---
title: "Tha Llama Stack"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Tha Llama Stack

The **Llama Stack** is a standardized set of interfaces and components designed to streamline the development and deployment of generative AI applications. It covers the entire lifecycle of AI model development, from training and fine-tuning to product evaluation and invoking AI agents in production³⁴.

Key features of the Llama Stack include:
- **Standardized Toolchain Components**: Provides a consistent framework for building and integrating various AI tools and models.
- **Agentic Applications**: Supports the creation of applications that can autonomously perform tasks based on user inputs.
- **Multimodal Capabilities**: Integrates text and image processing, enabling more versatile AI applications¹².

This stack is particularly useful for developers looking to leverage Meta's Llama models, such as the latest Llama 3.2, which includes advanced features like image reasoning and multilingual support¹².

Is there a specific aspect of the Llama Stack you're interested in?

Source: Conversation with Copilot, 9/26/2024
(1) meta-llama/llama-stack: Model components of the Llama Stack APIs - GitHub. https://github.com/meta-llama/llama-stack.
(2) llama-toolchain - PyPI. https://pypi.org/project/llama-toolchain/.
(3) Introducing Llama 3.2 models from Meta in Amazon Bedrock: A new .... https://aws.amazon.com/blogs/aws/introducing-llama-3-2-models-from-meta-in-amazon-bedrock-a-new-generation-of-multimodal-vision-and-lightweight-models/.
(4) Meta’s new Llama 3.2 models available on Azure AI. https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/meta-s-new-llama-3-2-slms-and-image-reasoning-models-now/ba-p/4255167.
(5) Llama 3.1 - OpenLM.ai. https://openlm.ai/llama-3.1/.

## Download the Model

[On Mac and Linux: How to download and run Llama 3.2 Locally!!!](https://www.youtube.com/watch?v=luGI1NWEAPY)

[Download, Install, and Run Llama 3.2 1B and 3B in Windows Using Ollama](https://www.youtube.com/watch?v=92_yb31Bqzk)

The Llama Stack can help you download and use the Llama 3.2 model. Here are the steps to get started:

1. **Install the Llama Toolchain**:
   - First, you need to install the Llama toolchain using `pip`:
     ```bash
     pip install llama-toolchain
     ```

2. **List Available Models**:
   - Use the Llama CLI to list the available models and find the ID for Llama 3.2:
     ```bash
     llama model list
     ```

3. **Download the Model**:
   - Once you have the model ID, you can download it using the following command:
     ```bash
     llama download --source meta --model-id CHOSEN_MODEL_ID
     ```

You can also access Llama 3.2 models through various platforms like Hugging Face, AWS, and Google Cloud³⁵. Additionally, platforms like Together AI and Azure AI offer APIs for using Llama 3.2 models, which can simplify the integration process¹².

If you need more detailed instructions or run into any issues, feel free to ask!

Source: Conversation with Copilot, 9/26/2024
(1) Llama 3.2: Meta’s New Open-Source Multimodal AI Models. https://fintechs.fi/llama-3-2-metas-new-open-source-multimodal-ai-models/.
(2) Meta Releases Llama 3.2: Lightweight and Multimodal Models. https://www.maginative.com/article/meta-releases-llama-3-2-lightweight-and-multimodal-models/.
(3) Explore the Full Range of Llama 3.2 Models - together.ai. https://www.together.ai/blog/llama-3-2-vision-stack.
(4) Meta’s new Llama 3.2 models available on Azure AI. https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/meta-s-new-llama-3-2-slms-and-image-reasoning-models-now/ba-p/4255167.
(5) llama-models/README.md at main · meta-llama/llama-models - GitHub. https://github.com/meta-llama/llama-models/blob/main/README.md.

## Ollama CLI vs Llama 3.2 Toolchain

To download Llama 3.2 models, both the Ollama CLI and the Llama 3.2 toolchain are viable options, but they serve slightly different purposes:

1. **Ollama CLI**: This is a straightforward way to run and manage Llama models. You can easily download and run Llama 3.2 models using commands like `ollama run llama3.2:3b`¹. It's user-friendly and integrates well with other tools like LangChain and LlamaIndex¹.

2. **Llama 3.2 Toolchain**: This is more comprehensive and might offer more flexibility for advanced users. It includes various tools for downloading, fine-tuning, and deploying Llama models. You can use commands like `llama download` to get the models³.

If you're looking for simplicity and ease of use, the **Ollama CLI** might be the better choice. However, if you need more control and additional features, the **Llama 3.2 toolchain** could be more suitable.

Which one do you think fits your needs better?

Source: Conversation with Copilot, 9/26/2024
(1) Llama 3 · Ollama Blog. https://ollama.com/blog/llama3.
(2) llama-stack/docs/cli_reference.md at main - GitHub. https://github.com/meta-llama/llama-stack/blob/main/docs/cli_reference.md.
(3) Llama 3.2 models from Meta are now available in Amazon SageMaker .... https://aws.amazon.com/blogs/machine-learning/llama-3-2-models-from-meta-are-now-available-in-amazon-sagemaker-jumpstart/.
(4) Llama 3.2 3B Instruct | [Download The New Model Easy Here!]. https://llamaimodel.com/3b/.

