How does Fine Tuning work, what methods are available ?
Firstly, pre-trained language models have been a game changer in natural language processing (NLP) and have made it possible to achieve state-of-the-art results on a variety of language tasks with limited resources. However, pre-trained models may not always perform optimally on specific tasks or domains, which is where fine-tuning and transfer learning come into play.
There are several pre-trained LLMs (Large Language Models) available for use. Some examples include GPT-3, GPT-4, BERT, RoBERTa, Alpaca These models have been trained on large amounts of text data and can be used for tasks such as text generation, translation, and summarization.
Two Main Approaches
There are two main approaches to transfer learning in NLP: feature-based transfer learning and fine-tuning.
Feature Based Transfer Learning
Feature Based Transfer learning is a method in machine learning where a pre-trained model is used as a starting point for training a new model on a different but related task. The idea behind transfer learning is that the knowledge gained from one task can be reused and applied to another task, reducing the amount of training data and computational resources required to achieve good performance. Transfer learning is especially useful in deep learning, where training large models from scratch can be very expensive and time-consuming.
Transfer learning example: Feature extraction
In this example, we want to build an image classifier to distinguish between images of cats and dogs. Instead of creating a new model and training it from scratch, we use a pre-trained model called VGG16, which has already learned to identify thousands of object categories from a large dataset called ImageNet.
We remove the last few layers of VGG16 (which are responsible for actual object classification) and use the remaining layers as a “feature extractor” for our cat and dog images. These layers can transform the input images into a compact representation that captures the essential information. We then use this compact representation as input for training a simpler classifier, like a Support Vector Machine (SVM) or logistic regression.
This approach leverages the knowledge VGG16 has already learned from the ImageNet dataset to speed up our training process and achieve better performance with less data.
How might an Enterprise use a pre-trained LLM Model in conjunction with feature based transfer learning ?
- Image and video recognition: Enterprises can use pre-trained models, such as VGG16, ResNet50, or MobileNet, to extract features from images and videos. They can then fine-tune the pre-trained models on their specific tasks, such as detecting defects in manufacturing products or identifying security threats in surveillance videos.
- Natural language processing: Enterprises can use pre-trained models, such as BERT, GPT-2, or RoBERTa, to extract features from text data. They can then fine-tune the pre-trained models on their specific tasks, such as sentiment analysis, question-answering, or document classification.
- Recommendation systems: Enterprises can use pre-trained models, such as deep autoencoders or matrix factorization, to learn latent representations of user preferences and item features. They can then fine-tune the pre-trained models on their specific recommendation tasks, such as product recommendations or personalized content recommendations.
- Speech recognition: Enterprises can use pre-trained models, such as DeepSpeech or Kaldi, to extract features from audio data. They can then fine-tune the pre-trained models on their specific speech recognition tasks, such as voice assistants or call center transcriptions.
- Anomaly detection: Enterprises can use pre-trained models, such as autoencoders or GANs, to learn the normal patterns of their data. They can then fine-tune the pre-trained models on their specific anomaly detection tasks, such as fraud detection or predictive maintenance.
Fine-tuning involves taking the pre-trained model and training it on a new task or domain with additional data.
Fine-tuning is a form of transfer learning, but they are not exactly the same thing. Transfer learning is a more general concept, while fine-tuning is a specific technique used within transfer learning. Fine-tuning involves taking a pre-trained model and further training it on a specific task or domain.
Fine-tuning is often necessary because pre-trained models are trained on general language understanding, and the fine-tuning process adapts the model to a specific task or domain. Fine-tuning involves training the model on task-specific data, where the model is initialized with the pre-trained weights, and the weights are updated during the fine-tuning process.
Fine-tuning involves adjusting various hyperparameters, such as learning rate, batch size, and number of training epochs, to optimize the model’s performance on the specific task. Fine-tuning can be done on a single task or on multiple tasks, where the model is trained on multiple tasks sequentially or simultaneously.
Fine-tuning example: Fine-tuning BERT for text classification
In this example, we want to build a sentiment analysis model for movie reviews. Instead of creating a new language model and training it from scratch, we use a pre-trained model called BERT, which has already learned the structure and nuances of the English language from a large text dataset.
We add a classification layer on top of the pre-trained BERT model and then fine-tune the entire model on our dataset of movie reviews. Fine-tuning means we continue the training process for a few more epochs with a smaller learning rate. This allows the BERT model to adjust its weights slightly to better understand the specific task of sentiment analysis while preserving the knowledge it has learned from the larger text dataset.
This approach leverages the knowledge BERT has already learned from the large text dataset to achieve better performance on our sentiment analysis task with less data and training time.
P-tuning is an NLP technique that fine-tunes pre-trained language models by selectively keeping task-relevant weights and updating others. It uses a projection matrix parameter to control the impact of each pre-trained weight during fine-tuning. This approach improves the efficiency and effectiveness of fine-tuning, leading to enhanced performance on various NLP tasks.
How might An Enterprise use a pre-trained model in conjunction with fine-tuning?
An enterprise can use fine-tuned large language models like BERT for various natural language processing (NLP) tasks to improve their products, services, or internal processes. Some use cases for an enterprise might include: Sentiment analysis: Customer support, Text classification, Content summarization, Information extraction, Content generation.
