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llama (server-side) CLI Reference

The llama CLI tool helps you set up and use the Llama Stack. The CLI is available on your path after installing the llama-stack package.

Installation

You have two ways to install Llama Stack:

  1. Install as a package: You can install the repository directly from PyPI by running the following command:

    pip install llama-stack

  2. Install from source: If you prefer to install from the source code, follow these steps:

     mkdir -p ~/local
 cd ~/local
 git clone git@github.com:meta-llama/llama-stack.git

 uv venv myenv --python 3.12
 source myenv/bin/activate  # On Windows: myenv\Scripts\activate

 cd llama-stack
 pip install -e .

llama subcommands

  1. download: Supports downloading models from Meta or Hugging Face. Downloading models
  2. model: Lists available models and their properties. Understanding models
  3. stack: Allows you to build a stack using the llama stack distribution and run a Llama Stack server. You can read more about how to build a Llama Stack distribution in the Build your own Distribution documentation.

Sample Usage

llama --help

usage: llama [-h] {download,model,stack} ...

Welcome to the Llama CLI

options:
  -h, --help            show this help message and exit

subcommands:
  {download,model,stack}

Downloading models

You first need to have models downloaded locally.

To download any model you need the Model Descriptor. This can be obtained by running the command

llama model list

You should see a table like this:

+----------------------------------+------------------------------------------+----------------+
| Model Descriptor(ID)             | Hugging Face Repo                        | Context Length |
+----------------------------------+------------------------------------------+----------------+
| Llama3.1-8B                      | meta-llama/Llama-3.1-8B                  | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama3.1-70B                     | meta-llama/Llama-3.1-70B                 | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama3.1-405B:bf16-mp8           | meta-llama/Llama-3.1-405B                | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama3.1-405B                    | meta-llama/Llama-3.1-405B-FP8            | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama3.1-405B:bf16-mp16          | meta-llama/Llama-3.1-405B                | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama3.1-8B-Instruct             | meta-llama/Llama-3.1-8B-Instruct         | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama3.1-70B-Instruct            | meta-llama/Llama-3.1-70B-Instruct        | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama3.1-405B-Instruct:bf16-mp8  | meta-llama/Llama-3.1-405B-Instruct       | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama3.1-405B-Instruct           | meta-llama/Llama-3.1-405B-Instruct-FP8   | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama3.1-405B-Instruct:bf16-mp16 | meta-llama/Llama-3.1-405B-Instruct       | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama3.2-1B                      | meta-llama/Llama-3.2-1B                  | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama3.2-3B                      | meta-llama/Llama-3.2-3B                  | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama3.2-11B-Vision              | meta-llama/Llama-3.2-11B-Vision          | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama3.2-90B-Vision              | meta-llama/Llama-3.2-90B-Vision          | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama3.2-1B-Instruct             | meta-llama/Llama-3.2-1B-Instruct         | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama3.2-3B-Instruct             | meta-llama/Llama-3.2-3B-Instruct         | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama3.2-11B-Vision-Instruct     | meta-llama/Llama-3.2-11B-Vision-Instruct | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama3.2-90B-Vision-Instruct     | meta-llama/Llama-3.2-90B-Vision-Instruct | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama-Guard-3-11B-Vision         | meta-llama/Llama-Guard-3-11B-Vision      | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama-Guard-3-1B:int4-mp1        | meta-llama/Llama-Guard-3-1B-INT4         | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama-Guard-3-1B                 | meta-llama/Llama-Guard-3-1B              | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama-Guard-3-8B                 | meta-llama/Llama-Guard-3-8B              | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama-Guard-3-8B:int8-mp1        | meta-llama/Llama-Guard-3-8B-INT8         | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Prompt-Guard-86M                 | meta-llama/Prompt-Guard-86M              | 128K           |
+----------------------------------+------------------------------------------+----------------+
| Llama-Guard-2-8B                 | meta-llama/Llama-Guard-2-8B              | 4K             |
+----------------------------------+------------------------------------------+----------------+

To download models, you can use the llama download command.

Downloading from Meta

Here is an example download command to get the 3B-Instruct/11B-Vision-Instruct model. You will need META_URL which can be obtained from here

Download the required checkpoints using the following commands:

# download the 8B model, this can be run on a single GPU
llama download --source meta --model-id Llama3.2-3B-Instruct --meta-url META_URL

# you can also get the 70B model, this will require 8 GPUs however
llama download --source meta --model-id Llama3.2-11B-Vision-Instruct --meta-url META_URL

# llama-agents have safety enabled by default. For this, you will need
# safety models -- Llama-Guard and Prompt-Guard
llama download --source meta --model-id Prompt-Guard-86M --meta-url META_URL
llama download --source meta --model-id Llama-Guard-3-1B --meta-url META_URL

Downloading from Hugging Face

Essentially, the same commands above work, just replace --source meta with --source huggingface.

llama download --source huggingface --model-id  Llama3.1-8B-Instruct --hf-token <HF_TOKEN>

llama download --source huggingface --model-id Llama3.1-70B-Instruct --hf-token <HF_TOKEN>

llama download --source huggingface --model-id Llama-Guard-3-1B --ignore-patterns *original*
llama download --source huggingface --model-id Prompt-Guard-86M --ignore-patterns *original*

Important: Set your environment variable HF_TOKEN or pass in --hf-token to the command to validate your access. You can find your token at https://huggingface.co/settings/tokens.

