Create a Chat GPT like large language model using Python
What is a large language model?
A large language model is a machine learning model trained on vast amounts of data. Currently Chat GPT is the most popular model. In this tutorial, you will learn how to create a large language model that predicts the next character in a sequence using a predictive database with Python. We'll be utilizing the Fixess client library to interact with the predictive database effectively.
Prerequisites
Visit https://fixess.ai/quick-start to get started.
You will need:
- A free API key from https://rapidapi.com/fixessgithub/api/fixess/details
- Basic knowledge of Python
- Python 3.x installed on your system
- The Fixess client library installed (`pip install fixess`)
- A text corpus file accessible on your system
Step 1: Import Fixess
First, we'll import the necessary module from the Fixess library.
```python
from fixess import Fixess
```
Step 2: Create a Fixess Client Dictionary
Next, instantiate the Fixess client with your API key from https://rapidapi.com/fixessgithub/api/fixess/details .
```python
fixess_client = Fixess(api_key=YOUR_API_KEY)
```
Step 3: Read in a Corpus File Line by Line
Load your corpus text file. Ensure the file is encoded correctly and easily accessible from your script.
```python
corpus_filepath = 'path/to/your/corpus.txt' # Replace with your corpus file path
with open(corpus_filepath, 'r', encoding='utf-8') as file:
corpus_lines = file.readlines()
```
Step 4: Create Guess the Next Character Entry
For each line in the corpus, we'll use the Fixess client to predict the next character. This process involves extracting character sequences and encoding them as needed for the predictive model.
```python
for line in corpus_lines:
# For each character in the processed line, index every n characters to predict the next one
n = 100 # Number of characters to use for prediction; adjust as per the model's requirements
for i in range(len(line) - n):
sequence = line[i:i+n]
next_char =line[i+n]
# Create an entry in the fixess_client dictionary to guess the next character
# This is a generalized representation; use your actual Fixess client's methods to add the data
fixess_client[sequence] = next_char
```
Conclusion
With the entries added to your predictive database, your large language model can now be trained to predict the next character in a sequence. This model can serve as a foundation for more complex natural language processing tasks using character-level encoding. Next learn how to create a Chat GPT like chat bot using your newly created large language model: https://fixess.ai/llm-chatbot. Send us a message if you run into any issues. We are excited to see what you will build.