Stages

Master AI model development with 3 key stages: pretraining, fine-tuning, and advanced alignment. Boost performance by leveraging domain-specific data and human feedback.

Development Method Categories

• Domain Knowledge Injection: Continue PreTraining (CPT): Vertical large models are typically developed based on general models. This requires continued pretraining using domain-specific data.

• Knowledge Recall (Activation): SFT (Supervised Fine-Tuning): SFT helps large models understand and answer domain-specific questions.

• Basic Preference Alignment: Reward Models (RM) and Reinforcement Learning (RL) align the model’s responses with human preferences, such as writing style.

• Advanced Preference Alignment: RLHF (Reinforcement Learning with Human Feedback) and DPO (Direct Preference Optimization).

Development Stages Classification

The model development process is divided into three stages:

• Stage 1: Continue pre-training, which involves additional pretraining on large domain-specific datasets to inject domain knowledge into the GPT model.

• Stage 2: SFT (Supervised Fine-Tuning), where instruction-tuning datasets are used to fine-tune the pre-trained model to align with specific instructions.

• Stage 3: Choose between RLHF and DPO for advanced preference alignment.