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16
README.md
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@ -93,12 +93,12 @@ Additionally, we have designed every element of the Stack such that APIs as well
| **Distribution** | **Llama Stack Docker** | Start This Distribution |
|:----------------: |:------------------------------------------: |:-----------------------: |
| Meta Reference | [llamastack/distribution-meta-reference-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/meta-reference-gpu.html) |
| Meta Reference Quantized | [llamastack/distribution-meta-reference-quantized-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-quantized-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/meta-reference-quantized-gpu.html) |
| Ollama | [llamastack/distribution-ollama](https://hub.docker.com/repository/docker/llamastack/distribution-ollama/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/ollama.html) |
| TGI | [llamastack/distribution-tgi](https://hub.docker.com/repository/docker/llamastack/distribution-tgi/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/tgi.html) |
| Together | [llamastack/distribution-together](https://hub.docker.com/repository/docker/llamastack/distribution-together/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/remote_hosted_distro/together.html) |
| Fireworks | [llamastack/distribution-fireworks](https://hub.docker.com/repository/docker/llamastack/distribution-fireworks/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/remote_hosted_distro/fireworks.html) |
| Meta Reference | [llamastack/distribution-meta-reference-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/meta-reference-gpu.html) |
| Meta Reference Quantized | [llamastack/distribution-meta-reference-quantized-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-quantized-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/meta-reference-quantized-gpu.html) |
| Ollama | [llamastack/distribution-ollama](https://hub.docker.com/repository/docker/llamastack/distribution-ollama/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/ollama.html) |
| TGI | [llamastack/distribution-tgi](https://hub.docker.com/repository/docker/llamastack/distribution-tgi/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/tgi.html) |
| Together | [llamastack/distribution-together](https://hub.docker.com/repository/docker/llamastack/distribution-together/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/together.html) |
| Fireworks | [llamastack/distribution-fireworks](https://hub.docker.com/repository/docker/llamastack/distribution-fireworks/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/fireworks.html) |
## Installation
@ -128,7 +128,7 @@ You have two ways to install this repository:
Please checkout our [Documentation](https://llama-stack.readthedocs.io/en/latest/index.html) page for more details.
* [CLI reference](https://llama-stack.readthedocs.io/en/latest/cli_reference/index.html)
* [CLI reference](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/index.html)
* Guide using `llama` CLI to work with Llama models (download, study prompts), and building/starting a Llama Stack distribution.
* [Getting Started](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html)
* Quick guide to start a Llama Stack server.
@ -136,7 +136,7 @@ Please checkout our [Documentation](https://llama-stack.readthedocs.io/en/latest
* The complete Llama Stack lesson [Colab notebook](https://colab.research.google.com/drive/1dtVmxotBsI4cGZQNsJRYPrLiDeT0Wnwt) of the new [Llama 3.2 course on Deeplearning.ai](https://learn.deeplearning.ai/courses/introducing-multimodal-llama-3-2/lesson/8/llama-stack).
* A [Zero-to-Hero Guide](https://github.com/meta-llama/llama-stack/tree/main/docs/zero_to_hero_guide) that guide you through all the key components of llama stack with code samples.
* [Contributing](CONTRIBUTING.md)
* [Adding a new API Provider](https://llama-stack.readthedocs.io/en/latest/api_providers/new_api_provider.html) to walk-through how to add a new API provider.
* [Adding a new API Provider](https://llama-stack.readthedocs.io/en/latest/contributing/new_api_provider.html) to walk-through how to add a new API provider.
## Llama Stack Client SDKs

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@ -8,7 +8,7 @@ This guide contains references to walk you through adding a new API provider.
- {repopath}`Remote Providers::llama_stack/providers/remote`
- {repopath}`Inline Providers::llama_stack/providers/inline`
3. [Build a Llama Stack distribution](https://llama-stack.readthedocs.io/en/latest/distribution_dev/building_distro.html) with your API provider.
3. [Build a Llama Stack distribution](https://llama-stack.readthedocs.io/en/latest/distributions/building_distro.html) with your API provider.
4. Test your code!
## Testing your newly added API providers

