parllama 0.3.7

Description:

parllama 0.3.7

PAR LLAMA
Table of Contents

About

Screenshots


Prerequisites for running
Prerequisites for dev
Prerequisites for huggingface model quantization
Installing from mypi using pipx
Installing from mypi using pip
Installing for dev mode
Command line arguments
Environment Variables
Running PAR_LLAMA

with pipx installation
with pip installation


Running against a remote instance
Running under Windows WSL

Dev mode


Quick start chat workflow
Custom Prompts
Themes
Contributing
FAQ
Roadmap

Where we are
Where we're going


What's new

v0.3.7
v0.3.6
v0.3.5
v0.3.4
v0.3.3
v0.3.2
v0.3.1
v0.3.0
v0.2.51
v0.2.5



About
PAR LLAMA is a TUI application designed for easy management and use of Ollama based LLMs.
The application was built with Textual and Rich
and runs on all major OS's including but not limited to Windows, Windows WSL, Mac, and Linux.
Screenshots
Supports Dark and Light mode as well as custom themes.







Prerequisites for running

Install and run Ollama
Install Python 3.11 or newer

https://www.python.org/downloads/ has installers for all versions of Python for all os's
On Windows the Scoop tool makes it easy to install and manage things like python

Install Scoop then do scoop install python





Prerequisites for dev

Install pipenv

if you have pip you can install it globally using pip install pipenv


Install GNU Compatible Make command

On windows if you have scoop installed you can install make with scoop install make



Prerequisites for huggingface model quantization
If you want to be able to quantize custom models from huggingface, download the following tool from the releases area:
HuggingFaceModelDownloader
Install Docker Desktop
Pull the docker image ollama/quantize
docker pull ollama/quantize

Installing from mypi using pipx
If you don't have pipx installed you can run the following:
pip install pipx
pipx ensurepath

Once pipx is installed, run the following:
pipx install parllama

To upgrade an existing installation use the --force flag:
pipx install parllama --force

Installing from mypi using pip
Create a virtual environment and install using pip
mkdir parllama
cd parllama
python -m venv venv
source venv/Scripts/activate
pip install parllama

Installing for dev mode
Clone the repo and run the following from the root of the repo:
make first-setup

Command line arguments
usage: parllama [-h] [-v] [-d DATA_DIR] [-u OLLAMA_URL] [-t THEME_NAME] [-m {dark,light}]
[-s {local,site,chat,prompts,tools,create,options,logs}] [--use-last-tab-on-startup {0,1}] [-p PS_POLL] [-a {0,1}]
[--restore-defaults] [--purge-cache] [--purge-chats] [--purge-prompts] [--no-save] [--no-chat-save]

PAR LLAMA -- Ollama TUI.

options:
-h, --help show this help message and exit
-v, --version Show version information.
-d DATA_DIR, --data-dir DATA_DIR
Data Directory. Defaults to ~/.parllama
-u OLLAMA_URL, --ollama-url OLLAMA_URL
URL of your Ollama instance. Defaults to http://localhost:11434
-t THEME_NAME, --theme-name THEME_NAME
Theme name. Defaults to par
-m {dark,light}, --theme-mode {dark,light}
Dark / Light mode. Defaults to dark
-s {local,site,chat,prompts,tools,create,options,logs}, --starting-tab {local,site,chat,prompts,tools,create,options,logs}
Starting tab. Defaults to local
--use-last-tab-on-startup {0,1}
Use last tab on startup. Defaults to 1
-p PS_POLL, --ps-poll PS_POLL
Interval in seconds to poll ollama ps command. 0 = disable. Defaults to 3
-a {0,1}, --auto-name-session {0,1}
Auto name session using LLM. Defaults to 0
--restore-defaults Restore default settings and theme
--purge-cache Purge cached data
--purge-chats Purge all chat history
--purge-prompts Purge all custom prompts
--no-save Prevent saving settings for this session
--no-chat-save Prevent saving chats for this session

Unless you specify "--no-save" most flags such as -u, -t, -m, -s are sticky and will be used next time you start PAR_LLAMA.
Environment Variables

