The ultimate open source large language model ecosystem

Image by author

We are seeing the growth of open source language model ecosystems that offer individuals comprehensive resources for building language applications for both research and commercial purposes.

Earlier, we highlighted Open Assistant and OpenChatKit. Today we’ll dive into GPT4ALL, which goes beyond specific use cases and provides comprehensive building blocks that allow anyone to build a ChatGPT-like chatbot.

GPT4ALL is a project that offers everything you need to work with modern natural language models. You can access open source models and datasets, train and run them with the provided code, use a web interface or desktop app to interact with them, connect to the Langchain backend for distributed computing, and use the Python API for easy integration.

The Apache-2-licensed GPT4All-J chatbot was recently released by developers trained on a huge, curated corpus of assistant interactions, including word problems, multi-part dialogs, code, poems, songs, and stories. To make it more accessible, they’ve also released Python bindings and a chat interface, allowing virtually anyone to run the model on a CPU.

You can try it by installing the native chat client on your desktop.

After that, run the GPT4ALL program and download the model you want. You can also download the models manually here and install them in the GUI in the location specified in the model download dialog.

Image by author

I had a great experience using it on my laptop, getting fast and accurate responses. In addition, it is user-friendly, making it accessible even to non-technical individuals.

Author’s gif

GPT4ALL comes with Python, TypeScript, Web Chat interface and Langchain backend.

In this section, we’ll explore the Python API to access models using nomic-ai/pygpt4all.

1. Install the Python GPT4ALL library using PIP.

2. Download the GPT4All model from http://gpt4all.io/models/ggml-gpt4all-l13b-snoozy.bin. You can also browse other models here.
3. Create a text callback function, load the model, and provide the query mode.generate() Text generation function. Check the library documentation to learn more.

from pygpt4all.models.gpt4all import GPT4All


def new_text_callback(text):
    print(text, end="")


model = GPT4All("./models/ggml-gpt4all-l13b-snoozy.bin")
model.generate("Once upon a time, ", n_predict=55, new_text_callback=new_text_callback)

In addition, you can download and run the conclusion using transformers. Just specify the model name and version. In our case, we’re accessing the latest and improved v1.3-groovy model.

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "nomic-ai/gpt4all-j", revision="v1.3-groovy"
)

The nomic-ai/gpt4all repository includes the training and inference source code, model weights, database, and documentation. You can start by trying some models on your own and then try to integrate it using a Python client or LangChain.

GPT4ALL provides a CPU quantized GPT4All model checkpoint. To access it we need to:

• Download gpt4all-lora-quantized.bin file from direct link or [Torrent-Magnet].
• Clone this repository and extract the downloaded bin file chat Folder.
• Run the appropriate command to access the model:
  • M1 Mac/OSX: cd chat;./gpt4all-lora-quantized-OSX-m1
  • Linux: cd chat;./gpt4all-lora-quantized-linux-x86
  • Windows (PowerShell): cd chat;./gpt4all-lora-quantized-win64.exe
  • Intel Mac/OSX: cd chat;./gpt4all-lora-quantized-OSX-intel

You can also go to Hugging Face Spaces and try the Gpt4all demo. It’s not official, but it’s a start.

Image from Gpt4all

Resources:

GPT4ALL Backend: GPT4All — ???? LangChain 0.0.154

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who enjoys building machine learning models. Currently, he focuses on content creation and writes technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a Bachelor’s degree in Telecommunications Engineering. His vision is to create an AI product using a graph neural network for students struggling with mental illness.