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docs/source/distributions/configuration.md
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@ -55,7 +55,7 @@ models:
shields: []
```
Let's break this down into the different sections. It starts by specifying the set of APIs that the stack server will serve:
Let's break this down into the different sections. The first section specifies the set of APIs that the stack server will serve:
```yaml
apis:
- agents
@ -65,7 +65,8 @@ apis:
- telemetry
```
Next up is the most critical section -- the set of providers that the stack will use to serve the above APIs. Let's take the `inference` API as an example:
## Providers
Next up is the most critical part: the set of providers that the stack will use to serve the above APIs. Consider the `inference` API:
```yaml
providers:
inference:
@ -74,8 +75,12 @@ providers:
config:
url: ${env.OLLAMA_URL:http://localhost:11434}
```
A _provider instance_ is identified with an (identifier, type, configuration) tuple. The identifier is a string you can choose freely. You may instantiate any number of provider instances of the same type. The configuration dictionary is provider-specific. Notice that configuration can reference environment variables (with default values), which are expanded at runtime. When you run a stack server (via docker or via `llama stack run`), you can specify `--env OLLAMA_URL=http://my-server:11434` to override the default value.
A few things to note:
- A _provider instance_ is identified with an (identifier, type, configuration) tuple. The identifier is a string you can choose freely.
- You can instantiate any number of provider instances of the same type.
- The configuration dictionary is provider-specific. Notice that configuration can reference environment variables (with default values), which are expanded at runtime. When you run a stack server (via docker or via `llama stack run`), you can specify `--env OLLAMA_URL=http://my-server:11434` to override the default value.
## Resources
Finally, let's look at the `models` section:
```yaml
models:
@ -87,3 +92,73 @@ models:
A Model is an instance of a "Resource" (see [Concepts](../concepts/index)) and is associated with a specific inference provider (in this case, the provider with identifier `ollama`). This is an instance of a "pre-registered" model. While we always encourage the clients to always register models before using them, some Stack servers may come up a list of "already known and available" models.
What's with the `provider_model_id` field? This is an identifier for the model inside the provider's model catalog. Contrast it with `model_id` which is the identifier for the same model for Llama Stack's purposes. For example, you may want to name "llama3.2:vision-11b" as "image_captioning_model" when you use it in your Stack interactions. When omitted, the server will set `provider_model_id` to be the same as `model_id`.
## Extending to handle Safety
Configuring Safety can be a little involved so it is instructive to go through an example.
The Safety API works with the associated Resource called a `Shield`. Providers can support various kinds of Shields. Good examples include the [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) system-safety models, or [Bedrock Guardrails](https://aws.amazon.com/bedrock/guardrails/).
To configure a Bedrock Shield, you would need to add:
- A Safety API provider instance with type `remote::bedrock`
- A Shield resource served by this provider.
```yaml
...
providers:
safety:
- provider_id: bedrock
provider_type: remote::bedrock
config:
aws_access_key_id: ${env.AWS_ACCESS_KEY_ID}
aws_secret_access_key: ${env.AWS_SECRET_ACCESS_KEY}
...
shields:
- provider_id: bedrock
params:
guardrailVersion: ${env.GUARDRAIL_VERSION}
provider_shield_id: ${env.GUARDRAIL_ID}
...
```
The situation is more involved if the Shield needs _Inference_ of an associated model. This is the case with Llama Guard. In that case, you would need to add:
- A Safety API provider instance with type `inline::llama-guard`
- An Inference API provider instance for serving the model.
- A Model resource associated with this provider.
- A Shield resource served by the Safety provider.
The yaml configuration for this setup, assuming you were using vLLM as your inference server, would look like:
```yaml
...
providers:
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
inference:
# this vLLM server serves the "normal" inference model (e.g., llama3.2:3b)
- provider_id: vllm-0
provider_type: remote::vllm
config:
url: ${env.VLLM_URL:http://localhost:8000}
# this vLLM server serves the llama-guard model (e.g., llama-guard:3b)
- provider_id: vllm-1
provider_type: remote::vllm
config:
url: ${env.SAFETY_VLLM_URL:http://localhost:8001}
...
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: vllm-0
provider_model_id: null
- metadata: {}
model_id: ${env.SAFETY_MODEL}
provider_id: vllm-1
provider_model_id: null
shields:
- provider_id: llama-guard
shield_id: ${env.SAFETY_MODEL} # Llama Guard shields are identified by the corresponding LlamaGuard model
provider_shield_id: null
...
```

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@ -36,7 +36,7 @@ The following environment variables can be configured:
## Prerequisite: Downloading Models
Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
```
$ ls ~/.llama/checkpoints

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docs/source/distributions/self_hosted_distro/meta-reference-quantized-gpu.md
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@ -36,7 +36,7 @@ The following environment variables can be configured:
## Prerequisite: Downloading Models
Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
```
$ ls ~/.llama/checkpoints

6
docs/to_situate/developer_cookbook.md
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@ -13,13 +13,13 @@ Based on your developer needs, below are references to guides to help you get st
* Developer Need: I want to start a local Llama Stack server with my GPU using meta-reference implementations.
* Effort: 5min
* Guide:
- Please see our [meta-reference-gpu](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/meta-reference-gpu.html) on starting up a meta-reference Llama Stack server.
- Please see our [meta-reference-gpu](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/meta-reference-gpu.html) on starting up a meta-reference Llama Stack server.
### Llama Stack Server with Remote Providers
* Developer need: I want a Llama Stack distribution with a remote provider.
* Effort: 10min
* Guide
- Please see our [Distributions Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/index.html) on starting up distributions with remote providers.
- Please see our [Distributions Guide](https://llama-stack.readthedocs.io/en/latest/concepts/index.html#distributions) on starting up distributions with remote providers.
### On-Device (iOS) Llama Stack
@ -38,4 +38,4 @@ Based on your developer needs, below are references to guides to help you get st
* Developer Need: I want to add a new API provider to Llama Stack.
* Effort: 3hr
* Guide
- Please see our [Adding a New API Provider](https://llama-stack.readthedocs.io/en/latest/api_providers/new_api_provider.html) guide for adding a new API provider.
- Please see our [Adding a New API Provider](https://llama-stack.readthedocs.io/en/latest/contributing/new_api_provider.html) guide for adding a new API provider.

