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Working with Ollama models

This guide provides step-by-step guidance on using Ollama based models in arey. We will download a model with ollama, configure arey to use the model and finally run a few commands.

Llama.cpp is an alternate way to download your models and run them locally. See our Llama guide if you prefer more customizable way to manage your models.

Concepts

Llama is a family of large language models (LLMs) provided by Meta. While this was the initial open weights architecture available for local use, Mistral and their fine-tunes are quite popular too.

LLMs are represented by the number of parameters they were trained with. E.g., 7B, 13B, 33B and 70B are usual buckets. Size of the model increases with the training parameters.

Llama.cpp provides a quantization mechanism (GGUF) to reduce the file size and allows running these models on smaller form factors including CPU-only devices. You will see quantization in the model name. E.g., Q4_K_M implies 4-bit quantization.

Choosing a quantization

Always choose the lower quantization of a higher param model. E.g., Q4_K_M of 13B is better than Q8_K_M of 7B.

Get the models

Ollama maintains a registry of their supported models.

https://www.reddit.com/r/LocalLLaMA/ is a fantastic community to stay updated and learn more about local models.

Our favorite models
Model Parameters Quant Purpose
OpenHermes-2.5-Mistral 7B Q4_K_M General chat
Deepseek-Coder-6.7B 7B Q4_K_M Coding
NousHermes-2-Solar-10.7B 11B Q4_K_M General chat

After you locate the ollama model info, please run it in ollama. In this example, we will try the Tiny Dolphin model.

ollama run tinydolphin

Configure

Let's add an entry for this model in arey's config file.

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# Example configuration for arey app
#
# Looked up from XDG_CONFIG_DIR (~/.config/arey/arey.yml) on Linux or
# ~/.arey on Windows.

models:
  tinydolphin:
    path: ~/models/tinydolphin-2.8-1.1b.Q4_K_M.gguf
    type: llama
    template: chatml
  ollama-tinydolphin:
    name: tinydolphin:latest # name of the model, see http://localhost:11434/api/tags
    type: ollama
    template: chatml

profiles:
  # See https://www.reddit.com/r/LocalLLaMA/comments/1343bgz/what_model_parameters_is_everyone_using/
  precise:
    # factual responses and straightforward assistant answers
    temperature: 0.7
    repeat_penalty: 1.176
    top_k: 40
    top_p: 0.1
  creative:
    # chatting, storywriting, and interesting assistant answers
    temperature: 0.72
    repeat_penalty: 1.1
    top_k: 0
    top_p: 0.73
  sphinx:
    # varied storywriting (on 30B/65B) and unconventional chatting
    temperature: 1.99
    repeat_penalty: 1.15
    top_k: 30
    top_p: 0.18

chat:
  model: ollama-tinydolphin
  profile: precise
task:
  model: ollama-tinydolphin
  profile: precise
  settings:
    host: http://localhost:11434/

Noteworthy changes to the configuration file:

  1. Line 11-14: we added a new model definition with the path of the downloaded model.
  2. Line 38: we instruct arey to use tinydolphin model for chat.
  3. Line 41: arey will use tinydolphin for the queries in ask command.

Usage

You can use chat, ask or play commands to run this model.

Completion settings

You can use profiles to configure arey. A profile is a collection of settings used for tuning the AI model's response. Usually it includes following parameters:

Parameter Value Purpose
num_predict 512 Maximum number of tokens to generate
repeat_penalty 1-2 Higher value discourages repetition of token
stop [] Comma separated list of stop words
temperature 0.0-1.0 Lower temperature implies precise response
top_k 0-30 Number of tokens to consider for sampling
top_p 0.0-1.0 Lower value samples from most likely tokens

See the list of all parameters in Model file API documentation.

Chatting with the model

Let's run arey chat to start a chat session. See below for an illustration.

❯ arey chat
Welcome to arey chat!
Type 'q' to exit.

✓ Model loaded. 0.13s.

How can I help you today?
> Who are you?

I am an artificial intelligence model that has been programmed to simulate human behavior, emotions, and responses based on data gathered from various sources. My primary goal is to provide
assistance in various areas of life, including communication, problem-solving, decision-making, and learning.


◼ Completed. 0.49s to first token. 2.10s total. 75.58 tokens/s. 159 tokens. 64 prompt tokens.
>

See quickstart for an example of ask and play commands.