Itinai.com it company office background blured chaos 50 v 41eae118 fe3f 43d0 8564 55d2ed4291fc 0
Itinai.com it company office background blured chaos 50 v 41eae118 fe3f 43d0 8564 55d2ed4291fc 0

What is Fine Tuning and Best Methods for Large Language Model (LLM) Fine-Tuning

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.

 What is Fine Tuning and Best Methods for Large Language Model (LLM) Fine-Tuning

“`html

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.

References:

Turing

Analytics Vidhya

Medium

Analytics Vidhya

SignalFire

AI Solutions for Middle Managers

If you want to evolve your company with AI, stay competitive, and use AI for your advantage, consider the practical AI solution of fine tuning large language models. Discover how AI can redefine your way of work, identify automation opportunities, define KPIs, select an AI solution, and implement gradually. For AI KPI management advice, connect with us at hello@itinai.com. For continuous insights into leveraging AI, stay tuned on our Telegram or Twitter.

Spotlight on a Practical AI Solution

Consider the AI Sales Bot from itinai.com/aisalesbot, designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. Explore how AI can redefine your sales processes and customer engagement.

“`

List of Useful Links:

Itinai.com office ai background high tech quantum computing 0002ba7c e3d6 4fd7 abd6 cfe4e5f08aeb 0

Vladimir Dyachkov, Ph.D
Editor-in-Chief itinai.com

I believe that AI is only as powerful as the human insight guiding it.

Unleash Your Creative Potential with AI Agents

Competitors are already using AI Agents

Business Problems We Solve

  • Automation of internal processes.
  • Optimizing AI costs without huge budgets.
  • Training staff, developing custom courses for business needs
  • Integrating AI into client work, automating first lines of contact

Large and Medium Businesses

Startups

Offline Business

100% of clients report increased productivity and reduced operati

AI news and solutions