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@ -231,7 +231,7 @@
"source": [
"Thanks for checking out this notebook! \n",
"\n",
"The next one will be a guide on [Prompt Engineering](./01_Prompt_Engineering101.ipynb), please continue learning!"
"The next one will be a guide on [Prompt Engineering](./02_Prompt_Engineering101.ipynb), please continue learning!"
]
}
],

2
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@ -276,7 +276,7 @@
"source": [
"Thanks for checking out this notebook! \n",
"\n",
"The next one will be a guide on how to chat with images, continue to the notebook [here](./02_Image_Chat101.ipynb). Happy learning!"
"The next one will be a guide on how to chat with images, continue to the notebook [here](./03_Image_Chat101.ipynb). Happy learning!"
]
}
],

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@ -175,7 +175,7 @@
"source": [
"Thanks for checking out this notebook! \n",
"\n",
"The next one in the series will teach you one of the favorite applications of Large Language Models: [Tool Calling](./03_Tool_Calling101.ipynb). Enjoy!"
"The next one in the series will teach you one of the favorite applications of Large Language Models: [Tool Calling](./04_Tool_Calling101.ipynb). Enjoy!"
]
}
],

2
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@ -373,7 +373,7 @@
"source": [
"Awesome, now we can embed all our notes with Llama-stack and ask it about the meaning of life :)\n",
"\n",
"Next up, we will learn about the safety features and how to use them: [notebook link](./05_Safety101.ipynb)"
"Next up, we will learn about the safety features and how to use them: [notebook link](./06_Safety101.ipynb)."
]
}
],

2
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@ -107,7 +107,7 @@
"source": [
"Thanks for leaning about the Safety API of Llama-Stack. \n",
"\n",
"Finally, we learn about the Agents API, [here](./06_Agents101.ipynb)"
"Finally, we learn about the Agents API, [here](./07_Agents101.ipynb)."
]
}
],

