Large Language Models (LLMs) such as GPT, PaLM, and LLaMa have enhanced AI and NLP by enabling machines to comprehend and produce human-like content. Finetuning is crucial to adapt these generalist models to specialized activities. Approaches include Parameter Efficient Fine Tuning (PEFT), Supervised Finetuning with hyperparameter tweaking, transfer learning, and few-shot learning, and Reinforcement Learning from Human Feedback (RLHF) involving reward modeling and Proximal Policy Optimisation. Source: Various.
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Large Language Models (LLMs) and Fine Tuning
Large Language Models (LLMs) such as GPT, PaLM, and LLaMa have made significant advancements in AI and NLP, enabling machines to comprehend and produce human-like content. However, their generalist nature often falls short in specialized activities or domains. Fine tuning is a crucial procedure that greatly improves the model’s performance by retraining it on a domain-specific dataset, allowing it to acquire the nuances and distinctive features of the intended field.
What is Fine Tuning?
Finetuning modifies a language model that has already been trained to perform well in a certain area. It involves retraining the model on a domain-specific dataset to enhance its performance on tasks linked to the domain, improving its understanding of intricacies, vocabulary, and context.
Fine Tuning Approaches
1. Parameter Efficient Fine Tuning (PEFT)
a) LoRA
Low-Rank Adaptation (LoRA) is a method that adds new parameters during training without permanently changing the model architecture, enabling parameter-efficient finetuning without adding more parameters to the model overall.
b) QLoRA
Quantized LoRA (QLoRA) combines low-precision storage with high-precision computation techniques to maintain good accuracy and performance while keeping the model small.
2. Supervised Fine Tuning
a) Basic Hyperparameter Tuning
Adjusting hyperparameters and important variables to find the ideal mix that enables the model to learn from task-specific data most effectively, significantly increasing learning efficacy and reducing overfitting.
b) Transfer Learning
Refining a pre-trained model on a smaller, task-specific dataset, utilizing the model’s broad information to tailor it to the new task, saving time and resources while producing better outcomes.
c) Few-shot Learning
Enabling a model to rapidly adjust to a new task using the least amount of task-specific data possible, helpful when gathering a sizable labeled dataset for the new task is not feasible.
3. Reinforcement Learning from Human Feedback (RLHF)
a) Reward Modeling
Assessing the model’s performance through human evaluation and training it to predict rewards for various outputs based on human evaluations.
b) Proximal Policy Optimisation
Improving the model’s decision-making policy iteratively to improve expected reward outcomes, ensuring controlled and steady advancement in learning.
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