VOLlama

Accessible Chat Client for Ollama

Instructions

To use VOLlama, you must first set up Ollama and download a model from Ollama’s library. Follow these steps:

Download and install Ollama.

You will need a model to generate text. Execute the command below in terminal (or command-line on Windows) to download a model. If you prefer to use a different model, replace llama3 with your chosen model.

ollama pull llama3.1

Optionally, If you want to utilize the image description feature, you need to download a multimodal (vision+language) model.

ollama pull llama3.2-vision

Optionally, If you want to utilize the retrieval-augmented generation feature, you need to download nomic-embed-text for embedding.

ollama pull nomic-embed-text

Finally, run VOLlama.

For Mac, VOLlama is not notarized by Apple, so you need to allow to run in system settings > privacy and security.

VOLlama may take a while to load especially on Mac, so be patient. You’ll eventually hear “VOLlama is starting.”

If you want responses to be read aloud automatically, you can enable the “Speak Response with System Voice” option from the chat menu.

If you are operating Ollama on a different machine, configure the host address in Chat menu > API Settings > Ollama > Base URL.

Shortcuts

Shortcuts for all the features can be found in the menu bar. Here are exceptions:

Control(or Command)+Enter: Insert a new line.
Esc: Shift focus to the prompt.

Image Description

In order to ask a multimodal model questions about an image:

choose a multimodal model from the toolbar (control+l.)
Attach an image file from the chat menu (or control+i.)
Type your question like “Can you describe the image?” on the prompt field and send it.

Generation Parameter Values

This table lists the generation parameters available in VOLlama, along with their descriptions, types, and default values:

Parameter	Description	Value Type	Default Value
num_ctx	Sets the size of the context window used to generate the next token. Depends on the model’s limit.	int	4096
num_predict	Maximum number of tokens to predict during text generation. Use -1 for infinite, -2 to fill context.	int	-1
temperature	Adjusts the model’s creativity. Higher values lead to more creative responses. Range: 0.0-2.0.	float	0.8
top_k	Limits the likelihood of less probable responses. Higher values allow more diversity. Range: -1-100	int	40
top_p	Works with top_k to manage diversity of responses. Higher values lead to more diversity. Range: 0.0-1.0.	float	0.95
min_p	The parameter p represents the minimum probability for a token to be considered, relative to the probability of the most likely token. Range: 0.0-1.0.	float	0.05
typical_p	Sets a minimum likelihood threshold for considering a token. Range: 0.0-1.0.	float	1.0
tfs_z	Tail free sampling reduces the impact of less probable tokens. Higher values diminish this impact.	float	1.0
repeat_penalty	Penalizes repetitions. Higher values increase the penalty. Range: 0.0-2.0.	float	1.0
repeat_last_n	How far back the model checks to prevent repetition. 0 = disabled, -1 = num_ctx.	int	64
presence_penalty	Penalizes new tokens based on their presence so far. Range: 0.0-1.0.	float	0.0
frequency_penalty	Penalizes new tokens based on their frequency so far. Range: 0.0-1.0.	float	0.0
mirostat	Enables Mirostat sampling to control perplexity. 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0.	int	0
mirostat_tau	Balances between coherence and diversity of output. Lower values yield more coherence. Range: 0.0-10.0.	float	5.0
mirostat_eta	Influences response speed to feedback in text generation. Higher rates mean quicker adjustments. Range: 0.0-1.0.	float	0.1
num_keep	Number of tokens to keep unchanged at the beginning of generated text.	int	0
penalize_newline	Whether to penalize the generation of new lines.	bool	True
stop	Triggers the model to stop generating text when this pattern is encountered. List strings separated by “, “.	string Array	empty
seed	Sets the random number seed for generation. Specific numbers ensure reproducibility. -1 = random.	int	-1

Retrieval-Augmented Generation

To retrieve a document and ask questions about it, follow these steps:

Note: It retrieves only snippets of text relevant to your question, so full summaries are not available.

Go to Rag menu > index a URL.
Enter https://bbc.com/.
Wait until the document is indexed.
In the message field, type /q What are some positive news for today? without the quotes. Prefacing your message with /q triggers processing your prompt with RAG using LlamaIndex.
You can also index a folder with documents in it, including all subfolders. It will index all accessible documents, such as PDFs, TXT files, and DOCs.

Rag Settings

This section describes the parameters related to the Retrieval-Augmented Generation (RAG) feature:

Parameter	Description	Value Type	Default Value
show_context	When enabled, displays the text chunks sent to the model.	bool	False
chunk_size	Determines the size of text chunks for indexing.	int	1024
chunk_overlap	Specifies the overlap between the start and end of each chunk.	int	20
similarity_top_k	Number of the most relevant chunks fed to the model.	int	2
similarity_cutoff	The threshold for filtering out less relevant chunks. Setting too high may exclude all chunks.	float	0.0
response_mode	Determines how RAG synthesizes responses.	string	refine

response modes

refine: create and refine an answer by sequentially going through each retrieved text chunk. This makes a separate LLM call per retrieved chunk. Good for more detailed answers.
compact (default): similar to refine but compact the chunks beforehand, resulting in less LLM calls.
tree_summarize: Query the LLM using the summary_template prompt as many times as needed so that all concatenated chunks have been queried, resulting in as many answers that are themselves recursively used as chunks in a tree_summarize LLM call and so on, until there?s only one chunk left, and thus only one final answer.
simple_summarize: Truncates all text chunks to fit into a single LLM prompt. Good for quick summarization purposes, but may lose detail due to truncation.
accumulate: Given a set of text chunks and the query, apply the query to each text chunk while accumulating the responses into an array. Returns a concatenated string of all responses. Good for when you need to run the same query separately against each text chunk.
compact_accumulate: The same as accumulate, but will ?compact? each LLM prompt similar to compact, and run the same query against each text chunk.

This feature allows you to duplicate an existing model via a model file, enabling you to use it as a preset with a different name and parameters (e.g., temperature, repeat penalty, maximum generation length, context length). It does not duplicate the model’s weight files, thus conserving storage space even with multiple duplicates.

For more details, see modelfile.

For Mac users, it is crucial to disable smart quotes before opening the copy model dialog. If your model file displays a left double quotation mark instead of a straight quotation mark, smart quotes are enabled.

MacOS 13 Ventura or later: Go to System Settings > Keyboard > Edit Input Source > turn off smart quotes.
Before Ventura: Navigate to System Preferences > Keyboard > Text > uncheck smart quotes.

Docker (Optional)

If you prefer to run Ollama using Docker, follow the instructions below:

Install Ollama by executing the following command in the command line:

docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama

Download a model to generate text. Replace llama3 with your desired model if you wish to use a different model:

docker exec ollama ollama pull llama3

Optionally, If you wish to use the retrieval-augmented generation feature, download nomic-embed-text for embedding:

docker exec ollama ollama pull nomic-embed-text

To stop Ollama, use the following command:

docker stop ollama

To restart Ollama, use the command below:

docker start ollama

Build from Source

Windows

python -m venv .venv
.venv\Scripts\activate
pip install -r requirements.txt
build-pyinstaller
git apply lib-win.patch
build

Mac

Make sure to use Python 3.12.

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
git apply lib-mac.patch
./build.sh

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