Fine-tuning

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A process of retraining a language model on a new dataset of data. This can be used to improve the model's performance on a specific task, such as generating text, translating languages, or answering questions. Fine-tuning is a way to add new knowledge to an existing AI model. It’s a simple upgrade that allows the model to learn new information.

Here are some more detailed information on fine-tuning:

  • Fine-tuning is a relatively simple process. The first step is to select a pre-trained language model. There are many pre-trained language models available, such as GPT-3, RoBERTa, and XLNet. Once you have selected a pre-trained language model, you need to gather a dataset of data for fine-tuning. This dataset should be relevant to the task that you want the model to perform. For example, if you want to fine-tune a language model for question answering, you would need to gather a dataset of questions and answers.
  • The next step is to fine-tune the language model on the dataset of data. This is done by using a technique called supervised learning. In supervised learning, the model is given a set of labeled examples. In the case of fine-tuning, the labels are the answers to the questions in the dataset. The model is then trained to predict the labels for the unlabeled examples in the dataset.
  • Fine-tuning can be a time-consuming process, but it can significantly improve the performance of a language model on a specific task. For example, fine-tuning a language model on a dataset of question and answers can improve the model's ability to answer new questions.


Large Language Model (LLM) Ecosystem Explained

The Large Language Model (LLM) ecosystem refers to the various commercial and open-source LLM providers, their offerings, and the tooling that helps accelerate their wide adoption. The functionality of LLMs can be segmented into five areas: Knowledge Answering, Translation, Text Generation, Response Generation, and Classification. There are many options to choose from for all types of language tasks.


LLM Ecosystem explained: Your ultimate Guide to AI | code_your_own_AI
Introduction to the world of LLM (Large Language Models) in April 2023. With detailed explanation of GPT-3.5, GPT-4, T5, Flan-T5 to LLama, Alpaca and KOALA LLM, plus dataset sources and configurations. Including ICL (in-context learning), adapter fine-tuning, PEFT LoRA and classical fine-tuning of LLM explained. When to choose what type of data set for what LLM job?

A comprehensive LLM /AI ecosystem is essential for the creation and implementation of sophisticated AI applications. It facilitates the efficient processing of large-scale data, the development of complex machine learning models, and the deployment of intelligent systems capable of performing complex tasks.

As the field of AI continues to evolve and expand, the importance of a well-integrated and cohesive AI ecosystem cannot be overstated.

A complete overview of today's LLM and how you can train them for your needs.

Methods For Fine-tuning an LLM


Fine-tuning can be applied to various types of models, such as convolutional neural networks, recurrent neural networks, and large language models. There are different ways to fine-tune a model, depending on the amount and similarity of the data available for the new task, the complexity and size of the model, and the computational resources and time constraints. Here are some examples of Fine-tuning:

  • Fine-tuning OpenAI's base models such as Davinc, Curie, Babbage, and Ada to improve their performance on a variety of tasks, such as generating text, translating languages, and answering questions.
  • Fine-tuning a binary classifier to rate each completion for truthfulness based on expert-labeled examples.
  • Incorporating proprietary content into a language model to improve its ability to provide relevant answers to questions.
  • Full-model fine-tuning: This method involves updating all the parameters of the pre-trained model on the new task. This method can achieve high performance, but it is also computationally expensive and prone to overfitting if the new data is small or noisy.
  • Partial-model fine-tuning: This method involves updating only a subset of the parameters of the pre-trained model, while keeping the rest fixed or frozen. This method can reduce the computational cost and prevent overfitting, but it also requires choosing which layers or modules to fine-tune and which ones to freeze. A common heuristic is to freeze the earlier layers that capture general features and fine-tune the later layers that capture task-specific features.
  • Adapter-based fine-tuning: This method involves adding small neural networks, called adapters, to each layer or module of the pre-trained model, and updating only the parameters of the adapters on the new task, while keeping the original parameters frozen. This method can achieve parameter-efficient fine-tuning, as adapters have much fewer parameters than the original model, and preserve the performance and robustness of the pre-trained model.

Fine-tuning is a powerful technique that can be used to improve the performance of language models on a variety of tasks. If you are looking to improve the performance of a language model on a specific task, fine-tuning is a good option to consider.

Instruction Tuning


Instructional tuning is a technique that aims to teach Large Language Model (LLM) to follow natural language instructions, such as prompts, examples, and constraints, to perform better on various Natural Language Processing (NLP) tasks. Instructional tuning can improve the capabilities and controllability of LLMs across different tasks, domains, and modalities. It can also enable LLMs to generalize to unseen tasks by using instructions as a bridge between the pretraining objective and the user’s objective.

Instructional tuning involves fine-tuning LLMs with instructional data, which consists of pairs of human-written instructions and desired outputs. For example, an instruction could be “Write a summary of the following article in three sentences” and an output could be “The article discusses the benefits of instructional tuning for large language models. It presents a survey paper that covers the fundamentals, challenges, and applications of this technique. It also introduces a new method called LoRA that leverages LLMs to generate instructional data for themselves.” Instructional data can be collected from various sources, such as existing NLP datasets, expert annotations, or even LLMs themselves.

