Get a Taste of LLMs from GPT4All

 

Large language models have become popular recently. ChatGPT is fashionable. Trying out ChatGPT to understand what LLMs are about is easy, but sometimes, you may want an offline alternative that can run on your computer. In this post, you will learn about GPT4All as an LLM that you can install on your computer. In particular, you will learn

• What is GPT4All
• How to install the desktop client for GPT4All
• How to run GPT4All in Python

 

Overview

This post is divided into three parts; they are:

• What is GPT4All?
• How to get GPT4All
• How to use GPT4All in Python

What is GPT4All?

The term “GPT” is derived from the title of a 2018 paper, “Improving Language Understanding by Generative Pre-Training” by Radford et al. This paper describes how transformer models are demonstrated to be able to understand human language.

Since then, many people attempted to develop language models using transformer architecture, and it has been found that a model large enough can give excellent results. However, many of the models developed are proprietary. There are either provided as a service with paid subscription or under a license with certain restrictive terms. Some are even impossible to run on commodity hardware due to is size.

GPT4All project tried to make the LLMs available to the public on common hardware. It allows you to train and deploy your model. Pretrained models are also available, with a small size that can reasonably run on a CPU.

How to get GPT4All

Let’s focus only on using the pre-trained models.

At the time of writing, GPT4All is available from https://gpt4all.io/index.html, which you can run as a desktop application or using a Python library. You can download the installer for your OS to run a desktop client. The client is only a few hundred MB. You should see an installation screen as follows:

After you have the client installed, launching it the first time will prompt you to install a model, which can be as large as many GB. To start, you may pick “gpt4all-j-v1.3-groovy” (the GPT4All-J model). It is a relatively small but popular model.

Once the client and model are ready, you can type your message in the input box. The model may expect a specific form of input, e.g., a particular language or style. This model expects a conversation style (like ChatGPT) and generally handles English well. For example, below is how it responds to the input “Give me a list of 10 colors and their RGB code”:

How to use GPT4All in Python

The key component of GPT4All is the model. The desktop client is merely an interface to it. Besides the client, you can also invoke the model through a Python library.

The library is unsurprisingly named “gpt4all,” and you can install it with pip command:

1	pip install gpt4all

Note: This is a fast-moving library and the functions may change. The following code has been tested on version 1.0.12 but it may not work in future versions.

Afterward, you can use it in Python in just a few lines of code:

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import pprint   import gpt4all   model = gpt4all.GPT4All("orca-mini-7b.ggmlv3.q4_0.bin") with model.chat_session():     response = model.generate("Give me a list of 10 colors and their RGB code")     print(response)     pprint.pprint(model.current_chat_session)

Running the above code will download the model file if you haven’t yet. Afterward, the model is loaded, input is provided, and the response is returned as a string. The output printed may be:

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Sure, here's a list of 10 colors along with their RGB codes:   1. Red (255, 0, 0) 2. Blue (0, 0, 255) 3. Green (0, 255, 0) 4. Yellow (255, 255, 0) 5. Orange (255, 165, 0) 6. Purple (192, 118, 192) 7. Pink (255, 192, 203) 8. Maroon (153, 42, 102) 9. Teal (0, 128, 128) 10. Lavender (238, 102, 147)

The chat history of the session is stored in the model’s attribute current_chat_session as a Python list. An example is as follows:

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[{'content': '### System:\n'              'You are an AI assistant that follows instruction extremely well. '              'Help as much as you can.',   'role': 'system'}, {'content': 'Give me a list of 10 colors and their RGB code', 'role': 'user'}, {'content': " Sure, here's a list of 10 colors along with their RGB codes:\n"              '\n'              '1. Red (255, 0, 0)\n'              '2. Blue (0, 0, 255)\n'              '3. Green (0, 255, 0)\n'              '4. Yellow (255, 255, 0)\n'              '5. Orange (255, 165, 0)\n'              '6. Purple (192, 118, 192)\n'              '7. Pink (255, 192, 203)\n'              '8. Maroon (153, 42, 102)\n'              '9. Teal (0, 128, 128)\n'              '10. Lavender (238, 102, 147)',   'role': 'assistant'}]

The history is a sequence of dialog in the format of Python dictionaries with keys role and content. The role can be "system", "assistant", or "user", while content is a string of text. If you’re chatting with your model like the example, your role is "user" while the computer’s response is "assistant". You can keep using the generate() call to continue your conversation. Below is an example:

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import pprint   import gpt4all   model = gpt4all.GPT4All("orca-mini-7b.ggmlv3.q4_0.bin") with model.chat_session():     questions = [         "Can you explain what is a large language model?",         "Can you give some examples applications?",         "Are there any limitations?",         "Summarize the above in two sentences.",     ]     for question in questions:         answer = model.generate(question)         print("Q:", question)         print("A:", answer)       pprint.pprint(model.current_chat_session)

Note that you invoked the model multiple times in the for-loop. Each time it responded, the model took the output and appended it to the list of chat messages so you accumulated the context. Then you add a new dialog and invoke the model again. This is how the model remember the chat history. Below is an example of how the above code respond to your questions:

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Q: Can you explain what is a large language model? A:  A large language model (LLM) is a type of artificial intelligence model that uses deep learning techniques to analyze and generate human-like language. These models are trained on massive amounts of text data, which allows them to learn complex patterns and relationships in the language. LLMs can be used for a variety of natural language processing tasks such as language translation, sentiment analysis, speech recognition, and more. They are typically implemented using deep learning frameworks like TensorFlow or PyTorch and require large amounts of computational resources to train and run effectively. Q: Can you give some examples applications? A:  Yes, there are many applications for LLMs, including:   1. Chatbots: LLMs can be used to power chatbots that can understand natural language and respond appropriately to user queries.   2. Virtual assistants: LLMs can help power virtual assistants like Siri or Alexa, allowing them to understand and respond to user requests in a more human-like way.   3. Language translation: LLMs can be used to translate text from one language to another with high accuracy.   4. Sentiment analysis: LLMs can analyze social media posts or customer feedback to determine the sentiment behind the content, which can help businesses understand their customers better.   5. Speech recognition: LLMs can be used to power speech recognition systems that allow users to speak commands and receive responses in natural language. Q: Are there any limitations? A:  Yes, there are some limitations to LLMs, including:   1. Data requirements: LLMs require large amounts of data to train effectively. This means that the model may not perform well on tasks with limited training data.   2. Overfitting: LLMs can sometimes overfit the training data, meaning they memorize the data rather than learning from it. This can lead to poor generalization and accuracy issues when tested on new data.   3. Hardware requirements: LLMs require powerful hardware to run effectively, which may not be available or affordable for all users.   4. Interpretability: LLMs can be difficult to interpret and understand how they are making decisions, which may limit their usefulness in some applications. Q: Summarize the above in two sentences. A:  There are limitations to LLMs such as data requirements, overfitting, hardware requirements, and interpretability.

Therefore, the chat history accumulated by the end of the above code would be the following:

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[{'content': '### System:\n'              'You are an AI assistant that follows instruction extremely well. '              'Help as much as you can.',   'role': 'system'}, {'content': 'Can you explain what is a large language model?', 'role': 'user'}, {'content': ' A large language model (LLM) is a type of artificial '              'intelligence model that uses deep learning techniques to analyze '              'and generate human-like language. These models are trained on '              'massive amounts of text data, which allows them to learn complex '              'patterns and relationships in the language. LLMs can be used for '              'a variety of natural language processing tasks such as language '              'translation, sentiment analysis, speech recognition, and more. '              'They are typically implemented using deep learning frameworks '              'like TensorFlow or PyTorch and require large amounts of '              'computational resources to train and run effectively.',   'role': 'assistant'}, {'content': 'Can you give some examples applications?', 'role': 'user'}, {'content': ' Yes, there are many applications for LLMs, including:\n'              '\n'              '1. Chatbots: LLMs can be used to power chatbots that can '              'understand natural language and respond appropriately to user '              'queries.\n'              '\n'              '2. Virtual assistants: LLMs can help power virtual assistants '              'like Siri or Alexa, allowing them to understand and respond to '              'user requests in a more human-like way.\n'              '\n'              '3. Language translation: LLMs can be used to translate text from '              'one language to another with high accuracy.\n'              '\n'              '4. Sentiment analysis: LLMs can analyze social media posts or '              'customer feedback to determine the sentiment behind the content, '              'which can help businesses understand their customers better.\n'              '\n'              '5. Speech recognition: LLMs can be used to power speech '              'recognition systems that allow users to speak commands and '              'receive responses in natural language.',   'role': 'assistant'}, {'content': 'Are there any limitations?', 'role': 'user'}, {'content': ' Yes, there are some limitations to LLMs, including:\n'              '\n'              '1. Data requirements: LLMs require large amounts of data to '              'train effectively. This means that the model may not perform '              'well on tasks with limited training data.\n'              '\n'              '2. Overfitting: LLMs can sometimes overfit the training data, '              'meaning they memorize the data rather than learning from it. '              'This can lead to poor generalization and accuracy issues when '              'tested on new data.\n'              '\n'              '3. Hardware requirements: LLMs require powerful hardware to run '              'effectively, which may not be available or affordable for all '              'users.\n'              '\n'              '4. Interpretability: LLMs can be difficult to interpret and '              'understand how they are making decisions, which may limit their '              'usefulness in some applications.',   'role': 'assistant'}, {'content': 'Summarize the above in two sentences.', 'role': 'user'}, {'content': ' There are limitations to LLMs such as data requirements, '              'overfitting, hardware requirements, and interpretability.',   'role': 'assistant'}]

You may get a better result from another model. You may also get a different result due to the randomness in the model.

Summary

GPT4All is a nice tool you can play with on your computer. It allows you to explore the interaction with a large language model and help you better understand the capability and limitation of a model. In this post, you learned that:

• GPT4All has a desktop client that you can install on your computer
• GPT4All has a Python interface that allows you to interact with a language model in code
• There are multiple language model available