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@ -1,37 +1,21 @@
# Llama Stack: from Zero to Hero
Llama-Stack allows you to configure your distribution from various providers, allowing you to focus on going from zero to production super fast.
Llama Stack defines and standardizes the set of core building blocks needed to bring generative AI applications to market. These building blocks are presented in the form of interoperable APIs with a broad set of Providers providing their implementations. These building blocks are assembled into Distributions which are easy for developers to get from zero to production.
This guide will walk you through how to build a local distribution, using Ollama as an inference provider.
This guide will walk you through an end-to-end workflow with Llama Stack with Ollama as the inference provider and ChromaDB as the memory provider. Please note the steps for configuring your provider and distribution will vary a little depending on the services you use. However, the user experience will remain universal - this is the power of Llama-Stack.
We also have a set of notebooks walking you through how to use Llama-Stack APIs:
If you're looking for more specific topics, we have a [Zero to Hero Guide](#next-steps) that covers everything from Tool Calling to Agents in detail. Feel free to skip to the end to explore the advanced topics you're interested in.
- Inference
- Prompt Engineering
- Chatting with Images
- Tool Calling
- Memory API for RAG
- Safety API
- Agentic API
Below, we will learn how to get started with Ollama as an inference provider, please note the steps for configuring your provider will vary a little depending on the service. However, the user experience will remain universal-this is the power of Llama-Stack.
Prototype locally using Ollama, deploy to the cloud with your favorite provider or own deployment. Use any API from any provider while focussing on development.
# Ollama Quickstart Guide
This guide will walk you through setting up an end-to-end workflow with Llama Stack with ollama, enabling you to perform text generation using the `Llama3.2-3B-Instruct` model. Follow these steps to get started quickly.
If you're looking for more specific topics like tool calling or agent setup, we have a [Zero to Hero Guide](#next-steps) that covers everything from Tool Calling to Agents in detail. Feel free to skip to the end to explore the advanced topics you're interested in.
> If you'd prefer not to set up a local server, explore our notebook on [tool calling with the Together API](Tool_Calling101_Using_Together's_Llama_Stack_Server.ipynb). This guide will show you how to leverage Together.ai's Llama Stack Server API, allowing you to get started with Llama Stack without the need for a locally built and running server.
> If you'd prefer not to set up a local server, explore our notebook on [tool calling with the Together API](Tool_Calling101_Using_Together's_Llama_Stack_Server.ipynb). This notebook will show you how to leverage together.ai's Llama Stack Server API, allowing you to get started with Llama Stack without the need for a locally built and running server.
## Table of Contents
1. [Setup ollama](#setup-ollama)
1. [Setup and run ollama](#setup-ollama)
2. [Install Dependencies and Set Up Environment](#install-dependencies-and-set-up-environment)
3. [Build, Configure, and Run Llama Stack](#build-configure-and-run-llama-stack)
4. [Run Ollama Model](#run-ollama-model)
5. [Next Steps](#next-steps)
4. [Test with llama-stack-client CLI](#test-with-llama-stack-client-cli)
5. [Test with curl](#test-with-curl)
6. [Test with Python](#test-with-python)
7. [Next Steps](#next-steps)
---
@ -39,107 +23,137 @@ If you're looking for more specific topics like tool calling or agent setup, we
1. **Download Ollama App**:
- Go to [https://ollama.com/download](https://ollama.com/download).
- Download and unzip `Ollama-darwin.zip`.
- Follow instructions based on the OS you are on. For example, if you are on a Mac, download and unzip `Ollama-darwin.zip`.
- Run the `Ollama` application.
1. **Download the Ollama CLI**:
- Ensure you have the `ollama` command line tool by downloading and installing it from the same website.
Ensure you have the `ollama` command line tool by downloading and installing it from the same website.
1. **Start ollama server**:
- Open the terminal and run:
```
ollama serve
```
Open the terminal and run:
```
ollama serve
```
1. **Run the model**:
- Open the terminal and run:
```bash
ollama run llama3.2:3b-instruct-fp16
```
**Note**: The supported models for llama stack for now is listed in [here](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/ollama/ollama.py#L43)
Open the terminal and run:
```bash
ollama run llama3.2:3b-instruct-fp16 --keepalive -1m
```
**Note**:
- The supported models for llama stack for now is listed in [here](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/ollama/ollama.py#L43)
- `keepalive -1m` is used so that ollama continues to keep the model in memory indefinitely. Otherwise, ollama frees up memory and you would have to run `ollama run` again.
---
## Install Dependencies and Set Up Environment
1. **Create a Conda Environment**:
- Create a new Conda environment with Python 3.10:
```bash
conda create -n ollama python=3.10
```
- Activate the environment:
```bash
conda activate ollama
```
Create a new Conda environment with Python 3.10:
```bash
conda create -n ollama python=3.10
```
Activate the environment:
```bash
conda activate ollama
```
2. **Install ChromaDB**:
- Install `chromadb` using `pip`:
```bash
pip install chromadb
```
Install `chromadb` using `pip`:
```bash
pip install chromadb
```
3. **Run ChromaDB**:
- Start the ChromaDB server:
```bash
chroma run --host localhost --port 8000 --path ./my_chroma_data
```
Start the ChromaDB server:
```bash
chroma run --host localhost --port 8000 --path ./my_chroma_data
```
4. **Install Llama Stack**:
- Open a new terminal and install `llama-stack`:
```bash
conda activate hack
pip install llama-stack==0.0.53
```
Open a new terminal and install `llama-stack`:
```bash
conda activate ollama
pip install llama-stack==0.0.55
```
---
## Build, Configure, and Run Llama Stack
1. **Build the Llama Stack**:
- Build the Llama Stack using the `ollama` template:
```bash
llama stack build --template ollama --image-type conda
```
After this step, you will see the console output:
```
Build Successful! Next steps:
Build the Llama Stack using the `ollama` template:
```bash
llama stack build --template ollama --image-type conda
```
**Expected Output:**
```
...
Build Successful! Next steps:
1. Set the environment variables: LLAMASTACK_PORT, OLLAMA_URL, INFERENCE_MODEL, SAFETY_MODEL
2. `llama stack run /Users/username/.llama/distributions/llamastack-ollama/ollama-run.yaml`
```
2. `llama stack run /Users/<username>/.llama/distributions/llamastack-ollama/ollama-run.yaml
```
2. **Set the ENV variables by exporting them to the terminal**:
```bash
export OLLAMA_URL="http://localhost:11434"
export LLAMA_STACK_PORT=5001
export INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct"
export SAFETY_MODEL="meta-llama/Llama-Guard-3-1B"
```
3. **Set the ENV variables by exporting them to the terminal**:
```bash
export OLLAMA_URL="http://localhost:11434"
export LLAMA_STACK_PORT=5051
export INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct"
export SAFETY_MODEL="meta-llama/Llama-Guard-3-1B"
```
3. **Run the Llama Stack**:
- Run the stack with command shared by the API from earlier:
```bash
llama stack run ollama \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env OLLAMA_URL=http://localhost:11434
```
Note: Everytime you run a new model with `ollama run`, you will need to restart the llama stack. Otherwise it won't see the new model
Run the stack with command shared by the API from earlier:
```bash
llama stack run ollama \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env OLLAMA_URL=$OLLAMA_URL
```
Note: Everytime you run a new model with `ollama run`, you will need to restart the llama stack. Otherwise it won't see the new model.
The server will start and listen on `http://localhost:5051`.
---
## Test with `llama-stack-client` CLI
After setting up the server, open a new terminal window and install the llama-stack-client package.
## Testing with `curl`
1. Install the llama-stack-client package
```bash
conda activate ollama
pip install llama-stack-client
```
2. Configure the CLI to point to the llama-stack server.
```bash
llama-stack-client configure --endpoint http://localhost:5051
```
**Expected Output:**
```bash
Done! You can now use the Llama Stack Client CLI with endpoint http://localhost:5051
```
3. Test the CLI by running inference:
```bash
llama-stack-client inference chat-completion --message "Write me a 2-sentence poem about the moon"
```
**Expected Output:**
```bash
ChatCompletionResponse(
completion_message=CompletionMessage(
content='Here is a 2-sentence poem about the moon:\n\nSilver crescent shining bright in the night,\nA beacon of wonder, full of gentle light.',
role='assistant',
stop_reason='end_of_turn',
tool_calls=[]
),
logprobs=None
)
```
## Test with `curl`
After setting up the server, open a new terminal window and verify it's working by sending a `POST` request using `curl`:
```bash
curl http://localhost:5051/inference/chat_completion \
curl http://localhost:$LLAMA_STACK_PORT/inference/chat_completion \
-H "Content-Type: application/json" \
-d '{
"model": "Llama3.2-3B-Instruct",
@ -168,15 +182,16 @@ You can check the available models with the command `llama-stack-client models l
---
## Testing with Python
## Test with Python
You can also interact with the Llama Stack server using a simple Python script. Below is an example:
### 1. Active Conda Environment and Install Required Python Packages
### 1. Activate Conda Environment and Install Required Python Packages
The `llama-stack-client` library offers a robust and efficient python methods for interacting with the Llama Stack server.
```bash
conda activate your-llama-stack-conda-env
conda activate ollama
pip install llama-stack-client
```
Note, the client library gets installed by default if you install the server library
@ -188,6 +203,8 @@ touch test_llama_stack.py
### 3. Create a Chat Completion Request in Python
In `test_llama_stack.py`, write the following code:
```python
from llama_stack_client import LlamaStackClient
@ -227,15 +244,15 @@ This command initializes the model to interact with your local Llama Stack insta
## Next Steps
**Explore Other Guides**: Dive deeper into specific topics by following these guides:
- [Understanding Distribution](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html#decide-your-inference-provider)
- [Understanding Distribution](https://llama-stack.readthedocs.io/en/latest/concepts/index.html#distributions)
- [Inference 101](00_Inference101.ipynb)
- [Local and Cloud Model Toggling 101](00_Local_Cloud_Inference101.ipynb)
- [Prompt Engineering](01_Prompt_Engineering101.ipynb)
- [Chat with Image - LlamaStack Vision API](02_Image_Chat101.ipynb)
- [Tool Calling: How to and Details](03_Tool_Calling101.ipynb)
- [Memory API: Show Simple In-Memory Retrieval](04_Memory101.ipynb)
- [Using Safety API in Conversation](05_Safety101.ipynb)
- [Agents API: Explain Components](06_Agents101.ipynb)
- [Local and Cloud Model Toggling 101](01_Local_Cloud_Inference101.ipynb)
- [Prompt Engineering](02_Prompt_Engineering101.ipynb)
- [Chat with Image - LlamaStack Vision API](03_Image_Chat101.ipynb)
- [Tool Calling: How to and Details](04_Tool_Calling101.ipynb)
- [Memory API: Show Simple In-Memory Retrieval](05_Memory101.ipynb)
- [Using Safety API in Conversation](06_Safety101.ipynb)
- [Agents API: Explain Components](07_Agents101.ipynb)
**Explore Client SDKs**: Utilize our client SDKs for various languages to integrate Llama Stack into your applications:
@ -244,7 +261,7 @@ This command initializes the model to interact with your local Llama Stack insta
- [Swift SDK](https://github.com/meta-llama/llama-stack-client-swift)
- [Kotlin SDK](https://github.com/meta-llama/llama-stack-client-kotlin)
**Advanced Configuration**: Learn how to customize your Llama Stack distribution by referring to the [Building a Llama Stack Distribution](https://llama-stack.readthedocs.io/en/latest/distributions/index.html#building-your-own-distribution) guide.
**Advanced Configuration**: Learn how to customize your Llama Stack distribution by referring to the [Building a Llama Stack Distribution](https://llama-stack.readthedocs.io/en/latest/distributions/building_distro.html) guide.
**Explore Example Apps**: Check out [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) for example applications built using Llama Stack.

