_                     _                 _         
| |__   __ _ ___  __ _| |__  _   _ _   _| | ____ _ 
| '_ \ / _` / __|/ _` | '_ \| | | | | | | |/ / _` |
| |_) | (_| \__ \ (_| | |_) | |_| | |_| |   < (_| |
|_.__/ \__,_|___/\__,_|_.__/ \__,_|\__,_|_|\_\__,_|
                                                   

This whole code is licensed under the License of Redistribution 1.0. You can find a copy of this licence on this instance, under

https://code.basabuuka.org/alpcentaur/license-of-redistribution

The whole prototype is nested in a tensorflow/tensorflow:2.2.0-gpu docker container. Install graphic card drivers according to your hardware and your OS. To make the tensorflow docker container work.

Also get Ollama running in a docker container, sharing the same network protoRustNet. That it is reachable from the ollama-cli-server under http://ollama:11434/api/generate.

I run my ollama container seperately together with open web ui. Like that, I can administrate the models over the web ui, and then use them by changing the code in the fastapi_server.py file of the ollama-cli-server container.

After having set up the ollama container and the gpu docker drivers, just start the whole project in the

compose

directory with

docker compose up -d

The deb-rust-interface will be running on port

For instructions how to setup a webserver as reverse proxy, you can contact basabuuka. My nginx configuration for the basabuuka prototype is the following:

upstream protointerface {
	server 127.0.0.1:1030;
}

server {

	server_name		  example.org;
	# access logs consume resources
	access_log /var/log/nginx/example.org.access.log;	

        location / {
		proxy_pass            http://protointerface;
	}

        # set high timeouts for using 14b model on 200 euro gpu
        proxy_connect_timeout       300;
        proxy_send_timeout          300;
        proxy_read_timeout          300;
        send_timeout                300;
        keepalive_timeout           300;


}