Fine Tune vs Incontext Learning: A Comprehensive Guide

Fine Tuning is a type of machine learning that involves adjusting the weights of a pre-trained model to fit a specific task. This process can be done in a few hours to a few days, depending on the complexity of the task and the size of the model.

In the article, we'll dive into the details of Fine Tuning and In-Context Learning, two techniques that are often used together to achieve state-of-the-art results. In-Context Learning, on the other hand, involves training a model on a specific task or dataset, with the goal of improving its performance on that task.

Fine Tuning is particularly useful for tasks that require a high degree of domain-specific knowledge, such as medical diagnosis or financial forecasting. In these cases, Fine Tuning can be used to adapt a pre-trained model to the specific requirements of the task.

By adjusting the weights of a pre-trained model, Fine Tuning can be a quick and efficient way to improve performance on a specific task.

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There are two main types of learning in LLMs: in-context learning and fine-tuning. In-context learning relies on carefully designed prompts to guide the model's behavior, while fine-tuning modifies the model's parameters through additional training.

The key difference between the two is that in-context learning doesn't require altering the model's parameters or training it on a specific dataset, whereas fine-tuning does. This makes in-context learning more flexible and suitable for prototyping.

Here's a summary of the two types of learning:

Fine-tuning typically requires a deeper understanding of machine learning and the specific problem domain, but modern software tools have made it more accessible to people without technical backgrounds.

Supervised learning for language models involves further training a pre-trained model to generate text conditioned on a provided prompt. This process is called supervised fine-tuning, and it's a key technique in building powerful applications with language models.

Pre-trained language models are widely available and free to use, even commercially, making fine-tuning a cost-effective option. Fine-tuning an LLM is cheap, with costs ranging from several hundred dollars to less than a thousand dollars.

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To fine-tune a language model, you need to transform your data into a format suited for supervised fine-tuning. This typically involves creating prompt-response pairs, where the prompt is a question or instruction and the response is the answer or output.

Here are the key steps in preparing data for supervised fine-tuning:

The resulting prompts should be in the Alpaca format, which was used to fine-tune the original LlaMA model from Meta to result in the Alpaca model.

Supervised fine-tuning is a powerful technique for building language models that can perform specific tasks, such as instruction following. By fine-tuning a pre-trained model on a dataset with prompt-response pairs, you can create a model that can generate accurate and relevant text in response to a given prompt.

One common technique used in supervised fine-tuning is masked language modeling. This involves presenting the model with sentences where certain words are intentionally masked or missing, and then giving it the correct answer to analyze how far off it was.

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Here's an example of how masked language modeling works:

This process helps the model understand how words relate to one another and how they fit within the bigger picture of a sentence.

Gradient-based optimization is another key technique used in supervised fine-tuning. This involves calculating the difference between the model's predictions and actual outcomes, and then using this information to iteratively fine-tune the model's parameters.

Here's an example of how gradient-based optimization works:

This minimizes prediction errors and enhances the LLM's task-specific expertise.

Unsupervised learning is like immersing the model in a vast sea of text data without any right or wrong answers for guidance.

It's a type of learning where the model learns through sheer exposure and context, absorbing the intricacies of language over time, without explicit instructions. This process can be likened to throwing someone into a language immersion program where they learn through exposure and context.

In unsupervised learning, the model is essentially learning to navigate and understand the patterns and relationships within the data on its own. This can be a highly effective way to learn, as it allows the model to discover new insights and connections that may not have been apparent with explicit guidance.

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Pre-training is a foundational step in the LLM training process, where the model gains a general understanding of language by exposure to vast amounts of text data. This phase involves feeding the model an extensive dataset containing diverse texts from books, articles, websites, and more.

Think of it as the model's introduction to the world of words, phrases, and ideas. During this phase, the LLM learns grammar rules, linguistic patterns, factual information, and reasoning abilities. For example, GPT-3 ingested around 570 GB of text data during pre-training, which is equivalent to reading hundreds of thousands of books in multiple languages.

Pre-training is expensive, costing several hundred thousand dollars in compute, but it's a crucial step in preparing the model for fine-tuning.

Masked language modeling is a technique used to provide the model with a learning structure by presenting it with sentences where certain words are intentionally masked or missing. This helps the model understand how words relate to one another and how they fit within the bigger picture of a sentence.

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The model is essentially acting as a language detective, trying to decipher the sentence and deduce what the missing words could be based on the context. It's given the correct answer and analyzes how far off it was to improve its ability to predict.

This process is essential in helping the model gain a deeper understanding of language, which is a crucial aspect of pre-training.

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Pre-training is a foundational step in the LLM training process, where the model gains a general understanding of language by exposure to vast amounts of text data.

