The OpenAI GPT-2 model was proposed in Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever of OpenAI. The causal (unidirectional) transformer was trained using language modeling on a large corpus of around 40 GB of text data.
GPT-2 is a huge transformer-based language model with 1.5 billion parameters that was trained on an 8 million web page dataset. GPT-2 is trained with a straightforward goal: predict the next word given all of the preceding words in a text.
What is NanoGPT?
NanoGPT is a Small-Scale GPT for Text Generation. the NanoGPT explains the three easy and simple main components of the GPT models: generative, pre-trained, and transformers.
We’ll utilize Andrej Karpathy’s nanoGPT repository for quick and easy GPT training. He offers a detailed video lesson that explains how GPT-2 works and how to train such a neural network. However, we’re interested in fine-tuning the model with our own dataset and evaluating how it differs from the original (GPT-2 trained by OpenAI).
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Training GPT-2 To Generate Haiku: Purpose of NanoGPT
GPT-2 is trained with a straightforward goal: predict the next word given all of the preceding words in a text. Because of the dataset’s richness, this straightforward goal includes naturally occurring demonstrations of a wide range of tasks across multiple domains.
One of GPT-2’s most notable breakthroughs is pre-training on a vast corpus of online material. This pre-training provides the model with general linguistic knowledge, allowing it to comprehend grammar, syntax, and semantics across a range of topics. The model can then be fine-tuned for specific purposes
Haiku Dataset Preparation
There is a lot of effort being done to use deep learning for text generation, which is the process of creating new text that looks like human-written text. Andrej Karpathy famously showed that even simple models may generate compelling writing. However, they struggle when the text is required to adhere to severe structural limitations.
Rules for structuring a Haiku:
- It has three lines.
- It has five syllables in the first and third lines.
- It has a total of seven syllables in the second line.
- Its lines don’t rhyme.
- It includes a kireji, or cutting word.
- It includes a kigo, a seasonal reference.
Developing a whole new operating system from scratch creates one of considerable complexity for the base developers, who need to have immense computer science knowledge and primary-level programming skills over a wide area. This then results in areas of development that are left on the back burner because of the scarcity of developers with the needed specialized knowledge in these particular areas. The primary cause is that Haiku developers have to contend with the fact that the Haiku project is not just a kernel, as with Linux or any other OS, but is an integrated and interconnected kernel; windowing system; widget toolkit; desktop environment, and more.
Fine-tuning refers to taking a pre-trained network and applying an additional amount of training to make it do a modified (i.e. different) task.
The benefit of fine-tuning is that it requires less training than building the network from scratch. The hope is that the resulting network is adequately trained to perform the updated task.
In general, because the network was repurposed rather than trained specifically for this new task, one would expect it to be significantly less efficient. However, one has saved all of the effort required to train the original network from scratch. That can be rather substantial. Thus, the trade-off may be worthwhile.
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Data Preparation And Fine-Tuning
Data preparation for fine-tuning an LLM on instructions involves the following steps:
- Tokenizing text
- Converting outputs to PyTorch tensors
- Padding inputs to match the length
- Performing preprocessing
- Storing the dataset
Fine-tuning a model for a specific task can improve its performance and reduce the time and effort required to process large volumes of data. To fine-tune a model, you can start with a pre-trained model and adapt it to a specific task. This allows you to make the most of the available labeled data and achieve good results with less effort.
Here are some other steps you can take after fine-tuning a model:
- Hyper parameter tuning: Use this to fine-tune the model to prevent overfitting
- Cross-validation: Perform this to ensure the model performs well on the validation set
- Evaluation: Evaluate the model’s performance on a separate validation dataset to gain insights into its generalization capabilities
- Inference and evaluation: View your job results, which will automatically upload a result file named step_metrics.csv
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Limitation Of NanoGPT
However, they are not without restrictions. For starters, while these models can generate persuasive and logical language, they lack a thorough understanding of the content they create.
- They are incapable of reasoning or having true discoveries because they lack consciousness or awareness.
- They merely produce responses based on patterns discovered through extensive data analysis.
- Another disadvantage is that they can occasionally produce incorrect or nonsensical answers.
- Despite having been trained on a wide range of content, they may still struggle to recognize context or complexity, resulting in off-target results.
- They also have a tendency to repeat themselves or go off on tangents when not properly guided, which can be frustrating for users.
- Furthermore, these models cannot learn or adapt in real-time.
- Once trained, their knowledge is fixed, and they are unable to assimilate fresh information or experiences.
- As a result, in an ever-changing world, they might swiftly become obsolete or irrelevant.
- Overall, GPT-2 and GPT-3 are effective tools, but they do not immediately get replaced by human intelligence, thinking and creativity.
- They can be handy for creating writing or automating specific chores, but they still have a long way to go before they can genuinely understand and interact with the environment like people.
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Limit On Using the Openai API (GPT-2)
The OpenAI API, like GPT-2, has its own limits on how many requests it can handle and you can make, and how much data you can send or receive. If you hit these limits, your requests might get slowed down. To avoid this, you can try being more concise in your requests or look into higher subscription plans for more usage. Also, always use the API responsibly, following OpenAI’s rules. If you need more, you might have to check out other services or models.
Additionally, you could consider training your own model using open-source resources.
However, keep in mind that this can be a time-consuming and resource-intensive process.
Remember, it’s always best to use technology responsibly and ethically and to respect the terms of service set by the providers.
Not only is it one of the right things to do, but it can also help to ensure that you’re getting the most out of the tool.
Conclusions
In conclusion, while ChatGPT’s haiku composition abilities can be impressive at times, they are far from perfect. The AI often relies on cliches and predictable patterns, resulting in haikus that feel generic and uninspired. Additionally, the AI can struggle with syllable count and rhythm, resulting in haikus that feel forced or awkward. However, when ChatGPT generates a superb haiku, it may be a work of art, with evocative language and vivid imagery that express the essence of human experience. Finally, the limitations of ChatGPT’s haiku writing abilities serve as a reminder that, while AI can be amazing, it is nevertheless constrained by the data it is trained on and the rules that regulate its cognitive processes.
Some critics have also said that a big disadvantage could be a lack of creativity. Since its output depends on the information it has been trained on, GPT-3 may mimic human sentences, but it can lack the creativity of content created by humans. It can make the text eventually boring and monotonous.