LoRA

Low-Rank Adaptation (LoRA) is a technique that leverages Large Language Model (LLM)s to generate instructional data for themselves. Instructional data consists of pairs of human-written instructions and desired outputs, which can be used to fine-tune LLMs to follow natural language instructions. LoRA involves prompting LLMs to generate instructions and instances for various Natural Language Processing (NLP) tasks, such as Summarization, Sentiment Analysis, question answering, etc. The generated instructional data can then be used to fine-tune the LLMs on the same or different tasks, improving their capabilities and controllability.

LoRA was proposed by a team of researchers from Microsoft Research in a paper titled “LoRA: Self-Supervised Generation of Instructional Data for Large Language Models” . The paper introduces a framework for Self-Supervised generation of instructional data using LLMs, such as GPT-4. The paper also demonstrates that LoRA can enable LLMs to learn new tasks and skills by generating instructions and instances for them.

LoRA generates instructions and instances by using a two-step process:

  • First, LoRA prompts the LLM to generate instructions for various NLP tasks, such as summarization, sentiment analysis, question answering, etc. The instructions are natural language descriptions of what the LLM should do given an input text. For example, an instruction for summarization could be “Write a summary of the following article in three sentences”. LoRA uses a set of templates and heuristics to guide the LLM to generate diverse and valid instructions.
  • Second, LoRA prompts the LLM to generate instances for each instruction, which are pairs of input texts and desired outputs. The input texts are sampled from a large corpus of text, such as Wikipedia or Common Crawl. The desired outputs are generated by the LLM itself, following the instruction. For example, an instance for summarization could be:

Input: The 2023 FIFA World Cup is scheduled to be the 23rd edition of the FIFA World Cup, the quadrennial international men’s association football championship contested by the national teams of the member associations of FIFA. It is scheduled to take place in Qatar from 21 November to 18 December 2023. This will be the first World Cup ever to be held in the Arab world and the first in a Muslim-majority country. This will be the second World Cup held entirely in Asia after the 2002 tournament in South Korea and Japan. In addition, the tournament will be the last to involve 32 teams, with an increase to 48 teams scheduled for the 2026 tournament in Canada, Mexico and United States.

Output: The 2023 FIFA World Cup is a global football tournament that will take place in Qatar from November to December 2023. It will be the first World Cup in the Arab world and a Muslim-majority country, and the second in Asia. The tournament will feature 32 teams for the last time before expanding to 48 teams in 2026.

LoRA ensures the quality of generated instructions and instances by using several techniques, such as:

  • Filtering out invalid or duplicate instructions based on syntactic and semantic criteria, such as length, readability, specificity, and uniqueness.
  • Evaluating the quality of generated instances based on metrics such as fluency, coherence, relevance, and accuracy.
  • Comparing the generated instances with human-written outputs from existing NLP datasets or expert annotations, and selecting the ones that have high similarity or agreement.
  • Applying post-processing steps such as spelling correction, punctuation normalization, and capitalization to improve the readability and consistency of the generated instances.

QLoRA

Quantized Low Rank Adapters (QLoRA) is a method for efficient fine-tuning of quantized large language models (LLMs) that reduces memory usage and preserves performance. QLoRA uses a combination of low-rank matrix factorization and 4-bit quantization to compress the weights of the LLM and the adapters. Adapters are small neural networks that are added to each layer of the LLM and are trained on a specific task, while the LLM itself is frozen. QLoRA enables fine-tuning LLMs with billions of parameters on a single GPU, which would otherwise require hundreds of GBs of memory. QLoRA also introduces several innovations to save memory and improve speed, such as NormalFloat, double quantization, and paged optimizers. QLoRA has been applied to fine-tune LLMs such as LLaMA and T5 on various tasks, such as instruction following and chatbot generation, and has achieved state-of-the-art results.

ULMFiT

Universal Language Model Fine-tuning (ULMFiT) is a method for fine-tuning a pre-trained language model for a specific downstream task, such as text classification, sentiment analysis, or hate speech detection. ULMFiT can be applied to any task in natural language processing (NLP). ULMFiT has three main steps:

  • General language model pre-training: This step involves training a language model on a large and diverse corpus of text, such as Wikipedia, to learn general linguistic features and patterns. ULMFiT uses a 3-layer AWD-LSTM architecture for its language model.
  • Target language model fine-tuning: This step involves fine-tuning the pre-trained language model on the text data of the target task, such as movie reviews or tweets, to adapt it to the specific domain and vocabulary. ULMFiT introduces several techniques to improve fine-tuning, such as discriminative fine-tuning, which adjusts the learning rate for each layer according to its importance; slanted triangular learning rates, which increases and then decreases the learning rate during training; and gradual unfreezing, which unfreezes and trains one layer at a time from the top to the bottom.
  • Target classifier fine-tuning: This step involves adding a classifier layer on top of the fine-tuned language model and training it on the labeled data of the target task, such as positive or negative sentiment. ULMFiT uses the same techniques as in the previous step to fine-tune the classifier.


ULMFiT has achieved state-of-the-art results on six text classification tasks, reducing the error by 18-24% on most datasets. It has also shown that it can match the performance of training from scratch on 100 times more data with only 100 labeled examples.

Gradient-based

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