Default for `llama download` is to run with `--ignore-patterns *.safetensors` since we use the `.pth` files in the `original` folder. For Llama Guard and Prompt Guard, however, we need safetensors. Hence, please run with `--ignore-patterns original` so that safetensors are downloaded and `.pth` files are ignored.

List the downloaded models

To list the downloaded models with the following command:

llama model list --downloaded

You should see a table like this:

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
┃ Model                                   ┃ Size     ┃ Modified Time       ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
│ Llama3.2-1B-Instruct:int4-qlora-eo8     │ 1.53 GB  │ 2025-02-26 11:22:28 │
├─────────────────────────────────────────┼──────────┼─────────────────────┤
│ Llama3.2-1B                             │ 2.31 GB  │ 2025-02-18 21:48:52 │
├─────────────────────────────────────────┼──────────┼─────────────────────┤
│ Prompt-Guard-86M                        │ 0.02 GB  │ 2025-02-26 11:29:28 │
├─────────────────────────────────────────┼──────────┼─────────────────────┤
│ Llama3.2-3B-Instruct:int4-spinquant-eo8 │ 3.69 GB  │ 2025-02-26 11:37:41 │
├─────────────────────────────────────────┼──────────┼─────────────────────┤
│ Llama3.2-3B                             │ 5.99 GB  │ 2025-02-18 21:51:26 │
├─────────────────────────────────────────┼──────────┼─────────────────────┤
│ Llama3.1-8B                             │ 14.97 GB │ 2025-02-16 10:36:37 │
├─────────────────────────────────────────┼──────────┼─────────────────────┤
│ Llama3.2-1B-Instruct:int4-spinquant-eo8 │ 1.51 GB  │ 2025-02-26 11:35:02 │
├─────────────────────────────────────────┼──────────┼─────────────────────┤
│ Llama-Guard-3-1B                        │ 2.80 GB  │ 2025-02-26 11:20:46 │
├─────────────────────────────────────────┼──────────┼─────────────────────┤
│ Llama-Guard-3-1B:int4                   │ 0.43 GB  │ 2025-02-26 11:33:33 │
└─────────────────────────────────────────┴──────────┴─────────────────────┘

Understand the models

The llama model command helps you explore the model’s interface.

  1. download: Download the model from different sources. (meta, huggingface)
  2. list: Lists all the models available for download with hardware requirements for deploying the models.
  3. prompt-format: Show llama model message formats.
  4. describe: Describes all the properties of the model.

Sample Usage

llama model <subcommand> <options>

llama model --help

usage: llama model [-h] {download,list,prompt-format,describe,verify-download,remove} ...

Work with llama models

options:
  -h, --help            show this help message and exit

model_subcommands:
  {download,list,prompt-format,describe,verify-download,remove}

Describe

You can use the describe command to know more about a model:

llama model describe -m Llama3.2-3B-Instruct

+-----------------------------+----------------------------------+
| Model                       | Llama3.2-3B-Instruct             |
+-----------------------------+----------------------------------+
| Hugging Face ID             | meta-llama/Llama-3.2-3B-Instruct |
+-----------------------------+----------------------------------+
| Description                 | Llama 3.2 3b instruct model      |
+-----------------------------+----------------------------------+
| Context Length              | 128K tokens                      |
+-----------------------------+----------------------------------+
| Weights format              | bf16                             |
+-----------------------------+----------------------------------+
| Model params.json           | {                                |
|                             |     "dim": 3072,                 |
|                             |     "n_layers": 28,              |
|                             |     "n_heads": 24,               |
|                             |     "n_kv_heads": 8,             |
|                             |     "vocab_size": 128256,        |
|                             |     "ffn_dim_multiplier": 1.0,   |
|                             |     "multiple_of": 256,          |
|                             |     "norm_eps": 1e-05,           |
|                             |     "rope_theta": 500000.0,      |
|                             |     "use_scaled_rope": true      |
|                             | }                                |
+-----------------------------+----------------------------------+
| Recommended sampling params | {                                |
|                             |     "temperature": 1.0,          |
|                             |     "top_p": 0.9,                |
|                             |     "top_k": 0                   |
|                             | }                                |
+-----------------------------+----------------------------------+

Prompt Format

You can even run llama model prompt-format see all of the templates and their tokens:

llama model prompt-format -m Llama3.2-3B-Instruct

alt text

You will be shown a Markdown formatted description of the model interface and how prompts / messages are formatted for various scenarios.

NOTE: Outputs in terminal are color printed to show special tokens.

Remove model

You can run llama model remove to remove an unnecessary model:

llama model remove -m Llama-Guard-3-8B-int8