Large language models (LLMs) are trained on human-generated text, but additional methods are needed to align an LLM with human values and preferences.
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Large language models (LLMs) are trained on human-generated text, but additional methods are needed to align an LLM with human values and preferences.
Large language models (LLMs) are trained on human-generated text, but additional methods are needed to align an LLM with human values and preferences.
Reinforcement Learning from Human Feedback (RLHF) is currently the main method for aligning LLMs with human values and preferences. RLHF is also used for further tuning a base LLM to align with values and preferences that are specific to your use case.
In this course, you will gain a conceptual understanding of the RLHF training process, and then practice applying RLHF to tune an LLM. You will:
1. Explore the two datasets that are used in RLHF training: the “preference” and “prompt” datasets.
2. Use the open source Google Cloud Pipeline Components Library, to fine-tune the Llama 2 model with RLHF.
3. Assess the tuned LLM against the original base model by comparing loss curves and using the “Side-by-Side (SxS)” method.
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Activities
Organize and review course materials
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Help you stay organized and focused, enhancing your ability to retain and recall information presented in the course.
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Create a system for organizing your notes, assignments, and other course materials.
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Review your materials regularly to reinforce your understanding.
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Summarize key concepts and ideas from the course materials.
Review NLP fundamentals
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Help you refresh your knowledge of the underlying technology and concepts of NLP.
Browse courses on
NLP
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Read through your notes or textbook chapters on NLP fundamentals.
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Complete practice problems or quizzes on NLP concepts.
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Review the course syllabus and identify the key NLP concepts that will be covered.
Follow a tutorial on RLHF
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Provide you with a deeper understanding of the RLHF process and its application in aligning LLMs with human values and preferences.
Show steps
-
Identify a comprehensive tutorial or course on RLHF.
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Follow the tutorial step-by-step, completing the exercises and assignments.
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Take notes on the key concepts and techniques involved in RLHF.
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Practice fine-tuning LLMs with RLHF
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Allow you to practice the practical application of RLHF in fine-tuning LLMs, giving you hands-on experience with the process.
Show steps
-
Set up your development environment for RLHF.
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Select a pre-trained LLM and a dataset for RLHF.
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Implement the RLHF fine-tuning process.
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Evaluate the fine-tuned LLM against the original base model.
Build a project using a fine-tuned LLM
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Help you apply your knowledge of RLHF and LLM fine-tuning to a practical project, demonstrating your understanding and ability to use these techniques.
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-
Identify a specific use case or problem that can be addressed with a fine-tuned LLM.
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Design and develop the project using the fine-tuned LLM.
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Evaluate the performance of the project and make improvements as needed.
Organize and review course materials
Show steps
Help you stay organized and focused, enhancing your ability to retain and recall information presented in the course.
Show steps
- Create a system for organizing your notes, assignments, and other course materials.
- Review your materials regularly to reinforce your understanding.
- Summarize key concepts and ideas from the course materials.
Review NLP fundamentals
Show steps
Help you refresh your knowledge of the underlying technology and concepts of NLP.
Browse courses on
NLP
Show steps
- Read through your notes or textbook chapters on NLP fundamentals.
- Complete practice problems or quizzes on NLP concepts.
- Review the course syllabus and identify the key NLP concepts that will be covered.
Follow a tutorial on RLHF
Show steps
Provide you with a deeper understanding of the RLHF process and its application in aligning LLMs with human values and preferences.
Show steps
- Identify a comprehensive tutorial or course on RLHF.
- Follow the tutorial step-by-step, completing the exercises and assignments.
- Take notes on the key concepts and techniques involved in RLHF.
Practice fine-tuning LLMs with RLHF
Show steps
Allow you to practice the practical application of RLHF in fine-tuning LLMs, giving you hands-on experience with the process.
Show steps
- Set up your development environment for RLHF.
- Select a pre-trained LLM and a dataset for RLHF.
- Implement the RLHF fine-tuning process.
- Evaluate the fine-tuned LLM against the original base model.
Build a project using a fine-tuned LLM
Show steps
Help you apply your knowledge of RLHF and LLM fine-tuning to a practical project, demonstrating your understanding and ability to use these techniques.
Show steps
- Identify a specific use case or problem that can be addressed with a fine-tuned LLM.
- Design and develop the project using the fine-tuned LLM.
- Evaluate the performance of the project and make improvements as needed.
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