Is Falcon LLM the OpenAI Alternative? An Experimental Setup with LangChain
Is Falcon LLM the OpenAI Alternative?
Introduction
If you're serious about learning how to work with large language models, it's essential to be able to work with different models. While OpenAI models paired with frameworks like LangChain are the current golden standard, there are some downsides. The API costs money and you may not want to share sensitive or private information with OpenAI. In this post, we'll explore the alternative of using open source large language models, specifically the Falcon model, which has been outperforming other open source models on the leaderboards from Hugging Face.
An Experimental Setup with LangChain
To demonstrate how to work with open source models, we'll run an example using the Falcon model with 7 billion parameters. This version is more manageable compared to the fully trained model with 40 billion parameters. We'll compare Falcon to the Text DaVinci 3 model from OpenAI and test them in a task summarization task. Additionally, we'll learn how to use open source models from Hugging Face, set them up, and compare them to OpenAI models. As a bonus, we'll also learn how to summarize large text using LangChain and its summarization methods.
Getting Started
In order to follow along with this example, you'll need to access the repository provided in the description. You'll also need a Python installation and basic understanding of LangChain. If you're unfamiliar with LangChain, I recommend watching my previous video on working with LangChain to familiarize yourself with the basics.
Setting Up
Once you have the repository cloned and open in your preferred IDE, be sure to install the required dependencies listed in the requirements.txt file. You'll also need to set up your Hugging Face API token by creating an account, generating a token, and adding it to the .env file provided in the repository.
Loading the Model
To load the model, we first need to load the environment variable containing the Hugging Face API token. Then, we can use the Hugging Face hub from LangChain to interact with the model. The specific model we'll be using can be found on the Hugging Face website, and we'll copy the repository ID to interact with it. We'll create a LangChain object with the repository ID and any desired model parameters.
Creating Prompts and Running the Chain
Now that the model is set up, we can create a prompt template and a large language model chain. We'll use this chain to interact with the model and get responses. We'll start with a simple question, "How do I make a sandwich?" and see how the Falcon model responds. We'll get the response, wrap it for readability, and print it.
Comparing Falcon and Open AI
Next, we'll take our experiment a step further and set up a task summarization task. We'll download a transcript from a YouTube video, split it into documents, and use summarization methods in LangChain to compare the Falcon and OpenAI models. We'll load the models, create a summarization chain, and run it on the document splits. Finally, we'll print the output summaries and compare the results.
Results and Conclusion
Based on the experiment, the OpenAI model still outperforms the Falcon model in terms of text summarization. However, the capabilities of open source models continue to improve, narrowing the gap between them and commercial models like OpenAI. Knowing how to work with a variety of models, both paid and open source, will be an important skill in the future. It's exciting to see the potential of these open source models and their applications in AI development.
FAQs
1. How much does the OpenAI API cost?
The OpenAI API has a cost associated with it. You can find detailed pricing information on the OpenAI website.
2. Can I use the Falcon model for commercial applications?
Yes, the Falcon model is open source and can be used for commercial applications. However, it's important to familiarize yourself with the specific licensing requirements for the model.
3. Are there limitations to text summarization with LangChain?
Yes, text summarization can be challenging due to limitations in the number of tokens that can be sent to the API. Splitting the text into chunks can result in some loss of context and potentially lead to inaccurate summaries.
4. How can I run the Falcon model with 14 billion parameters?
The fully trained Falcon model with 40 billion parameters may not be practical to run due to resource constraints. It's recommended to experiment with the 7 billion parameter model and optimize it for your specific use case.
5. Can I train my own language model using LangChain?
No, LangChain is a framework for working with pre-trained language models. It does not provide functionality for training new models. However, you can fine-tune existing models using LangChain.
To sum up, learning to work with different models, including both open source and commercial options, is an essential skill for working with large language models. The Falcon model shows promise as an alternative to OpenAI, but it still lags behind in certain tasks. As technology progresses, the gap between commercial and open source models may narrow, making open source models a viable choice for various applications.
