PPO fine-tuning

After having initialized the trainer, you now have to initialize the loop to fine-tune the model.

The reward trainer ppo_trainer has been initialized using the PPOTrainer class from the trl python library.

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Reinforcement Learning from Human Feedback (RLHF)

Anleitung zur Übung

Generate response tensors using the input ids, and the trainer within the PPO loop.
Complete the step within the PPO loop that uses queries, response, and reward data to optimize the PPO model.

Interaktive Übung

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for batch in tqdm(ppo_trainer.dataloader): 

    # Generate responses for the given queries using the trainer
    response_tensors = ____(batch["input_ids"])

    batch["response"] = [tokenizer.decode(r.squeeze()) for r in response_tensors]

    texts = [q + r for q, r in zip(batch["query"], batch["response"])]

    rewards = reward_model(texts)

    # Training PPO step with the query, responses ids, and rewards
    stats = ____(batch["input_ids"], response_tensors, rewards)

    ppo_trainer.log_stats(stats, batch, rewards)

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Reinforcement Learning from Human Feedback (RLHF)

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This chapter introduces the basics of Reinforcement Learning with Human Feedback (RLHF), a technique that uses human input to help AI models learn more effectively. Get started with RLHF by understanding how it differs from traditional reinforcement learning and why human feedback can enhance AI performance in various domains.

Exercise 1: Introduction to RLHF Exercise 2: Text generation with RLHF Exercise 3: Classifying generated text for RLHF Exercise 4: RL vs. RLHF Exercise 5: Exploring pre-trained LLMs Exercise 6: Tokenize a text dataset Exercise 7: Fine-tuning for review classification Exercise 8: Preparing data for RLHF Exercise 9: Preparing the preference dataset Exercise 10: Extracting prompts

Discover how to set up systems for gathering human feedback in this Chapter. Learn best practices for collecting high-quality data, from pairwise comparisons to uncertainty sampling, and explore strategies for enhancing your data collection.

Exercise 1: Methods for high-quality feedback gathering Exercise 2: Understanding comparison and rating in RLHF Exercise 3: Comparing slogans for a gym campaign Exercise 4: Measuring feedback quality and relevance Exercise 5: Low confidence Exercise 6: K-means for feedback clustering Exercise 7: Active learning Exercise 8: Implementing an active learning pipeline Exercise 9: Active learning loop

In this Chapter, you'll get into the core of Reinforcement Learning from Human Feedback training. This includes exploring fine-tuning with PPO, techniques to train efficiently, and handling potential divergences from your metrics' objectives.

Exercise 1: Reward models explored Exercise 2: Initializing the reward Exercise 3: Setting up the reward trainer Exercise 4: Training with PPO Exercise 5: Initialize the PPO trainer Exercise 6: PPO fine-tuning

Aktuelle Übung

Exercise 7: Efficient fine-tuning in RLHF Exercise 8: Prepare for 8-bit Training Exercise 9: Train with LoRA

Explore key techniques for assessing and improving model performance in this last Chapter of Reinforcement Learning from Human Feedback (RLHF): from fine-tuning metrics to incorporating diverse feedback sources, you'll be provided with a comprehensive toolkit to refine your models effectively.

Exercise 1: Model metrics and adjustments Exercise 2: Mitigating negative KL divergence Exercise 3: Checking the reward model Exercise 4: Incorporating diverse feedback sources Exercise 5: Majority voting on multiple data sources Exercise 6: Unreliable data source identification Exercise 7: Evaluating RLHF models Exercise 8: Interpreting curves Exercise 9: Evaluating RLHF with metrics Exercise 10: Wrapping up your RLHF journey