Trainen met LoRA

Je wilde beginnen met RLHF-finetuning maar kreeg steeds out-of-memory-fouten. Hoewel je het model in 8-bit-precisie laadde, bleef de fout bestaan. Daarom besloot je de volgende stap te zetten en LoRA toe te passen voor efficiënter finetunen.

Het volgende is al voor je geïmporteerd:

Het model geladen in 8-bit-precisie als pretrained_model_8bit
LoraConfig en get_peft_model uit peft
AutoModelForCausalLMWithValueHead uit trl

Deze oefening maakt deel uit van de cursus

Reinforcement Learning from Human Feedback (RLHF)

Cursus bekijken

Oefeninstructies

Zet de LoRA-dropout op 0.1 en stel het bias-type in op lora-only.
Voeg de LoRA-configuratie toe aan het model.
Richt het model in met een value head voor PPO-training.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Set the configuration parameters
config = LoraConfig(
    r=32,  
    lora_alpha=32,  
    lora_dropout=____,  
    bias=____)  

# Apply the LoRA configuration to the 8-bit model
lora_model = get_peft_model(pretrained_model_8bit, ____)
# Set up the tokenizer and model with a value head for PPO training
model = ____.from_pretrained(____)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Reinforcement Learning from Human Feedback (RLHF)

SkillTag.level.advancedSkillTag.label

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 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

Huidige oefening

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