This repo allows you to run Llama 3.1 as a system capable of performing "agentic" tasks like:
- Breaking a task down and performing multi-step reasoning.
- Ability to use tools
- built-in: the model has built-in knowledge of tools like search or code interpreter
- zero-shot: the model can learn to call tools using previously unseen, in-context tool definitions
Additionally, we would like to shift safety evaluation from the model level to the overall system level. This allows the underlying model to remain broadly steerable and adaptable to use cases which need varying levels of safety protection.
One of the safety protections is provided by Llama Guard. By default, Llama Guard is used for both input and output filtering. However, the system can be configured to modify this default setting. For example, it is recommended to use Llama Guard for output filtering in situations where refusals to benign prompts are frequently observed, as long as safety requirements are met for your use case.
Note
The API is still evolving and may change. Feel free to build and experiment, but please don't rely on its stability just yet!
An agentic app requires a few components:
- ability to run inference on the underlying Llama series of models
- ability to run safety checks using the Llama-Guard series of models
- ability to execute tools, including a code execution environment, and loop using the model's multi-step reasoning process
The Llama Stack defines and standardizes these components and many others that are needed to make building Generative AI applications smoother. Various implementations of these APIs are then conveniently assembled together via a Llama Stack Distribution.
To get started with Distributions, you'll need to:
- Install any prerequisites
- Setup the toolchain which provides the core
llamaCLI - Download the models
- Install the distribution
- Start the distribution server
Once started, you can then point your agentic app to the URL for this server (e.g. http://localhost:5000) and make magic happen.
Let's go through these steps in detail now:
Python Packages
We recommend creating an isolated conda Python environment.
# Create and activate a virtual environment
ENV=agentic_env
conda create -n $ENV python=3.10
cd <path-to-llama-agentic-system-repo>
conda activate $ENV
# Install dependencies
pip install -r requirements.txtYou should now be able to run llama --help:
usage: llama [-h] {download,distribution,model} ...
Welcome to the LLama cli
options:
-h, --help show this help message and exit
subcommands:
{download,distribution,model}bubblewrap
The code execution environment uses bubblewrap for isolation. This may already be installed on your system; if not, it's likely in your OS's package repository.
Ollama (optional)
If you plan to use Ollama for inference, you'll need to install the server via these instructions.
Downloading from Meta
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 Meta-Llama3.1-8B-Instruct --meta-url META_URL
# you can also get the 70B model, this will require 8 GPUs however
llama download --source meta --model-id Meta-Llama3.1-70B-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-3-8B --meta-url META_URLFor all the above, you will need to provide a URL (META_URL) which can be obtained from https://llama.meta.com/llama-downloads/ after signing an agreement.
Downloading from Huggingface
Essentially, the same commands above work, just replace --source meta with --source huggingface.
llama download --source huggingface --model-id Meta-Llama3.1-8B-Instruct --hf-token <HF_TOKEN>
llama download --source huggingface --model-id Meta-Llama3.1-70B-Instruct --hf-token <HF_TOKEN>
llama download --source huggingface --model-id Llama-Guard-3-8B --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.
Tip: Default for
llama downloadis to run with--ignore-patterns *.safetensorssince we use the.pthfiles in theoriginalfolder. For Llama Guard and Prompt Guard, however, we need safetensors. Hence, please run with--ignore-patterns originalso that safetensors are downloaded and.pthfiles are ignored.
If you're already using ollama, we also have a supported distribuiton local-ollama and you can continue to use ollama for managing model downloads.
ollama pull llama3.1:8b-instruct-fp16
ollama pull llama3.1:70b-instruct-fp16
Note
Only the above two models are currently supported.
Note
local distribution has only been tested on linux as of now.
For other platforms (ubuntu, mac) try using the local-ollama distro and install platform specific ollama.