PARLLAMA_DATA_DIR - Used to set --data-dir
PARLLAMA_THEME_NAME - Used to set --theme-name
PARLLAMA_THEME_MODE - Used to set --theme-mode
OLLAMA_URL - Used to set --ollama-url
PARLLAMA_AUTO_NAME_SESSION - Set to 0 or 1 to disable / enable session auto naming using LLM

Running PAR_LLAMA
with pipx installation
From anywhere:
parllama

with pip installation
From parent folder of venv
source venv/Scripts/activate
parllama

Running against a remote instance
parllama -u "http://REMOTE_HOST:11434"

Running under Windows WSL
Ollama by default only listens to localhost for connections, so you must set the environment variable OLLAMA_HOST=0.0.0.0:11434
to make it listen on all interfaces.
Note: this will allow connections to your Ollama server from other devices on any network you are connected to.
If you have Ollama installed via the native Windows installer you must set OLLAMA_HOST=0.0.0.0:11434 in the "System Variable" section
of the "Environment Variables" control panel.
If you installed Ollama under WSL, setting the var with export OLLAMA_HOST=0.0.0.0:11434 before starting the Ollama server will have it listen on all interfaces.
If your Ollama server is already running, stop and start it to ensure it picks up the new environment variable.
You can validate what interfaces the Ollama server is listening on by looking at the server.log file in the Ollama config folder.
You should see as one of the first few lines "OLLAMA_HOST:http://0.0.0.0:11434"
Now that the server is listening on all interfaces you must instruct PAR_LLAMA to use a custom Ollama connection url with the "-u" flag.
The command will look something like this:
parllama -u "http://$(hostname).local:11434"

Depending on your DNS setup if the above does not work, try this:
parllama -u "http://$(grep -m 1 nameserver /etc/resolv.conf | awk '{print $2}'):11434"

PAR_LLAMA will remember the -u flag so subsequent runs will not require that you specify it.
Dev mode
From repo root:
make dev

Quick start chat workflow

Start parllama.
Click the "Site" tab.
Use ^R to fetch the latest models from Ollama.com.
Use the "Filter Site models" text box and type "llama3".
Find the entry with title of "llama3".
Click the blue tag "8B" to update the search box to read "llama3:8b".
Press ^P to pull the model from Ollama to your local machine. Depending on the size of the model and your internet connection this can take a few min.
Click the "Local" tab to see models that have been locally downloaded.
Select the "llama3:8b" entry and press ^C to jump to the "Chat" tab and auto select the model.
Type a message to the model such as "Why is the sky blue?". It will take a few seconds for Ollama to load the model. After which the LLMs answer will stream in.
Towards the very top of the app you will see what model is loaded and what percent of it is loaded into the GPU / CPU. If a model cant be loaded 100% on the GPU it will run slower.
To export your conversation as a Markdown file type "/session.export" in the message input box. This will open a export dialog.
Press ^N to add a new chat tab.
Select a different model or change the temperature and ask the same questions.
Jump between the tabs to compare responses by click the tabs or using slash commands /tab.1 and /tab.2
Press ^S to see all your past and current sessions. You can recall any past session by selecting it and pressing Enter or ^N if you want to load it into a new tab.
Type "/help" or "/?" to see what other slash commands are available.

Custom Prompts
You can create a library of custom prompts for easy starting of new chats.
You can set up system prompts and user messages to prime conversations with the option of sending immediately to the LLM upon loading of the prompt.
Currently, importing prompts from the popular Fabric project is supported with more on the way.
Themes
Themes are json files stored in the themes folder in the data directory which defaults to ~/.parllama/themes
The default theme is "par" so can be located in ~/.parllama/themes/par.json
Themes have a dark and light mode are in the following format:
{
"dark": {
"primary": "#e49500",
"secondary": "#6e4800",
"warning": "#ffa62b",
"error": "#ba3c5b",
"success": "#4EBF71",
"accent": "#6e4800",
"panel": "#111",
"surface":"#1e1e1e",
"background":"#121212",
"dark": true
},
"light": {
"primary": "#004578",
"secondary": "#ffa62b",
"warning": "#ffa62b",
"error": "#ba3c5b",
"success": "#4EBF71",
"accent": "#0178D4",
"background":"#efefef",
"surface":"#f5f5f5",
"dark": false
}
}

