De beloning initialiseren

Je bent in de laatste fase van het uitrollen van een generatief model dat persoonlijke aanbevelingen doet voor een online boekwinkel. Om dit model af te stemmen op aanbevelingen die mensen prefereren, moet je een rewardmodel trainen met verzamelde voorkeursdata. De eerste stap is het initialiseren van het model en de configuratieparameters.

De AutoTokenizer en AutoModelForSequenceClassification zijn vooraf geladen uit transformers. RewardConfig is vooraf geladen uit trl.

Deze oefening maakt deel uit van de cursus

Reinforcement Learning from Human Feedback (RLHF)

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Oefeninstructies

Laad het GPT-1-model, "openai-gpt", voor de sequentieclassificatietaak met Hugging Face's AutoModelForSequenceClassification.
Initialiseer de beloningsconfiguratie met "output_dir" als de uitvoermap en zet de maximale tokenlengte op 60.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Load the pre-trained GPT-1 model for text classification
model = ____

tokenizer = AutoTokenizer.from_pretrained("openai-gpt")

# Initialize the reward configuration and set max_length
config = ____(output_dir=____, max_length=____)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Reinforcement Learning from Human Feedback (RLHF)

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4.8+

Begin de cursus gratis

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: Rewardmodellen onder de loep Exercise 2: De beloning initialiseren

Huidige oefening

Exercise 3: De reward trainer instellen Exercise 4: Trainen met PPO Exercise 5: Initialiseer de PPO-trainer Exercise 6: PPO-finetuning Exercise 7: Efficiënt fine-tunen in RLHF Exercise 8: Voorbereiden op 8-bit-training Exercise 9: Trainen met 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