This process is like reading hundreds of thousands of books in multiple languages, giving the model a rich tapestry of language to draw from. GPT-3, for example, ingested around 570 GB of text data during pre-training.

The model learns grammar rules, linguistic patterns, factual information, and reasoning abilities during this phase. This is an essential step, as it allows the model to understand the world of words, phrases, and ideas.

Pre-training is an expensive process, requiring several hundred thousand dollars in compute. However, it's a crucial step that enables the model to perform well in subsequent fine-tuning stages.

High-quality, pre-trained LLMs are widely available and free to use, making it easier to build powerful applications by fine-tuning them on relevant tasks.

Transfer learning is a technique where a model developed for a task is adapted for a second related task, saving time and resources needed to train a model from scratch.

Pre-trained models have already learned features from large datasets, which can be leveraged for a new task with a smaller dataset, making it especially useful when acquiring labeled data is challenging or costly.

Fine-tuning involves adjusting the deeper layers of the model while keeping the initial layers fixed, with the initial layers capturing generic features and the deeper layers capturing more task-specific patterns.

By leveraging pre-trained models and fine-tuning, we can build powerful applications for various tasks, as seen in the case of large language models (LLMs) that are fine-tuned on relevant tasks after pretraining.

SFT is actually quite simple, it's the first training step within the alignment process for LLMs, where you curate a dataset of high-quality LLM outputs.

This dataset is essentially a collection of examples that the LLM behaves correctly, which you can see below.

Then, you directly fine-tune the model over these examples, and the model learns to replicate the style of these examples during fine-tuning.

Interestingly, SFT is not much different from language model pretraining, both use next token prediction as their underlying training objective.

During pretraining, you use a massive corpus of raw textual data, whereas SFT uses a supervised dataset of high-quality LLM outputs.

During each training iteration, you sample several examples, then fine-tune the model on this data using a next token prediction objective.

Typically, the next token prediction objective is only applied to the portion of each example that corresponds to the LLM’s output.

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Transfer learning is a technique that saves time and resources by adapting a model developed for one task to another related task. This approach is especially useful when acquiring labeled data is challenging or costly, as pre-trained models have already learned features from large datasets.

Pre-trained models can be used as the starting point for new tasks, such as computer vision and natural language processing, due to the extensive computational resources and time required to train models from scratch. These models have already learned generic features like edges or textures, which can be leveraged for new tasks with smaller datasets.

Fine-tuning is a common approach in transfer learning, where the deeper layers of the model are adjusted while keeping the initial layers fixed. This is because the initial layers capture generic features, while the deeper layers capture more task-specific patterns.

Here are some key benefits of transfer learning:

The extent to which layers are fine-tuned can vary based on the similarity between the new task and the original task. This approach has been successfully applied in various domains, including language models and computer vision.

In-context learning is a flexible and efficient way to guide a model's behavior without modifying its parameters or training it on a specific dataset. This method relies on carefully designed prompts to condition the model's output, making it suitable for prototyping and tasks that require adaptability.

Unlike fine-tuning, in-context learning doesn't require additional computational resources beyond those needed for running the inference. This is especially useful for tasks that need to be completed quickly, such as responding to customer inquiries or providing financial advice.

In-context learning can be done on the fly for various tasks without requiring a retraining process, making it ideal for applications like dialogue systems. For instance, Capital One's virtual assistant, Eno, utilizes in-context learning to engage in natural customer conversations.

Here are the key differences between in-context learning and fine-tuning:

In-context learning is a powerful tool for creating conversational interfaces that can engage with users in a natural and context-dependent way.

In-context learning is a method of guiding a language model's behavior based on the specific context given to it during prompt formulation. It's like providing a virtual assistant with a set of instructions within the interaction itself to influence its responses.

Unlike fine-tuning, in-context learning doesn't require altering the model's parameters or training the model on a specific dataset. Instead, you provide the model with a prompt or set of instructions within the interaction to condition its output.

In-context learning relies on carefully designed prompts to guide the model, while fine-tuning modifies the model's parameters through additional training. To change output with in-context learning, the single prompt must be modified, whereas fine-tuning requires adding, editing, or removing training examples from the dataset.

In-context learning is more flexible and can be done on the fly for various tasks without requiring a retraining process. Fine-tuning, however, specializes the model for specific tasks at the cost of this flexibility.

Here are some key differences between in-context learning and fine-tuning:

In-context learning doesn't require additional computational resources beyond those needed for running the inference, making it a great option for prototyping. Fine-tuning, on the other hand, requires additional computational power and data to retrain the model, but the fine-tuned model typically requires fewer resources for inference.

Self-improvement is a key aspect of in-context learning, and one way to achieve it is through bootstrapping. Bootstrapping involves using existing knowledge and resources to improve oneself, without relying on external help or guidance.

