The text discusses the feasibility of building a local chatbot using Llama2, LangChain, and Streamlit on a CPU. The author carries out a case study to test the performance of the chatbot and evaluates its limitations. The conclusion is that while it is possible to build a chatbot on a CPU, the limited tokens, long run time, and high memory usage make it unfeasible for practical use.
Can a Llama 2-Powered Chatbot Be Trained on a CPU?
Building a local chatbot with Llama2, LangChain, and Streamlit on a CPU
Introduction
Local models have become popular among businesses looking to build their own custom AI applications. These models allow developers to create solutions that can run offline and meet privacy and security requirements.
Previously, local models were large and mainly used by enterprises with the resources to train models on large amounts of data using GPUs.
Now, smaller local models are available, raising the question: Can individuals with basic CPUs use these tools and technologies?
In this article, we explore the possibility of building a personal, local chatbot using Meta’s Llama2 on a CPU and evaluate its performance as a reliable tool for individuals.
Case Study
To test the feasibility of building a local chatbot that can run offline on a personal computer, let’s conduct a case study.
The objective is to build a chatbot using a quantized version of Meta’s Llama2 model. The model will be used to create a LangChain application that generates responses to user queries.
The chatbot will be trained on two PDF documents related to computer vision in sports.
For context, the chatbot will be trained on a computer with Windows 10, an Intel i7 processor, and 8GB of RAM.
Step 1 – Create a Vector Store
The first step is to create a vector store, which stores the embedded data from the documents and allows for retrieval of relevant documents.
The PDF documents are loaded and split into chunks of 500 characters. These chunks are then converted into embeddings using a sentence transformer from HuggingFace. The vector store is created using the Facebook AI Similarity Search (FAISS).
Step 2 – Creating the QA Chain
The next step is to load the retrieval QA chain, which retrieves relevant documents from the vector store and uses them to answer user queries.
The QA chain requires the quantized Llama2 model, the FAISS vector store, and a prompt template. The model is downloaded from the HuggingFace repository and loaded using CTransformers. The vector store and prompt template are also loaded.
Step 3 – Creating the User Interface
With the core elements built, we can now create a user interface for the chatbot using the Streamlit library. The user interface incorporates the previously built functions.
Evaluating the Chatbot
The chatbot is evaluated by asking it three different questions related to computer vision in sports. The responses are satisfactory, but there are limitations in terms of the number of tokens and the response time.
The Final Verdict
While it is possible to build a Llama2-powered chatbot on a CPU, the limitations in terms of tokens and response time make it unfeasible for practical use. However, with more powerful CPUs and advancements in AI technology, it may become more viable in the future.
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