2
llama_stack/distribution/request_headers.py
View file

@ -35,7 +35,7 @@ class NeedsRequestProviderData:
provider_data = validator(**val)
return provider_data
except Exception as e:
log.error("Error parsing provider data", e)
log.error(f"Error parsing provider data: {e}")
def set_request_provider_data(headers: Dict[str, str]):

11
llama_stack/distribution/ui/README.md Normal file
View file

@ -0,0 +1,11 @@
# LLama Stack UI
[!NOTE] This is a work in progress.
## Running Streamlit App
```
cd llama_stack/distribution/ui
pip install -r requirements.txt
streamlit run app.py
```

5
llama_stack/distribution/ui/__init__.py Normal file
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@ -0,0 +1,5 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

173
llama_stack/distribution/ui/app.py Normal file
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@ -0,0 +1,173 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import json
import pandas as pd
import streamlit as st
from modules.api import LlamaStackEvaluation
from modules.utils import process_dataset
EVALUATION_API = LlamaStackEvaluation()
def main():
# Add collapsible sidebar
with st.sidebar:
# Add collapse button
if "sidebar_state" not in st.session_state:
st.session_state.sidebar_state = True
if st.session_state.sidebar_state:
st.title("Navigation")
page = st.radio(
"Select a Page",
["Application Evaluation"],
index=0,
)
else:
page = "Application Evaluation" # Default page when sidebar is collapsed
# Main content area
st.title("🦙 Llama Stack Evaluations")
if page == "Application Evaluation":
application_evaluation_page()
def application_evaluation_page():
# File uploader
uploaded_file = st.file_uploader("Upload Dataset", type=["csv", "xlsx", "xls"])
if uploaded_file is None:
st.error("No file uploaded")
return
# Process uploaded file
df = process_dataset(uploaded_file)
if df is None:
st.error("Error processing file")
return
# Display dataset information
st.success("Dataset loaded successfully!")
# Display dataframe preview
st.subheader("Dataset Preview")
st.dataframe(df)
# Select Scoring Functions to Run Evaluation On
st.subheader("Select Scoring Functions")
scoring_functions = EVALUATION_API.list_scoring_functions()
scoring_functions = {sf.identifier: sf for sf in scoring_functions}
scoring_functions_names = list(scoring_functions.keys())
selected_scoring_functions = st.multiselect(
"Choose one or more scoring functions",
options=scoring_functions_names,
help="Choose one or more scoring functions.",
)
available_models = EVALUATION_API.list_models()
available_models = [m.identifier for m in available_models]
scoring_params = {}
if selected_scoring_functions:
st.write("Selected:")
for scoring_fn_id in selected_scoring_functions:
scoring_fn = scoring_functions[scoring_fn_id]
st.write(f"- **{scoring_fn_id}**: {scoring_fn.description}")
new_params = None
if scoring_fn.params:
new_params = {}
for param_name, param_value in scoring_fn.params.to_dict().items():
if param_name == "type":
new_params[param_name] = param_value
continue
if param_name == "judge_model":
value = st.selectbox(
f"Select **{param_name}** for {scoring_fn_id}",
options=available_models,
index=0,
key=f"{scoring_fn_id}_{param_name}",
)
new_params[param_name] = value
else:
value = st.text_area(
f"Enter value for **{param_name}** in {scoring_fn_id} in valid JSON format",
value=json.dumps(param_value, indent=2),
height=80,
)
try:
new_params[param_name] = json.loads(value)
except json.JSONDecodeError:
st.error(
f"Invalid JSON for **{param_name}** in {scoring_fn_id}"
)
st.json(new_params)
scoring_params[scoring_fn_id] = new_params
# Add run evaluation button & slider
total_rows = len(df)
num_rows = st.slider("Number of rows to evaluate", 1, total_rows, total_rows)
if st.button("Run Evaluation"):
progress_text = "Running evaluation..."
progress_bar = st.progress(0, text=progress_text)
rows = df.to_dict(orient="records")
if num_rows < total_rows:
rows = rows[:num_rows]
# Create separate containers for progress text and results
progress_text_container = st.empty()
results_container = st.empty()
output_res = {}
for i, r in enumerate(rows):
# Update progress
progress = i / len(rows)
progress_bar.progress(progress, text=progress_text)
# Run evaluation for current row
score_res = EVALUATION_API.run_scoring(
r,
scoring_function_ids=selected_scoring_functions,
scoring_params=scoring_params,
)
for k in r.keys():
if k not in output_res:
output_res[k] = []
output_res[k].append(r[k])
for fn_id in selected_scoring_functions:
if fn_id not in output_res:
output_res[fn_id] = []
output_res[fn_id].append(score_res.results[fn_id].score_rows[0])
# Display current row results using separate containers
progress_text_container.write(
f"Expand to see current processed result ({i+1}/{len(rows)})"
)
results_container.json(
score_res.to_json(),
expanded=2,
)
progress_bar.progress(1.0, text="Evaluation complete!")
# Display results in dataframe
if output_res:
output_df = pd.DataFrame(output_res)
st.subheader("Evaluation Results")
st.dataframe(output_df)
if __name__ == "__main__":
main()

