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Loading...Introduction to Fine-Tuning LLaMA 2.0 with RLHF
Last quarter, our team discovered that fine-tuning LLaMA 2.0 with Reinforcement Learning from Human Feedback (RLHF) significantly improved our conversational AI model's performance. We tried various approaches, but RLHF stood out for its ability to align the model's responses with human preferences.
The Problem with Standard Fine-Tuning Methods
Standard fine-tuning methods often rely on supervised learning, where the model is trained on labeled datasets. However, this approach can be limiting when dealing with complex, open-ended tasks like conversational AI. The model may not always understand the nuances of human language and may generate responses that are not engaging or relevant.
How RLHF Works
RLHF is a type of reinforcement learning that involves training the model using human feedback. The process involves the following steps:
- Data Collection: We collect a dataset of human-generated text and corresponding feedback (e.g.
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