ChatGPT is a very popular language model developed by OpenAI. It is widely used in a variety of natural language processing (NLP) tasks such as text generation, conversation generation, and text summarization.
One of the reasons for its popularity is that it was trained on a massive dataset of text, which allows it to generate high-quality, human-like text. It has also been fine-tuned for specific tasks, such as answering questions, which makes it well-suited for a wide range of applications. Another reason for its popularity is that the model is easily accessible through OpenAI’s API, which makes it easy to use in a variety of programming languages and frameworks. This has led to the development of a wide range of applications that utilize ChatGPT’s capabilities, such as chatbots, language translation tools, and text summarization tools.
The model also has been fine-tuned for various languages beside english, this makes it a versatile model for various use cases. In short, ChatGPT is a powerful and flexible language model that has become a popular choice for many NLP tasks and applications.
How Chat GPT works under the hood?
ChatGPT is a type of language model known as a transformer model. It uses a technique called unsupervised learning, where the model is trained on a large dataset of text, such as books or articles, without any specific labels or targets.
The model consists of an encoder and a decoder, both of which are made up of multiple layers of neural networks. The encoder takes in the input text and converts it into a fixed-length vector representation, which is then passed to the decoder. The decoder generates the output text based on this vector representation and the previous tokens generated by the model.
For example, I asked Chat GPT to write a Python script to search for a term on GitHub and this is how it built a script for me. Tested it on my system and it worked like a charm.
The model uses a technique called attention mechanism, which allows it to selectively focus on certain parts of the input when generating the output. This allows the model to better understand the context of the input and generate more accurate and coherent text.
The model is trained using a technique called maximum likelihood estimation, where the model is optimized to maximize the likelihood of the training data. This means that the model is trained to generate text that is similar to the text in the training dataset.
After training, ChatGPT can be fine-tuned on a smaller dataset with specific task in mind (such as answering questions or generating responses in a conversation) to fine-tune the model to the task at hand.
In summary, ChatGPT uses a transformer model architecture with attention mechanism, it’s trained on a large dataset of text using unsupervised learning and fine-tuned on specific task to improve its performance.
What makes Chat GPT a powerful language model?
ChatGPT is a powerful language model for several reasons:
- Large training dataset: ChatGPT is trained on a massive dataset of text, which allows it to understand a wide variety of language patterns and styles. This allows it to generate high-quality, human-like text that is coherent and grammatically correct.
- Attention mechanism: ChatGPT uses an attention mechanism, which allows it to selectively focus on certain parts of the input when generating text. This allows the model to better understand the context of the input and generate text that is more accurate and relevant.
- Fine-tuning: ChatGPT can be fine-tuned on a smaller dataset for specific tasks, such as answering questions or generating responses in a conversation. This allows the model to adapt to the specific task and improve its performance.
- Multilinguality: The model is fine-tuned for multiple languages, this makes it versatile and can be used in various use cases.
- OpenAI API: The model is easily accessible through OpenAI’s API, which makes it easy to use in a variety of programming languages and frameworks. This has led to the development of a wide range of applications that utilize ChatGPT’s capabilities.
- Large Scale: ChatGPT is a large model, with 175 Billion parameters, making it one of the largest models in the world. This makes it capable of handling more complex and nuanced tasks, as well as being able to generate more coherent and diverse responses.
All of these features combined make ChatGPT a very powerful language model that is well-suited for a wide range of natural language processing tasks.
Where is Chat GPT hosted?
ChatGPT is hosted by OpenAI and is accessible through their API. The API allows developers to send requests to the model and receive the generated text in response. The API is hosted on OpenAI’s servers, which are located in different geographic regions to ensure low latency and high availability.
Additionally, OpenAI offers a GPT-3 playground where users can test the model and generate text through the browser, which is accessible from the OpenAI website.
In summary, ChatGPT is hosted by OpenAI and is accessible through their API, it is hosted on OpenAI’s servers and can be accessed from any platform that supports the API.
In order to test drive how OpenAI actually works, I forked the Pet name generator app repository and tried to create my own version of Dockerized solution. First, I tried to bring up the app without using Docker just to ensure that it works on my local machine.
1. Clone the repository
git clone https://github.com/ajeetraina/openai-quickstart-node
2. Navigate into the project directory
3. Install the requirements
4. Make a copy of the example environment variables file
cp .env.example .env
Add your API key to the newly created .env file
4. Run the app
npm run build
You should now be able to access the app at
Containerising Pet Name Generator app using Docker Desktop
Let us try to run the Pet Name Generator app in a Docker container. To do this, you will need to install Docker locally in your system. I recommend using Docker Desktop which is free of cost for personal usage.
Create a Dockerfile
Create a Dockerfile: In your project directory, create a file called Dockerfile and add the following content to it:
# Use the official Node.js 10 image as the base FROM node:14 # Create a working directory RUN mkdir -p /usr/src/app # Set the working directory WORKDIR /usr/src/app # Copy the package.json and package-lock.json files COPY package*.json /usr/src/app/ # Install the dependencies RUN npm install # Copy the rest of the application code COPY . /usr/src/app # Expose the application's port EXPOSE 8080 # Run the application CMD [ "npm", "run", "dev" ]
This Dockerfile specifies the base image (node:14) to use for the Docker container and installs the OpenAI API client. It also copies the app code to the container and sets the working directory to the app code.
Building the Chatbot Docker Image
docker build -t ajeetraina/chatbot-docker .
Running the Chatbot container
docker run -d -p 3000:3000 ajeetraina/chatbot-docker
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