This approach is exemplified by the story of Andrew Ng, who used online courses and tutorials to learn machine learning, eventually becoming a leading expert in the field.

By leveraging online resources, you can access a wealth of information and learn at your own pace. This self-directed approach to learning can be incredibly empowering.

For instance, the article highlights how Andrew Ng used online courses to learn machine learning, and how he later applied this knowledge to create his own MOOC (Massive Open Online Course) platform.

With bootstrapping, you can take ownership of your learning journey and make progress without relying on external validation or support.

As the article notes, Andrew Ng's self-improvement journey was marked by a willingness to learn from others and adapt to new information. This mindset is essential for in-context learning.

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In-context learning can be a powerful tool for meeting human needs, such as the need for autonomy, mastery, and purpose.

People have a fundamental need for autonomy, which is the desire to have control over one's own learning.

In-context learning allows learners to take ownership of their learning by applying what they've learned in real-world situations.

This can be especially beneficial for learners who are self-directed and motivated by a sense of purpose.

Learners who feel a sense of purpose are more likely to be engaged and motivated in their learning.

In-context learning can help learners develop a sense of purpose by connecting what they're learning to real-world problems or scenarios.

This can also help learners develop a sense of mastery by allowing them to apply what they've learned in a practical way.

By meeting human needs, in-context learning can lead to more effective and sustainable learning outcomes.

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LoRA and QLoRA are two parameter-efficient approaches for fine-tuning language models that have proven to be effective.

LoRA, or Low Rank Adaptation, fine-tunes two smaller matrices that approximate the weight matrix of the pre-trained large language model, resulting in a significant reduction in trainable parameters.

This fine-tuned adapter is then loaded to the pre-trained model and used for inference, achieving similar effectiveness to full fine-tuning in some cases.

QLoRA, an even more memory-efficient version of LoRA, loads the pre-trained model to GPU memory as quantized 4-bit weights, preserving similar effectiveness to LoRA.

The Hugging Face Parameter Efficient Fine-Tuning (PEFT) library implements LoRA, offering ease of use, and QLoRA can be leveraged by using bitsandbytes and PEFT together.

The SFTTrainer class in the TRL library provides a high-level abstraction for fine-tuning large language models, making it easier to perform QLoRA.

To perform QLoRA, you need to load the model to GPU memory in 4-bit, define the train and test splits of the prepped instruction following data, define training arguments, and pass these arguments into an instance of SFTTrainer.

The fine-tuning process can be performed using a compute cluster created using the latest Databricks Machine runtime with GPU support.

By using QLoRA with r=8 and targeting all linear layers, you can update 12,994,560 parameters and achieve relatively high-quality results by fine-tuning less than a 1% of the model’s weights with a total dataset of 5000 such prompt-description pairs.

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Fine-tuning and in-context learning are two powerful techniques for adapting language models to specific tasks. Fine-tuning involves adapting a pre-trained model to a new task by adjusting its weights, while in-context learning involves providing the model with specific context to perform a task.

Fine-tuning can be used for domain adaptation, where the source and target tasks are the same but the data distributions differ, allowing the model to adapt to new data. Data augmentation can also be used in conjunction with fine-tuning to improve performance, especially when labeled data is limited.

Some use cases for fine-tuned language models include support issue prioritization, fraud detection, blog writing, and text classification. These models can also be used for question answering and more.

Supervised fine-tuning (SFT) is a widely-used approach in AI research for fine-tuning language models.

SFT is simple and cheap to use, making it a popular tool within the open-source LLM research community.

Fine-tuning has led to significant breakthroughs in NLP, such as the development of Ferret, a Multimodal Large Language Model that can understand spatial references in images.

Ferret's advancement highlights the potential of fine-tuning pre-trained models like BERT to achieve specific tasks with high precision.

In computer vision, fine-tuning has also led to breakthroughs, such as the introduction of Improved Baselines with Visual Instruction Tuning.

This research emphasized the progress of large multimodal models (LMM) with visual instruction tuning, underscoring the importance of fine-tuning in adapting pre-trained models to specific tasks or datasets.

Fine-tuning has been instrumental in achieving state-of-the-art results on various image classification tasks using models like ResNet and VGG.

The cross-entropy loss function is a crucial component in fine-tuning, allowing models to learn from their mistakes and improve their performance over time.

By leveraging the power of fine-tuning, researchers and practitioners can adapt pre-trained models to new tasks and datasets, unlocking their full potential.

Before fine-tuning a model, it's essential to test its performance to establish a baseline for pre-trained model performance. This can be done by loading the model in 8-bit and prompting it with the format specified in the model card on Hugging Face.