Let’s start with listing available distributions
$ llama distribution list
+---------------+---------------------------------------------+----------------------------------------------------------------------+
| Spec ID | ProviderSpecs | Description |
+---------------+---------------------------------------------+----------------------------------------------------------------------+
| local | { | Use code from `llama_toolchain` itself to serve all llama stack APIs |
| | "inference": "meta-reference", | |
| | "safety": "meta-reference", | |
| | "agentic_system": "meta-reference" | |
| | } | |
+---------------+---------------------------------------------+----------------------------------------------------------------------+
| remote | { | Point to remote services for all llama stack APIs |
| | "inference": "inference-remote", | |
| | "safety": "safety-remote", | |
| | "agentic_system": "agentic_system-remote" | |
| | } | |
+---------------+---------------------------------------------+----------------------------------------------------------------------+
| local-ollama | { | Like local-source, but use ollama for running LLM inference |
| | "inference": "meta-ollama", | |
| | "safety": "meta-reference", | |
| | "agentic_system": "meta-reference" | |
| | } | |
+---------------+---------------------------------------------+----------------------------------------------------------------------+
As you can see above, each “spec” details the “providers” that make up that spec. For eg. The local uses the “meta-reference” provider for inference while the local-ollama relies on a different provider (Ollama) for inference.
At this point, we don't recommend using the remote distribution since there are no remote providers supporting the Llama Stack APIs yet.
To install a distro, we run a simple command providing 2 inputs:
- Spec Id of the distribution that we want to install ( as obtained from the list command )
- A Name by which this installation will be known locally.
Let's imagine you are working with a 8B-Instruct model, so we will name our local installation as local-llama-8b. The following command will both install and configure the distribution. As part of the configuration, you will be asked for some inputs (model_id, max_seq_len, etc.)
llama distribution install --spec local --name local-llama-8b
Once it runs successfully , you should see some outputs in the form:
$ llama distribution install --spec local --name local-llama-8b
....
....
Successfully installed cfgv-3.4.0 distlib-0.3.8 identify-2.6.0 libcst-1.4.0 llama_toolchain-0.0.2 moreorless-0.4.0 nodeenv-1.9.1 pre-commit-3.8.0 stdlibs-2024.5.15 toml-0.10.2 tomlkit-0.13.0 trailrunner-1.4.0 ufmt-2.7.0 usort-1.0.8 virtualenv-20.26.3
Distribution `local-llama-8b` (with spec local) has been installed successfully!
You can re-configure this distribution by running:
llama distribution configure --name local-llama-8b
Here is an example run of how the CLI will guide you to fill the configuration
$ llama distribution configure --name local-llama-8b
Configuring API surface: inference
Enter value for model (required): Meta-Llama3.1-8B-Instruct
Enter value for quantization (optional):
Enter value for torch_seed (optional):
Enter value for max_seq_len (required): 4096
Enter value for max_batch_size (default: 1): 1
Configuring API surface: safety
Do you want to configure llama_guard_shield? (y/n): y
Entering sub-configuration for llama_guard_shield:
Enter value for model (required): Llama-Guard-3-8B
Enter value for excluded_categories (required): []
Enter value for disable_input_check (default: False):
Enter value for disable_output_check (default: False):
Do you want to configure prompt_guard_shield? (y/n): y
Entering sub-configuration for prompt_guard_shield:
Enter value for model (required): Prompt-Guard-86M
Configuring API surface: agentic_system
YAML configuration has been written to /home/ashwin/.llama/distributions/i0/config.yaml
As you can see, we did basic configuration above and configured:
- inference to run on model
Meta-Llama3.1-8B-Instruct(obtained fromllama model list) - Llama Guard safety shield with model
Llama-Guard-3-8B - Prompt Guard safety shield with model
Prompt-Guard-86M
For how these configurations are stored as yaml, checkout the file printed at the end of the configuration.
Note that all configurations as well as models are stored in ~/.llama
On one terminal, start ollama server using
ollama serve
Note
In the server logs, you should see messages of the form msg="llama runner started in xx seconds" suggesting that the models are ready for inference.