41
llama_stack/distribution/ui/modules/api.py Normal file
View file

@ -0,0 +1,41 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import os
from typing import Optional
from llama_stack_client import LlamaStackClient
class LlamaStackEvaluation:
def __init__(self):
self.client = LlamaStackClient(
base_url=os.environ.get("LLAMA_STACK_ENDPOINT", "http://localhost:5000"),
provider_data={
"fireworks_api_key": os.environ.get("FIREWORKS_API_KEY", ""),
"together_api_key": os.environ.get("TOGETHER_API_KEY", ""),
"openai_api_key": os.environ.get("OPENAI_API_KEY", ""),
},
)
def list_scoring_functions(self):
"""List all available scoring functions"""
return self.client.scoring_functions.list()
def list_models(self):
"""List all available judge models"""
return self.client.models.list()
def run_scoring(
self, row, scoring_function_ids: list[str], scoring_params: Optional[dict]
):
"""Run scoring on a single row"""
if not scoring_params:
scoring_params = {fn_id: None for fn_id in scoring_function_ids}
return self.client.scoring.score(
input_rows=[row], scoring_functions=scoring_params
)

31
llama_stack/distribution/ui/modules/utils.py Normal file
View file

@ -0,0 +1,31 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import os
import pandas as pd
import streamlit as st
def process_dataset(file):
if file is None:
return "No file uploaded", None
try:
# Determine file type and read accordingly
file_ext = os.path.splitext(file.name)[1].lower()
if file_ext == ".csv":
df = pd.read_csv(file)
elif file_ext in [".xlsx", ".xls"]:
df = pd.read_excel(file)
else:
return "Unsupported file format. Please upload a CSV or Excel file.", None
return df
except Exception as e:
st.error(f"Error processing file: {str(e)}")
return None

3
llama_stack/distribution/ui/requirements.txt Normal file
View file

@ -0,0 +1,3 @@
streamlit
pandas
llama-stack-client>=0.0.55

5
llama_stack/distribution/utils/model_utils.py
View file

@ -4,11 +4,10 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import os
from pathlib import Path
from .config_dirs import DEFAULT_CHECKPOINT_DIR
def model_local_dir(descriptor: str) -> str:
path = os.path.join(DEFAULT_CHECKPOINT_DIR, descriptor)
return path.replace(":", "-")
return str(Path(DEFAULT_CHECKPOINT_DIR) / (descriptor.replace(":", "-")))

5
llama_stack/providers/inline/datasetio/__init__.py Normal file
View file

@ -0,0 +1,5 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

5
llama_stack/providers/inline/scoring/braintrust/__init__.py
View file

@ -6,10 +6,15 @@
from typing import Dict
from llama_stack.distribution.datatypes import Api, ProviderSpec
from pydantic import BaseModel
from .config import BraintrustScoringConfig
class BraintrustProviderDataValidator(BaseModel):
openai_api_key: str
async def get_provider_impl(
config: BraintrustScoringConfig,
deps: Dict[Api, ProviderSpec],

