AI technologies are rapidly advancing, with GPT (Generative Pretrained Transformers) and other large language models (LLMs) leading the charge. Particularly, when it comes to finetuning these models for specific use cases like medical or legal applications. In this blog post, I'll guide you through the process of finetuning the Falcon 7B or 40B model using your own dataset, which can be a game-changer in AI applications. Let's dive in.
Fine-tuning is a method wherein you retrain an LLM using a large volume of proprietary data. It's useful for shaping the behavior of the model, making it speak or write in a certain way. For instance, if you want your model to mimic a specific persona, like how Jada AI can sound like Trump, you can fine-tune it using chat history or podcast transcripts.
However, if you need to integrate a significant amount of domain knowledge (such as legal cases or financial statistics), you may want to consider the knowledge base method. In this case, you're not retraining the model but creating an embedded database with all your knowledge, ideal for situations where you need accurate, real-time data.
In summary, the right method depends on your use case. If you need to cut costs while ensuring your AI behaves a certain way, finetuning is your go-to. If you need accurate, up-to-date data, opt for a knowledge base.
The first step is selecting a model for finetuning. Hugging Face has a leaderboard showcasing various LLMs. One standout model is Falcon. Known for its quick ascent to the leaderboard's top position, Falcon has proven to be a powerful LLM for commercial use. Moreover, it's not just limited to English but includes various languages like German, Spanish, and French.
Falcon comes in two versions: 40B and 7B. While the Falcon 40B is more powerful (akin to GPT-3), it's also slower. On the other hand, Falcon 7B is quicker and less costly to train, making it ideal for small to medium-scale projects.
Quality training data is crucial for the successful finetuning of your model. Datasets can be either public (obtained from platforms like Kaggle or Hugging Face's dataset library) or private (proprietary datasets unique to your use case). Notably, you don't need an enormous dataset; starting with as little as a hundred rows of data is manageable.
In fact, GPT can be a useful tool in creating a significant amount of training data. For instance, if you have a list of high-quality mid-journey prompts, you can utilize GPT to reverse engineer user instructions.
Platforms like Rasa and ChatGPT provide functionalities to generate such prompts in bulk. Just upload a CSV file with your prompts, and the AI will generate hundreds of user instructions corresponding to each prompt, automatically creating your training dataset.
Google Colab is a convenient platform for finetuning Falcon models, offering a GPU runtime type for faster processing. Before starting, ensure you have installed necessary libraries and obtained your Hugging Face API key.
Once you've downloaded your chosen Falcon model, you can use a specific method called low-rank adapters for efficient finetuning. Before finetuning, try the base model with your prompt to benchmark the results. For example, in the case of a 7B model, this might reveal that it doesn't generate high-quality mid-journey prompts.
After this initial test, proceed to upload your training dataset. Load the data and map it according to the human and assistant prompt format. Finally, initiate the training process. This step can take time depending on your data volume and processing power.
After finetuning the model, you'll need to save it. This can be done locally or by uploading the model to Hugging Face. Following this, you can run a few prompts with the finetuned model. Typically, the results are noticeably improved compared to the base model, demonstrating the effectiveness of finetuning.
Finetuning a Falcon 7B/40B model can significantly enhance the capabilities of your AI. While the process requires time and computing power, it's a worthwhile investment, given the quality of the results. If you're looking to further explore the power of AI, consider joining the ongoing contest by OpenAI, where the winner receives a significant amount of computing power for training.
Finetuning can apply to a range of use cases, from customer support to medical diagnosis or financial advisory services. It's a fantastic tool to refine your AI applications, and I'm excited to see the results you achieve with it. Stay tuned for my upcoming posts on creating an embedding knowledge base.
Remember, the AI revolution is here, and it's time to finetune your own LLM. Don't miss out on this opportunity! Happy finetuning!
1. Google colab