You can test your ollama setup via
ollama run llama3.1:8b-instruct-fp16
Now, install the llama stack distribution:
llama distribution install --spec local-ollama --name ollama
Now let’s start the distribution using the CLI. You will require the name of the distribution that was used for installation / configuration.
llama distribution start --name local-llama-8b --port 5000
You should see the distribution start and print the APIs that it is supporting,
$ llama distribution start --name local-llama-8b --port 5000
> initializing model parallel with size 1
> initializing ddp with size 1
> initializing pipeline with size 1
Loaded in 19.28 seconds
NCCL version 2.20.5+cuda12.4
Finished model load YES READY
Serving POST /inference/batch_chat_completion
Serving POST /inference/batch_completion
Serving POST /inference/chat_completion
Serving POST /inference/completion
Serving POST /safety/run_shields
Serving POST /agentic_system/memory_bank/attach
Serving POST /agentic_system/create
Serving POST /agentic_system/session/create
Serving POST /agentic_system/turn/create
Serving POST /agentic_system/delete
Serving POST /agentic_system/session/delete
Serving POST /agentic_system/memory_bank/detach
Serving POST /agentic_system/session/get
Serving POST /agentic_system/step/get
Serving POST /agentic_system/turn/get
Listening on :::5000
INFO: Started server process [453333]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://[::]:5000 (Press CTRL+C to quit)
Note
Configuration is in ~/.llama/distributions/local-llama-8b/config.yaml. Feel free to increase max_seq_len.
Important
The "local" distribution inference server currently only supports CUDA. It will not work on Apple Silicon machines.
This server is running a Llama model locally.
Tip
You might need to use the flag --disable-ipv6 to Disable IPv6 support
Now that the Distribution server is setup, the next thing would be to run an agentic app using AgenticSystem APIs.
We have built sample scripts, notebooks and a UI chat interface ( using Mesop ! ) to help you get started.
If you want to use tools, you must create a .env file in your repo root directory and add API Keys for tools. Once you do that, you will need to restart the distribution server.
Tools that the model supports and which need API Keys --
- Brave for web search (https://api.search.brave.com/register)
- Wolfram for math operations (https://developer.wolframalpha.com/)
Tip If you do not have API keys, you can still run the app without model having access to the tools.
Start an app (local) and interact with it by running the following command:
mesop app/main.pyThis will start a mesop app and you can go to localhost:32123 to play with the chat interface.
Optionally, you can setup API keys for custom tools:
- WolframAlpha: store in
WOLFRAM_ALPHA_API_KEYenvironment variable - Brave Search: store in
BRAVE_SEARCH_API_KEYenvironment variable
Similar to this main app, you can also try other variants
PYTHONPATH=. mesop app/chat_with_custom_tools.pyto showcase how custom tools are integratedPYTHONPATH=. mesop app/chat_moderation_with_llama_guard.pyto showcase how the app is modified to act as a chat moderator for safety
NOTE: Ensure that Distribution server is still running.
cd <path-to-llama-agentic-system>
conda activate $ENV
llama distribution start --name local-llama-8b --port 5000 # If not already started
PYTHONPATH=. python examples/scripts/vacation.py localhost 5000You should see outputs to stdout of the form --
Environment: ipython
Tools: brave_search, wolfram_alpha, photogen
Cutting Knowledge Date: December 2023
Today Date: 23 July 2024
User> I am planning a trip to Switzerland, what are the top 3 places to visit?
Final Llama Guard response shield_type=<BuiltinShield.llama_guard: 'llama_guard'> is_violation=False violation_type=None violation_return_message=None
Ran PromptGuardShield and got Scores: Embedded: 0.9999765157699585, Malicious: 1.1110752893728204e-05
StepType.shield_call> No Violation
role='user' content='I am planning a trip to Switzerland, what are the top 3 places to visit?'
StepType.inference> Switzerland is a beautiful country with a rich history, culture, and natural beauty. Here are three must-visit places to add to your itinerary: ....
Tip You can optionally do
--disable-safetyin the scripts to avoid running safety shields all the time.
Feel free to reach out if you have questions.
Note
While you can run the apps using venv, installation of a distribution requires conda.
# Create and activate a virtual environment
python3 -m venv venv
source venv/bin/activate# Create and activate a virtual environment
python -m venv venv
venv\Scripts\activate # For Command Prompt
# or
.\venv\Scripts\Activate.ps1 # For PowerShell
# or
source venv\Scripts\activate # For GitThe instructions thereafter (including pip install -r requirements.txt for installing the dependencies) remain the same.