In this blog post, we will guide you through the process of setting up and running your own private Open Assistant. Open Assistant is a chat-based assistant capable of understanding tasks, interacting with third-party systems, and retrieving information dynamically. It is a genuinely open-source solution, unlike other popular solutions that only include the word “open” in their names.
By following this tutorial, you can harness the power of Open Assistant without relying on third-party inference APIs or exposing your conversations to external entities.
To follow this tutorial, you will need:
Create New Instance.
Choose the location for your new instance: 🇮🇸 / 🇳🇴
If you’re unsure, select one closer to your location to reduce latency, which will be beneficial later in the process.
NVIDIA® GeForce™ RTX 3090instance. The “CPU & Memory optimized” instance types are a good choice as they provide double the system memory, which is sensible for this use case.
20.04as the system image for your instance.
Your instance will be created and will appear on the console dashboard. A message will be displayed, and the public IPv4 address will become visible. This process usually takes 1-2 minutes.
Unless stated otherwise, execute all the following steps via SSH on your instance.
Windows users can use Putty (guide available here), while Linux or macOS command line SSH client users can refer to this knowledge base entry.
Configure a local Ubuntu mirror and update the base system.
The examples below assume you created your instance in Norway. If you used Iceland, replace the country code
This command will configure your apt to use a mirror in the selected country, ensuring optimal download speeds for system packages.
sudo sed -i 's/nova.clouds./NO./g' /etc/apt/sources.list && sudo apt update
Upgrade all packages (without prompting for confirmation).
sudo apt -o Dpkg::Options::="--force-confold" upgrade --yes
At the time of writing, CUDA 12 was released. As the compatibility of many software packages is not yet a given we install CUDA 11.8 to avoid unexpected issues.
Ensure you have read, understood, and agreed to the appropriate EULA.
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.0-1_all.deb sudo dpkg -i cuda-keyring_1.0-1_all.deb sudo apt-get update sudo apt-get -y install cuda-11-8
Add CUDA to the relevant paths to ensure it can be found. Execute the following sed command to add two export statements that set the proper environment variables to your
sed -i '1i\export PATH="/usr/local/cuda/bin:$PATH"\nexport LD_LIBRARY_PATH="/usr/local/cuda/lib64:$LD_LIBRARY_PATH"\n' ~/.bashrc
Reboot (do not skip)!
The reboot is required to load the NVIDIA drivers, as the current system still has the
nouveau drivers loaded, causing a conflict.
We will use the
text-generation-webui to interface with the Open Assistant model.
curl -sL "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" > "Miniconda3.sh" # The next command installs Miniconda in batch mode. It expects that you agree with its license terms! bash Miniconda3.sh -b -u -p $HOME/miniconda3 ~/miniconda3/bin/conda init $(basename $SHELL)
Now close your shell (or disconnect your SSH session) and re-connect. If you see a
(base) in front of your prompt, the Miniconda install succeeded.
Install the build dependencies
sudo apt -y install build-essential
Create a new conda environment for all of our Python dependencies
conda create --yes -n textgen python=3.10.10
Activate your conda environment. This step must be repeated every time you are in a new session as it is not persisted (e.g., after disconnecting SSH or restarting your instance).
conda activate textgen
Install torch and related Python packages via pip. We specifically select a version compatible with CUDA 11.8 that we installed earlier:
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
Install the web UI and its requirements
git clone https://github.com/oobabooga/text-generation-webui cd text-generation-webui pip install -r requirements.txt
As outlined in the README of the text-generation-webui we have to place the models in the aptly named
Luckily, this is mostly automated. Execute the following command in the current directory to take care of it:
python3 download-model.py OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5
This will download the ~23 GB open/free model data from the Hugging Face servers. There are other variants with questionable legality floating around, use those on your own risk. Your Genesis Cloud instance can (by default) download with up to 1Gbit/s so you can expect this step to take 4-5 minutes depending on the load and connectivity of the servers.
Now that we have the model in place, we can start the web UI:
python3 server.py --gpu-memory 22 --share
If you use a GPU other than a RTX3090, you need to adopt the
--gpu-memory parameter. The same is true if you want to use multiple GPUs. Running
python3 server.py -h will provide more details and examples.
Suppose you disconnected your SSH session to set up the forwarding. In that case, you need to re-activate the conda environment, switch to the text-generation-webui directory, and start the server again:
conda activate textgen cd text-generation-webui python3 server.py --chat --model OpenAssistant_oasst-sft-4-pythia-12b-epoch-3.5 --gpu-memory 22 --share # Give it a few seconds to load the model and start-up
You can now access the web UI at the displayed URL (https://….gradio.live) 🎉
We recommend to not rely on the gradio proxy service to access the service but accessing it in another way. As there are many ways to skin this cat (SSH port forwarding, local proxy with TLS termination, using (free) Cloudflare fronting, …) it should be out of scope for this article (though not relying on the public gradio proxy service makes it much more responsive).
Now that everything is up and running we want to use the WebUi. Is it as easy as it gets:
If you only get truncated responses, check your console output for
You can work around those by using an instance with multiple (e.g., 2x RTX 3090) GPUs. If you use multiple GPU make sure to adopt the
--gpu-memory parameter appropriately by noting the amount of vmem that should be allocated separated by spaces. E.g.,
--gpu-memory 23 23 for 2x RTX 3090).
Keep accelerating 🚀
The Genesis Cloud team
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Sign up for an account with Genesis Cloud here and benefit from $15 in free credits. If you want to find out more, please write to [email protected].Written on April 20th, 2023 by Tristan Helmich, Marouane Khoukh