Building Your Mini-ChatGPT at Home

ChatGPT is an exciting tool that many enjoy using. If you’re interested in having your own private copy, you might think it’s impossible to run a full version of ChatGPT due to its complexity and resource demands. However, you can create a simplified model that operates on standard hardware. In this tutorial, you will learn about:

Language models that can function like ChatGPT.
How to build a chatbot using advanced language models.

Table of Contents

Overview

This article is divided into three sections:

Understanding Instruction-Following Models
Finding Instruction-Following Models
Building a Simple Chatbot

Understanding Instruction-Following Models

Language models are machine learning models capable of predicting the probability of a word based on its preceding context. When users request the next word and feed it back to the model iteratively, the model effectively generates text.

While basic text generation models can assist in completing sentences, instruction-following models are specially fine-tuned versions that are adept at engaging in dialogue and following specific instructions. This means they operate like a conversation between two individuals, responding appropriately when one finishes speaking.

Although a text generation model can be used to develop a chatbot, an instruction-following model typically yields better quality responses, fine-tuned for such interactions.

Finding Instruction-Following Models

There are many instruction-following models available today, but for building a chatbot, it’s important to choose one that is user-friendly.

A great resource for discovering these models is Hugging Face, where models are designed to work with the Transformers library. This library standardizes the functionality of various models, making it easier to implement without dealing with the intricacies of each model individually.

Models that are instruction-following often feature the keyword “instruct” in their names. Searching with this keyword on Hugging Face yields a vast array of models, but not all will suit your needs. Review each model’s card to understand its capabilities and choose the best one for your purpose.

Key criteria for selecting your model include:

Training Data: What language is the model trained in? For example, a model trained on English text from novels may not be appropriate for a German chatbot focused on physics.
Deep Learning Library: Most Hugging Face models are built using TensorFlow, PyTorch, or Flax. Ensure that your system has the required library installed.
Resource Requirements: The model size matters. Many large models require a GPU, and some may need high-end GPUs or multiple units. Verify that your available resources can support the model.

Building a Simple Chatbot

Let’s create a simple chatbot that runs in the command line, accepting one line of text from the user and responding with a generated line from the language model of your choice.

For this example, we will use the falcon-7b-instruct model, which consists of 7 billion parameters. This model should ideally run on modern GPUs like the Nvidia RTX 3000 series, or leverage GPU resources available on cloud platforms like Google Colab or AWS EC2 instances.

The basic structure of the chatbot in Python would look like this:

while True:
    user_input = input("> ")
    # Print response logic here

The input("> ") function captures a line of text from the user, and the program waits for your input.

The next step is to acquire a response from the model. Language models require input in the form of token IDs (integers) and respond with a sequence of token IDs. You will need to convert between token sequences and text strings. Each model’s token IDs correspond to specific words, so they differ from one model to another.

The Hugging Face Transformers library simplifies these processes. Setting up a pipeline for the tiiuae/falcon-7b-instruct model allows you to specify parameters effectively:

from transformers import AutoTokenizer, pipeline
import torch

model = "tiiuae/falcon-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
)

When creating the pipeline, ensure the task is set to “text-generation,” as suggested in the model card.

To utilize the pipeline, specify additional parameters for text generation. The model generates tokens based on the probabilities it calculates, and instead of always picking the token with the highest probability, it samples from the distribution to introduce variation.

Here’s how to interact with the model using the pipeline:

newline_token = tokenizer.encode("\n")[0]  # 193
sequences = pipeline(
    prompt,
    max_length=500,
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    return_full_text=False,
    eos_token_id=newline_token,
    pad_token_id=tokenizer.eos_token_id,
)

You pass your input as a prompt, generate the responses, and display them to the user:

print(sequences[0]["generated_text"])

Remember, a language model doesn’t retain memory of previous interactions, so each input must include the conversational history for context.

You can start with a context setup that provides a persona for the chatbot. For example:

dialog = ["Bob is a Physics professor."]

Then, run the chatbot loop to capture user input and append the generated response.

Conclusion

This tutorial demonstrated how to create a simple chatbot using a large language model from the Hugging Face library. Specifically, you learned about:

The concept of instruction-following models that facilitate conversation.
How to find suitable models on Hugging Face.
Building a chatbot that engages in dialogue based on user input.