23
llama_stack/providers/inline/scoring/braintrust/braintrust.py
View file

@ -12,9 +12,11 @@ from llama_stack.apis.common.type_system import * # noqa: F403
from llama_stack.apis.datasetio import * # noqa: F403
from llama_stack.apis.datasets import * # noqa: F403
# from .scoring_fn.braintrust_scoring_fn import BraintrustScoringFn
import os
from autoevals.llm import Factuality
from autoevals.ragas import AnswerCorrectness
from llama_stack.distribution.request_headers import NeedsRequestProviderData
from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
from llama_stack.providers.utils.scoring.aggregation_utils import aggregate_average
@ -24,7 +26,9 @@ from .scoring_fn.fn_defs.answer_correctness import answer_correctness_fn_def
from .scoring_fn.fn_defs.factuality import factuality_fn_def
class BraintrustScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
class BraintrustScoringImpl(
Scoring, ScoringFunctionsProtocolPrivate, NeedsRequestProviderData
):
def __init__(
self,
config: BraintrustScoringConfig,
@ -79,12 +83,25 @@ class BraintrustScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
f"Dataset {dataset_id} does not have a '{required_column}' column of type 'string'."
)
async def set_api_key(self) -> None:
# api key is in the request headers
if self.config.openai_api_key is None:
provider_data = self.get_request_provider_data()
if provider_data is None or not provider_data.openai_api_key:
raise ValueError(
'Pass OpenAI API Key in the header X-LlamaStack-ProviderData as { "openai_api_key": <your api key>}'
)
self.config.openai_api_key = provider_data.openai_api_key
os.environ["OPENAI_API_KEY"] = self.config.openai_api_key
async def score_batch(
self,
dataset_id: str,
scoring_functions: List[str],
save_results_dataset: bool = False,
) -> ScoreBatchResponse:
await self.set_api_key()
await self.validate_scoring_input_dataset_schema(dataset_id=dataset_id)
all_rows = await self.datasetio_api.get_rows_paginated(
dataset_id=dataset_id,
@ -105,6 +122,7 @@ class BraintrustScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
async def score_row(
self, input_row: Dict[str, Any], scoring_fn_identifier: Optional[str] = None
) -> ScoringResultRow:
await self.set_api_key()
assert scoring_fn_identifier is not None, "scoring_fn_identifier cannot be None"
expected_answer = input_row["expected_answer"]
generated_answer = input_row["generated_answer"]
@ -118,6 +136,7 @@ class BraintrustScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
async def score(
self, input_rows: List[Dict[str, Any]], scoring_functions: List[str]
) -> ScoreResponse:
await self.set_api_key()
res = {}
for scoring_fn_id in scoring_functions:
if scoring_fn_id not in self.supported_fn_defs_registry:

6
llama_stack/providers/inline/scoring/braintrust/config.py
View file

@ -6,4 +6,8 @@
from llama_stack.apis.scoring import * # noqa: F401, F403
class BraintrustScoringConfig(BaseModel): ...
class BraintrustScoringConfig(BaseModel):
openai_api_key: Optional[str] = Field(
default=None,
description="The OpenAI API Key",
)

2
llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/answer_correctness.py
View file

@ -10,7 +10,7 @@ from llama_stack.apis.scoring_functions import ScoringFn
answer_correctness_fn_def = ScoringFn(
identifier="braintrust::answer-correctness",
description="Test whether an output is factual, compared to an original (`expected`) value. One of Braintrust LLM basd scorer https://github.com/braintrustdata/autoevals/blob/main/py/autoevals/llm.py",
description="Scores the correctness of the answer based on the ground truth.. One of Braintrust LLM basd scorer https://github.com/braintrustdata/autoevals/blob/main/py/autoevals/llm.py",
params=None,
provider_id="braintrust",
provider_resource_id="answer-correctness",

1
llama_stack/providers/registry/scoring.py
View file

@ -44,5 +44,6 @@ def available_providers() -> List[ProviderSpec]:
Api.datasetio,
Api.datasets,
],
provider_data_validator="llama_stack.providers.inline.scoring.braintrust.BraintrustProviderDataValidator",
),
]

5
llama_stack/providers/remote/datasetio/__init__.py Normal file
View file

@ -0,0 +1,5 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

3
llama_stack/providers/remote/datasetio/huggingface/config.py
View file

@ -3,12 +3,13 @@
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from pydantic import BaseModel
from llama_stack.distribution.utils.config_dirs import RUNTIME_BASE_DIR
from llama_stack.providers.utils.kvstore.config import (
KVStoreConfig,
SqliteKVStoreConfig,
)
from pydantic import BaseModel
class HuggingfaceDatasetIOConfig(BaseModel):

1
llama_stack/providers/remote/datasetio/huggingface/huggingface.py
View file

@ -9,6 +9,7 @@ from llama_stack.apis.datasetio import * # noqa: F403
import datasets as hf_datasets
from llama_stack.providers.datatypes import DatasetsProtocolPrivate
from llama_stack.providers.utils.datasetio.url_utils import get_dataframe_from_url
from llama_stack.providers.utils.kvstore import kvstore_impl

4
llama_stack/providers/remote/inference/nvidia/config.py
View file

@ -35,7 +35,9 @@ class NVIDIAConfig(BaseModel):
"""
url: str = Field(
default="https://integrate.api.nvidia.com",
default_factory=lambda: os.getenv(
"NVIDIA_BASE_URL", "https://integrate.api.nvidia.com"
),
description="A base url for accessing the NVIDIA NIM",
)
api_key: Optional[str] = Field(

8
llama_stack/providers/remote/inference/tgi/tgi.py
View file

@ -89,8 +89,9 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> AsyncGenerator:
model = await self.model_store.get_model(model_id)
request = CompletionRequest(
model=model_id,
model=model.provider_resource_id,
content=content,
sampling_params=sampling_params,
response_format=response_format,
@ -194,8 +195,9 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> AsyncGenerator:
model = await self.model_store.get_model(model_id)
request = ChatCompletionRequest(
model=model_id,
model=model.provider_resource_id,
messages=messages,
sampling_params=sampling_params,
tools=tools or [],
@ -249,7 +251,7 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
def _get_params(self, request: ChatCompletionRequest) -> dict:
prompt, input_tokens = chat_completion_request_to_model_input_info(
request, self.formatter
request, self.register_helper.get_llama_model(request.model), self.formatter
)
return dict(
prompt=prompt,