Instruct Fine-Tuning: A Powerful Tool for Customization
Instruct fine-tuning is a relatively new technique in the field of NLP that is revolutionizing how we customize pre-trained models. It’s a strategy that allows you to guide a model’s learning process by providing specific instructions during fine-tuning. This means you can train the model to perform a specific task by giving it directives in the data you feed it.
In traditional fine-tuning, a model is given labeled examples, which are used to update the model’s weights and biases to perform better on a given task. However, with instruct fine-tuning, instead of simply giving examples, you give the model instructions on what to do, thereby teaching it to follow prompts, perform tasks, or answer questions.
This method brings a few advantages:
- Increased flexibility: With instruct fine-tuning, you’re not just tweaking the model to perform better on a given task, but actively guiding its learning process. This means you can teach it to do a broader range of tasks.
- Improved performance: Initial research and applications have shown that instruct fine-tuning can lead to improvements in performance on a variety of tasks, including question answering, summarization, and translation.
- Greater control: By providing direct instructions, you can guide the model towards better behavior, potentially reducing biases and errors in the model’s responses.
Instruct Fine-Tuning Example: Guiding GPT-4
Let’s say you want to improve GPT-4’s performance in a customer service chatbot context, where it needs to provide specific responses to customer queries. In traditional fine-tuning, you would train GPT-4 on a dataset of example conversations between customers and customer service representatives.
With instruct fine-tuning, however, you would additionally include specific instructions with each example. For instance, you might provide an example where a customer is asking about the return policy and include an instruction like “provide a polite and succinct summary of the return policy”. This instructs the model not just on the correct content, but also the tone and level of detail expected in the response.
Harnessing Instruct Fine-Tuning in Enterprises
Enterprises can leverage instruct fine-tuning to enhance their use of pre-trained models in numerous ways:
- Customer service: As in the example above, instruct fine-tuning can be used to improve the performance of chatbots, making them more helpful, polite, and precise in their responses to customer queries.
- Content generation: Enterprises can use instruct fine-tuning to guide the generation of content by models, such as creating marketing copy or writing product descriptions that match a specific style or tone.
- Data analysis: Enterprises can use instruct fine-tuning to instruct models on how to analyze and summarize large datasets, providing specific instructions on what insights to look for or how to present the results.
- Risk management: Enterprises can use instruct fine-tuning to guide models in identifying potential risks or anomalies in data, instructing them on what signs to look for or how to prioritize different types of risks.
As with any application of machine learning, careful design, testing, and monitoring are key to success with instruct fine-tuning. In addition, instruct fine-tuning requires a clear understanding of the task at hand and the ability to articulate clear, effective instructions for the model. But with these tools, enterprises can unlock new levels of performance and flexibility from their pre-trained models.
Challenges with fine-tuning.
Fine-tuning and transfer learning present several challenges in NLP. One challenge is the selection of the pre-trained model and the choice of the fine-tuning or transfer learning approach. The selection of the pre-trained model can have a significant impact on the performance of the fine-tuned or transfer-learned model. The choice of the fine-tuning or transfer learning approach can also affect the performance of the model, and it may require careful tuning of hyperparameters.
Using pre-trained models has several advantages and disadvantages. Here are some of the pros and cons:
- Pre-trained models can save time and resources by allowing developers to start with a model that has already learned useful features from data. This can be especially useful when working with small datasets or when trying to solve complex problems.
- Pre-trained models can provide a good starting point for further fine-tuning and customization. This can help developers achieve better results more quickly than if they were starting from scratch.
- Pre-trained models can provide a level of performance that would be difficult to achieve with a model trained from scratch. This is because pre-trained models have been trained on large amounts of data and have learned to recognize many different patterns and relationships.
- Pre-trained models may not be suitable for all tasks or domains. For example, a pre-trained model trained on text data may not perform well on image data.
- Pre-trained models may require additional fine-tuning and customization to achieve the desired level of performance. This can require additional time and resources.
- Pre-trained models may not always be available for the specific task or domain that a developer is working on. In this case, the developer may need to train their own model from scratch.
To make the most of a fine-tuned large language model, an enterprise should consider the following steps:
- Identify the specific NLP tasks that would benefit from the LLM’s capabilities.
- Collect, clean, and label the necessary data for fine-tuning the LLM for the specific tasks.
- Fine-tune the pre-trained LLM using the collected data and evaluate its performance.
- Deploy the fine-tuned model in production environments, such as chatbots, document management systems, or analytics platforms, using tools like NVIDIA’s Triton Inference Server.
- Continuously monitor the model’s performance and update it with new data to ensure its accuracy and relevance to the tasks at hand.
Again, how can Dell help?
Ill re-iterate my closing statements from the previous post – weather its training your own LLM, using a pre-training model Dell is make AI simpler and more accessible.
Over the coming months reference guides and validated solution architectures will be released with guidance on modular and flexible architecture for each use case. Focusing on ease of deployment with pre validated hardware and software stacks (a lot more to come on this)
In our next post we’ll take a look at inferencing.
Check Out the Entire Generative AI 101 Blog Series:
- Generative AI 101: Series Introduction
- Generative AI 101 Part 1: Key Concepts
- Generative AI 101 Part 2: How are LLM’s Trained?
- Generative AI 101 Part 3: Pre-Trained Model Fine Tuning
- Generative AI 101 Part 4: Inferencing (Running your LLM)
- Generative AI 101 Part 5: Project Helix Dell and NVIDIA Solution Architecture