The output obtained may not be satisfactory, with the first part of the result being acceptable but the rest being a rambling mess. This is expected when the model is prompted with the input text in the 'Alpaca format'.

The model performs as expected, predicting the next most probable token, but the goal of supervised fine-tuning is to generate the desired text in a controllable manner.

Domain adaptation is a scenario where a model trained on source data is adapted to perform well on target data, even if the data distributions differ. This can be achieved through fine-tuning.

Fine-tuning can be used to improve a model's performance on target data, especially when the available labeled data is limited. Data augmentation, which involves creating new training samples by applying transformations to the existing data, can also be combined with fine-tuning to further improve performance.

Data augmentation involves applying transformations such as rotations, scaling, and cropping to the existing data, creating new training samples. This can be particularly helpful when the available labeled data is limited.

Fine-tuned models have a wide range of applications, including support issue prioritization, fraud detection, and text classification. They can also be used for lead qualification, question answering, and more.

SFT is a highly effective technique for improving the quality of a language model, yielding a clear benefit in terms of instruction following capabilities, correctness, coherence, and overall performance.

To get the most out of SFT, you need a high-quality dataset that captures all relevant alignment criteria and characterizes the language model's expected output. Creating such a dataset can be difficult and expensive.

Recent research has explored automated frameworks for generating datasets for SFT, but there's no guarantee on the quality of data.

Using a large and high-quality dataset for SFT, like the 27,540 examples in the LLaMA-2 publication, can still benefit from further RLHF.

After SFT has been performed, the language model is capable of generating dialogue sessions of similar quality to those written by humans, making it less beneficial to create more data for SFT.

The optimal approach to alignment seems to be performing SFT over a moderately-sized dataset of examples with very high quality and investing remaining efforts into curating human preference data for fine-tuning via RLHF.

Task-specific data is essential for fine-tuning a language model, providing it with the domain expertise needed to excel in a particular task, like categorizing news articles.

Fine-tuning a language model can be a complex task, and it's essential to consider its limitations. One major challenge is ensuring compatibility between the pre-trained model and the new task, which can be a daunting task.

Overfitting is another significant concern, especially when fine-tuning on a small dataset. This can lead to a model that performs well on the training data but poorly on new, unseen data.

Fine-tuning can also result in knowledge degradation, where the model forgets some of the features and knowledge it acquired during its initial training. This phenomenon is often referred to as "catastrophic forgetting."

Fine-tuning can also exacerbate biases present in the pre-trained model, which can be particularly problematic in applications that require high sensitivity, such as facial recognition.

Here are some key limitations of fine-tuning:

Limitations of fine-tuning can be a challenge. Ensuring that the input and output formats, as well as the architectures and frameworks of the pre-trained model, align with the new task can be a difficult task.

Overfitting is a major concern when fine-tuning on a small dataset. This can lead to a reduced model's ability to generalize to new, unseen data.

Knowledge degradation is another issue that can arise from fine-tuning. This is where the model might forget some of the features and knowledge acquired during its initial training, a phenomenon often referred to as "catastrophic forgetting."

Pre-trained models can carry inherent biases that can be exacerbated when fine-tuned. These biases can be particularly problematic in applications that require high sensitivity, such as facial recognition.

Some of the key limitations of fine-tuning include:

SFT is a highly effective technique for improving the quality of a language model, yielding a clear benefit in terms of the model's instruction following capabilities, correctness, coherence, and overall performance.

However, curating a high-quality dataset for SFT can be difficult and requires careful manual inspection of data, which is not scalable and usually expensive.

The results of SFT are heavily dependent upon the dataset that we curate, which means that the quality of the dataset directly affects the performance of the model.

SFT can be computationally cheap, with some studies indicating that it's 100X less expensive than pretraining, making it an attractive option for many researchers and practitioners.

A recent study found that even after curating a high-quality dataset for SFT, further benefit can be gained by performing RLHF, which suggests that SFT alone may not be enough to achieve optimal results.

Here are some key points to consider when evaluating the pros and cons of SFT:

Overall, SFT is a valuable tool for improving the quality of language models, but it requires careful consideration of the dataset and potential limitations.

The size of the LoRA adapter is typically just a few megabytes, which is significantly smaller than the pretrained base model that can be several gigabytes in memory and on disk.

This size difference can lead to a slight increase in inference latency if the weights of the pre-trained LLM and the adapter aren’t merged. Fortunately, the PEFT library makes it easy to merge the weights with a single line of code.

Merging adapters is not a universal solution and can make the adapter pattern impossible to use. However, it allows for efficient inference by utilizing the pretrained model as a backbone for different tasks.

The decision to merge weights depends on the specific use case and acceptable inference latency. LoRA and QLoRA continue to be highly effective methods for parameter-efficient fine-tuning, widely used in the field.