You must specify at least one of light or dark for the theme to be usable.
Theme can be changed via command line with the --theme-name option.
Contributing
Start by following the instructions in the section Installing for dev mode.
Please ensure that all pull requests are formatted with black, pass mypy and pylint with 10/10 checks.
You can run the make target pre-commit to ensure the pipeline will pass with your changes.
There is also a pre-commit config to that will assist with formatting and checks.
The easiest way to setup your environment to ensure smooth pull requests is:
If you don't have pipx installed you can run the following:
pip install pipx
pipx ensurepath

With pipx installed:
pipx install pre-commit

From repo root run the following:
pre-commit install
pre-commit run --all-files

After running the above all future commits will auto run pre-commit. pre-commit will fix what it can and show what
if anything remains to be fixed before the commit is allowed.
FAQ

Q: Do I need Docker?

A: Docker is only required if you want to Quantize models downloaded from Huggingface or similar llm repositories.


Q: Does ParLlama require internet access?

A: ParLlama by default does not require any network / internet access unless you enable checking for updates or want to import / use data from an online source.


Q: Does ParLlama run on ARM?

A: Short answer is yes. ParLlama should run any place python does. It has been tested on Windows 11 x64, Windows WSL x64, Mac OSX intel and silicon


Q: Does Parllama require Ollama be installed locally?

A: No. ParLlama has options to connect to remote Ollama instances



Roadmap
Where we are

Initial release - Find, maintain and create new models
Connect to remote instances
Chat with history / conversation management
Chat tabs allow chat with multiple models at same time
Custom prompt library with import from Fabric
Auto complete of slash commands, input history, multi line edit

Where we're going

Chat using embeddings for local documents
Expand ability to import custom prompts of other tools
LLM tool use
Ability to use other AI providers like Open AI

What's new
v0.3.7

Fix for possible crash when there is more than one model loaded into ollama

v0.3.6

Added option to save chat input history and set its length
Fixed tab switch issue on startup
Added cache for Fabric import to speed up subsequent imports

v0.3.5

Added first time launch welcome
Added Options tab which exposes more options than are available via command line switches
Added option to auto check for new versions
Added ability to import custom prompts from fabric
Added toggle between single and multi line input (Note auto complete and command history features not available in multi line edit mode)

v0.3.4

Added custom prompt library support (Work in progress)
Added cli option and environment var to enable auto naming of sessions using LLM (Work in progress)
Added tokens per second stats to session info line on chat tab
Fixed app crash when it cant contact ollama server for PS info
Fixed slow startup when you have a lot of models available locally
Fixed slow startup and reduced memory utilization when you have many / large chats
Fixed session unique naming bug where it would always add a "1" to the session name
Fixed app sometimes slowing down during LLM generation
Major rework of internal message handling
Issue where some footer items are not clickable has been resolved by a library PARLLAMA depends on

v0.3.3

Added ability to edit existing messages. select message in chat list and press "e" to edit, then "escape" to exit edit mode
Add chat input history access via up / down arrow while chat message input has focus
Added /session.system_prompt command to set system prompt in current chat tab

v0.3.2

Ollama ps stats bar now works with remote connections except for CPU / GPU %'s which ollama's api does not provide
Chat tabs now have a session info bar with info like current / max context length
Added conversation stop button to abort llm response
Added ability to delete messages from session
More model details displayed on model detail screen
Better performance when changing session params on chat tab

v0.3.1

Add chat tabs to support multiple sessions
Added cli option to prevent saving chat history to disk
Renamed / namespaced chat slash commands for better consistency and grouping
Fixed application crash when ollama binary not found

v0.3.0

Added chat history panel and management to chat page

v0.2.51

Fix missing dependency in package

v0.2.5

Added slash commands to chat input
Added ability to export chat to markdown file
ctrl+c on local model list will jump to chat tab and select currently selected local model
ctrl+c on chat tab will copy selected chat message