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<a href="https://www.kdnuggets.com/2023/05/ultimate-opensource-large-language-model-ecosystem.html" rel="noopener" target="_blank">GPT4ALL</a> is a free and open-source AI Playground that can be run locally on Windows, Mac, and Linux computers without requiring an internet connection or a GPU. It offers users access to various state-of-the-art language models through a simple two-step process. All you need to do is to download and install the application and then download your preferred model.
<a href="https://github.com/nomic-ai/gpt4all" rel="noopener" target="_blank">GPT4ALL</a> is a project that provides everything you need to work with state-of-the-art natural language models. You can access open source models and datasets, train and run them with the provided code, use a web interface or a desktop app to interact with them, connect to the Langchain Backend for distributed computing, and use the Python API for easy integration.
Apache-2 licensed GPT4All-J chatbot was recently launched by the developers, which was trained on a vast, curated corpus of assistant interactions, comprising word problems, multi-turn dialogues, code, poems, songs, and stories. To make it more accessible, they have also released Python bindings and a Chat UI, enabling virtually anyone to run the model on CPU.&nbsp;
You can try it yourself by installing native chat-client on your desktop.&nbsp;
<ul>
<li><a href="https://gpt4all.io/installers/gpt4all-installer-darwin.dmg" rel="noopener" target="_blank">Mac/OSX</a></li>
<li><a href="https://gpt4all.io/installers/gpt4all-installer-win64.exe" rel="noopener" target="_blank">Windows</a></li>
<li><a href="https://gpt4all.io/installers/gpt4all-installer-linux.run" rel="noopener" target="_blank">Ubuntu</a></li>
</ul>
After that, run the GPT4ALL program and download the model of your choice. You can also download models manually <a href="https://github.com/nomic-ai/gpt4all-chat#manual-download-of-models" rel="noopener" target="_blank">here</a> and install them in the location indicated by the model download dialog in the GUI.
<h1>GPT4ALL Python Client</h1>
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The GPT4ALL comes with <a href="https://github.com/nomic-ai/pyllamacpp" rel="noopener" target="_blank">Python</a>, <a href="https://github.com/nomic-ai/gpt4all-ts" rel="noopener" target="_blank">TypeScript</a>, <a href="https://github.com/nomic-ai/gpt4all-ui" rel="noopener" target="_blank">Web Chat interface</a>, and <a href="https://python.langchain.com/en/latest/modules/models/llms/integrations/gpt4all.html" rel="noopener" target="_blank">Langchain</a> backend.&nbsp;&nbsp;
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In this section, we will look into the Python API to access the models using&nbsp; <a href="https://github.com/nomic-ai/pygpt4all" rel="noopener" target="_blank">nomic-ai/pygpt4all</a>.
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<ol>
<li>Install the Python GPT4ALL library using PIP.</li>
</ol>
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<div style="width: 98%; overflow: auto; padding-left: 10px; padding-bottom: 10px; padding-top: 10px; background: #F5F5F5;">
<pre><code>pip install pygpt4all</code></pre>
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<ol start="2">
<li>Download a GPT4All model from <a href="http://gpt4all.io/models/ggml-gpt4all-l13b-snoozy.bin" rel="noopener" target="_blank">http://gpt4all.io/models/ggml-gpt4all-l13b-snoozy.bin</a>. You can also browse other models <a href="https://github.com/nomic-ai/gpt4all-chat#manual-download-of-models" rel="noopener" target="_blank">here</a>.</li>
<li>Create a text callback function, load the model, and provide a prompt to <code style="background: #F5F5F5;">mode.generate()</code> function to generate text. Check out the library <a href="https://nomic-ai.github.io/pygpt4all/" rel="noopener" target="_blank">documentation</a> to learn more.</li>
</ol>
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<div style="width: 98%; overflow: auto; padding-left: 10px; padding-bottom: 10px; padding-top: 10px; background: #F5F5F5;">
<pre><code>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)
</code></pre>
</div>
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Moreover, you can download and run inference using transformers. Just provide the model name and the version. In our case, we are accessing the latest and improved v1.3-groovy model.
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<div style="width: 98%; overflow: auto; padding-left: 10px; padding-bottom: 10px; padding-top: 10px; background: #F5F5F5;">
<pre><code>from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
 "nomic-ai/gpt4all-j", revision="v1.3-groovy"
)
</code></pre>
</div>
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<h1>Getting Started</h1>
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The <a href="https://github.com/nomic-ai/gpt4all" rel="noopener" target="_blank">nomic-ai/gpt4all</a> repository comes with source code for training and inference, model weights, dataset, and documentation. You can start by trying a few models on your own and then try to integrate it using a Python client or LangChain.&nbsp;
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The GPT4ALL provides us with a CPU quantized GPT4All model checkpoint. To access it, we have to:
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<ul>
<li>Download the gpt4all-lora-quantized.bin file from <a href="https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-quantized.bin" rel="noopener" target="_blank">Direct Link</a> or <a href="https://tinyurl.com/gpt4all-lora-quantized" rel="noopener" target="_blank">[Torrent-Magnet]</a>.</li>
<li>Clone this repository and move the downloaded bin file to <code style="background: #F5F5F5;">chat</code> folder.&nbsp;</li>
<li>Run the appropriate command to access the model:
<ul>
<li>M1 Mac/OSX: <code style="background: #F5F5F5;">cd chat;./gpt4all-lora-quantized-OSX-m1</code></li>
<li>Linux: <code style="background: #F5F5F5;">cd chat;./gpt4all-lora-quantized-linux-x86</code></li>
<li>Windows (PowerShell): <code style="background: #F5F5F5;">cd chat;./gpt4all-lora-quantized-win64.exe</code></li>
<li>Intel Mac/OSX: <code style="background: #F5F5F5;">cd chat;./gpt4all-lora-quantized-OSX-intel</code></li>
</ul>
</li>
</ul>
You can also head to Hugging Face Spaces and try out the <a href="https://huggingface.co/spaces/Monster/GPT4ALL" rel="noopener" target="_blank">Gpt4all</a> demo. It is not official, but it is a start.
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