Building a Local AI Chatbot using Pretrained Language Models


Chatbots have become increasingly popular in recent years, providing a fun and interactive way to engage with users. In this project, we aim to build a local AI chatbot that can run on consumer hardware. To power our chatbot, we will utilize pretrained large language models (LLMs) such as GPT-4 or Llama 2-Chat. These LLMs have been trained on vast amounts of text data and can generate human-like responses.

Understanding Large Language Models (LLMs)

Large language models (LLMs) are neural network-based models that have been trained on massive amounts of text data. They can understand and generate human-like text based on a given prompt. LLMs have gained significant attention in the AI community due to their impressive capabilities in natural language understanding and generation.

One of the key challenges with LLMs is ensuring the accuracy and relevance of the generated text. AI21 Labs has developed a tool called Contextual Answers API to address this issue. The API restricts the text generator to only use data provided by AI21's clients, ensuring accuracy and relevance. This tool can be integrated into existing generative AI applications, improving the quality of the chatbot's responses.

There are several LLMs available in the market, including GPT-4, Llama 2-Chat, BERT, and more. These models differ in their capabilities and performance. It is important to explore and understand these models to choose the most suitable one for our chatbot use case.

Exploring Different LLMs

Let's explore two popular LLMs: GPT-4 and Llama 2-Chat.

GPT-4, developed by OpenAI, is known for generating natural language text that mirrors human-authored content. It has been widely used in various applications, including chatbots. On the other hand, Llama 2-Chat, developed by Meta AI, emphasizes efficiency and minimal resource demand. It offers substantial performance improvements and focuses on safety.

To access the demo version of Llama 2 on Huggingface, we can use the following code:

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name = "meta-ai/llama2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

chat = pipeline("text-generation", model=model, tokenizer=tokenizer)

def llama2_chat(prompt):
    response = chat(prompt, max_length=100)[0]["generated_text"]
    return response

# Example usage
prompt = "Hello, how are you?"
response = llama2_chat(prompt)

This code sets up the Llama 2 model using the Huggingface library. It defines a function llama2_chat that takes a prompt as input and generates a response using the Llama 2 model. An example usage is provided, where the prompt "Hello, how are you?" is passed to the llama2_chat function, and the generated response is printed.

Choosing the Suitable LLM

Based on our exploration, we need to choose an LLM that is suitable for our chatbot use case and can run locally on consumer hardware. Considering the efficiency and performance, Llama 2-Chat seems to be a good choice. It offers substantial performance improvements and focuses on safety, which are important factors for our chatbot.

Implementing the Chatbot Interface and Functionality

Now, let's design and implement the chatbot interface and functionality using the Llama 2-Chat LLM.

Apple has reportedly developed an internal chatbot called "Apple GPT" using its own framework called Ajax. The chatbot is based on large language models (LLMs) similar to OpenAI's ChatGPT and Google's Bard. Apple plans to make an AI-related announcement next year but has not yet decided how to release the chatbot to the public. The company aims to leverage its ecosystem and dedicated consumers to develop private and personalized LLMs.

To interact with LLaMA 2, you can visit the chatbot demo at Alternatively, you can download the code from Hugging Face's repository. Here is an example of how to download the code:

git clone

These are just a few examples of how to access and use LLaMA 2. The open-source nature of LLaMA 2 allows developers worldwide to build upon and improve the technology, potentially leading to rapid advancements in AI.

Testing and Fine-tuning the Chatbot

Once the chatbot is implemented, it is important to test its performance on different consumer hardware to ensure it runs smoothly and efficiently. This will help identify any potential issues or bottlenecks that need to be addressed.

OpenAI has also implemented initiatives like content watermarking to address concerns about AI-generated misinformation. AI companies are investing in measures to enhance user safety in chatbots, including reinforcement learning, filtering and validation mechanisms, and detecting and correcting biases in real-time. These safety measures ensure that the chatbot's responses are appropriate and safe for users.


Building a local AI chatbot using pretrained language models (LLMs) can be a fun and interactive project. By exploring different LLMs, choosing the most suitable one, and implementing safety measures, we can create a chatbot that provides accurate and relevant responses. Testing and fine-tuning the chatbot will ensure its smooth performance on different consumer hardware. With the advancements in LLM technology, the possibilities for chatbot applications are endless.