## Getting Started

https://github.com/meta-llama/llama-stack/blob/main/docs/getting_started.md


Getting started with the Llama Stack involves several steps. Here's a simple guide to help you begin:

1. **Set Up Your Environment**:
   - Ensure you have a compatible development environment. This typically includes Python, necessary libraries, and a good IDE like VS Code or PyCharm.

2. **Install Required Packages**:
   - Use package managers like `pip` to install the necessary libraries. For example:
     ```bash
     pip install llama-stack
     ```

3. **Access Llama Models**:
   - Obtain access to Meta's Llama models. You might need to sign up for access or download the models from a repository if available.

4. **Initialize the Llama Stack**:
   - Start by initializing the Llama Stack in your project. This might involve setting up configuration files and initializing the stack in your code:
     ```python
     from llama_stack import LlamaStack

     llama = LlamaStack(model='llama-3.2')
     ```

5. **Develop Your Application**:
   - Use the Llama Stack's APIs to build your application. This could involve creating text generation, image processing, or multimodal applications:
     ```python
     response = llama.generate_text("Hello, Llama Stack!")
     print(response)
     ```

6. **Test and Deploy**:
   - Test your application thoroughly to ensure it meets your requirements. Once satisfied, deploy it to your preferred environment, whether it's a local server, cloud service, or integrated into a larger system.

7. **Explore Advanced Features**:
   - Take advantage of advanced features like agentic applications and multimodal capabilities to enhance your application.

If you need more detailed documentation or specific examples, checking the official Llama Stack documentation or community forums can be very helpful.

## Llama CLI

**Llama CLI** is a command-line interface tool designed to fetch and preprocess learning data from various supported learning tools. It's particularly useful for educators and researchers who need to access and analyze raw data generated by digital learning environments².

### Key Features:
- **Data Fetching**: Connects to multiple data sources to fetch learning data.
- **Data Preprocessing**: Prepares data for analysis by anonymizing, excluding unwanted data, and transforming it into usable formats.
- **Privacy Configuration**: Ensures data privacy by configuring pseudo-anonymous settings.

### How to Use Llama CLI:
1. **Installation**:
   - Install via pip:
     ```bash
     python3 -m pip install llama-cli
     ```
   - Alternatively, set up a virtual environment:
     ```bash
     python3 -m venv .venv && .venv/bin/pip install llama-cli
     .venv/bin/llama
     ```

2. **Basic Commands**:
   - **Check Status**:
     ```bash
     llama status
     ```
   - **Manage Data Sources**:
     ```bash
     llama source add
     ```
   - **List Available Data**:
     ```bash
     llama list
     ```
   - **Fetch Data**:
     ```bash
     llama fetch
     ```

3. **Example Workflow**:
   - Add a data source:
     ```bash
     llama source add
     ```
   - List available data tables:
     ```bash
     llama list
     ```
   - Fetch data:
     ```bash
     llama fetch
     ```

This tool operates within the current working directory, storing configurations and data there, similar to how Git repositories work².

Would you like more detailed instructions on a specific feature or command?

Source: Conversation with Copilot, 9/26/2024
(1) llama-cli - PyPI. https://pypi.org/project/llama-cli/.
(2) Ollama. https://ollama.com/.
(3) GitHub - teemulehtinen/llama-cli: Llama CLI fetches and preprocesses .... https://github.com/teemulehtinen/llama-cli.
(4) llama-cli command - github.com/go-skynet/llama-cli - Go Packages. https://pkg.go.dev/github.com/go-skynet/llama-cli.
(5) undefined. https://apluslms.github.io/.
(6) undefined. https://github.com/acos-server/acos-server.
(7) undefined. https://docs.mongodb.com/database-tools/mongodump/.
(8) Step-by-Step Guide: Installing and Using Llama 2 Locally. https://www.youtube.com/watch?v=VYHwRs_RQLU.
(9) Anyone can Fine Tune LLMs using LLaMA Factory: End-to-End Tutorial. https://www.youtube.com/watch?v=iMD7ba1hHgw.
(10) How to Run LLaMA Locally on CPU or GPU | Python & Langchain & CTransformers Guide. https://www.youtube.com/watch?v=SvjWDX2NqiM.
(11) How to Run Llama 3 Locally: A Complete Guide | DataCamp. https://www.datacamp.com/tutorial/run-llama-3-locally.
(12) GitHub - ollama/ollama: Get up and running with Llama 3.1, Mistral .... https://github.com/ollama/ollama.
(13) RAG CLI - LlamaIndex. https://docs.llamaindex.ai/en/stable/getting_started/starter_tools/rag_cli/.
(14) undefined. https://ollama.com/install.sh.
