Running a local LLM model
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Notice:
LLMs require a lot of computational power and therefore lots of electricity.
Smaller models typically respond qualitatively worse than bigger ones, but they are faster, need less memory and might already be sufficient for your use case.
The size of a model can be measured in number of parameters in its neural network. The "b" in the model name typically stands for billion parameters. It also can be measured in terms of gigabytes required to load the model into your devices RAM/VRAM.
The model should always completely fit into VRAM (fast), otherwise layers will be offloaded to RAM (slower) and if it doesn't fit in there either, it will use SSD (abysmally slow).
Hardware recommendation for maximize prompt processing and token generation speed: A device with high bandwidth. A modern GPU with lots of VRAM will satisfy this requirement best.
You can use any program that creates a server with OpenAI-compatible API.
After you started your service, you can do this:
The "Chat Model" field in AI preferences is editable, so you can enter any model you have downloaded
There is a field called "API base URL" in "Expert Settings" where you need to provide the address of an OpenAI-compatible API server
VoilĂ ! You can use a local LLM right away in JabRef.
ollama
The following steps guide you on how to use ollama
to download and run local LLMs.
Install ollama
from
Select a model that you want to run. The ollama
provides to choose from. Some popular models are for instance , , , or .
When you have selected your model, type ollama pull <MODEL>:<PARAMETERS>
in your terminal. <MODEL>
refers to the model name like gemma2
or mistral
, and <PARAMETERS>
refers to parameters count like 2b
or 9b
.
ollama
will download the model for you
After that, you can run ollama serve to start a local web server. This server will accept requests and respond with LLM output. Note: The ollama server may already be running, so do not be alarmed by a cannot bind error. If it is not yet running, use the following command: ollama run <MODEL>:<PARAMETERS>
Go to JabRef Preferences -> AI
Set the "AI provider" to "OpenAI"
Set the "Chat Model" to the model you have downloaded in the format <MODEL>:<PARAMETERS>
Set the "API base URL" in "Expert Settings" to http://localhost:11434/v1/
Now, you are all set and can chat "locally".
The following steps guide you on how to use GPT4All
to download and run local LLMs.
Open JabRef, go to "File" > "Preferences" > "AI"
Set the "AI provider" to "GPT4All"
Set the "Chat model" to the name (including the .gguf
part) of the model you have downloaded in GPT4All.
Set the "API base URL" in "Expert Settings" to http://localhost:4891/v1/chat/completions
.
Install GPT4All
from their .
Open GPT4All, , configure it in the and .