16
llama_stack/providers/tests/eval/conftest.py
View file

@ -6,10 +6,14 @@
import pytest
from ..agents.fixtures import AGENTS_FIXTURES
from ..conftest import get_provider_fixture_overrides
from ..datasetio.fixtures import DATASETIO_FIXTURES
from ..inference.fixtures import INFERENCE_FIXTURES
from ..memory.fixtures import MEMORY_FIXTURES
from ..safety.fixtures import SAFETY_FIXTURES
from ..scoring.fixtures import SCORING_FIXTURES
from .fixtures import EVAL_FIXTURES
@ -20,6 +24,9 @@ DEFAULT_PROVIDER_COMBINATIONS = [
"scoring": "basic",
"datasetio": "localfs",
"inference": "fireworks",
"agents": "meta_reference",
"safety": "llama_guard",
"memory": "faiss",
},
id="meta_reference_eval_fireworks_inference",
marks=pytest.mark.meta_reference_eval_fireworks_inference,
@ -30,6 +37,9 @@ DEFAULT_PROVIDER_COMBINATIONS = [
"scoring": "basic",
"datasetio": "localfs",
"inference": "together",
"agents": "meta_reference",
"safety": "llama_guard",
"memory": "faiss",
},
id="meta_reference_eval_together_inference",
marks=pytest.mark.meta_reference_eval_together_inference,
@ -40,6 +50,9 @@ DEFAULT_PROVIDER_COMBINATIONS = [
"scoring": "basic",
"datasetio": "huggingface",
"inference": "together",
"agents": "meta_reference",
"safety": "llama_guard",
"memory": "faiss",
},
id="meta_reference_eval_together_inference_huggingface_datasetio",
marks=pytest.mark.meta_reference_eval_together_inference_huggingface_datasetio,
@ -75,6 +88,9 @@ def pytest_generate_tests(metafunc):
"scoring": SCORING_FIXTURES,
"datasetio": DATASETIO_FIXTURES,
"inference": INFERENCE_FIXTURES,
"agents": AGENTS_FIXTURES,
"safety": SAFETY_FIXTURES,
"memory": MEMORY_FIXTURES,
}
combinations = (
get_provider_fixture_overrides(metafunc.config, available_fixtures)

20
llama_stack/providers/tests/eval/fixtures.py
View file

@ -40,14 +40,30 @@ async def eval_stack(request):
providers = {}
provider_data = {}
for key in ["datasetio", "eval", "scoring", "inference"]:
for key in [
"datasetio",
"eval",
"scoring",
"inference",
"agents",
"safety",
"memory",
]:
fixture = request.getfixturevalue(f"{key}_{fixture_dict[key]}")
providers[key] = fixture.providers
if fixture.provider_data:
provider_data.update(fixture.provider_data)
test_stack = await construct_stack_for_test(
[Api.eval, Api.datasetio, Api.inference, Api.scoring],
[
Api.eval,
Api.datasetio,
Api.inference,
Api.scoring,
Api.agents,
Api.safety,
Api.memory,
],
providers,
provider_data,
)

18
llama_stack/providers/tests/inference/fixtures.py
View file

@ -20,6 +20,7 @@ from llama_stack.providers.remote.inference.bedrock import BedrockConfig
from llama_stack.providers.remote.inference.fireworks import FireworksImplConfig
from llama_stack.providers.remote.inference.nvidia import NVIDIAConfig
from llama_stack.providers.remote.inference.ollama import OllamaImplConfig
from llama_stack.providers.remote.inference.tgi import TGIImplConfig
from llama_stack.providers.remote.inference.together import TogetherImplConfig
from llama_stack.providers.remote.inference.vllm import VLLMInferenceAdapterConfig
from llama_stack.providers.remote.inference.sambanova import SambanovaImplConfig
@ -172,6 +173,22 @@ def inference_sambanova() -> ProviderFixture:
)
@pytest.fixture(scope="session")
def inference_tgi() -> ProviderFixture:
return ProviderFixture(
providers=[
Provider(
provider_id="tgi",
provider_type="remote::tgi",
config=TGIImplConfig(
url=get_env_or_fail("TGI_URL"),
api_token=os.getenv("TGI_API_TOKEN", None),
).model_dump(),
)
],
)
def get_model_short_name(model_name: str) -> str:
"""Convert model name to a short test identifier.
@ -207,6 +224,7 @@ INFERENCE_FIXTURES = [
"bedrock",
"nvidia",
"sambanova",
"tgi",
]

7
llama_stack/providers/tests/scoring/fixtures.py
View file

@ -10,9 +10,10 @@ import pytest_asyncio
from llama_stack.apis.models import ModelInput
from llama_stack.distribution.datatypes import Api, Provider
from llama_stack.providers.inline.scoring.braintrust import BraintrustScoringConfig
from llama_stack.providers.tests.resolver import construct_stack_for_test
from ..conftest import ProviderFixture, remote_stack_fixture
from ..env import get_env_or_fail
@pytest.fixture(scope="session")
@ -40,7 +41,9 @@ def scoring_braintrust() -> ProviderFixture:
Provider(
provider_id="braintrust",
provider_type="inline::braintrust",
config={},
config=BraintrustScoringConfig(
openai_api_key=get_env_or_fail("OPENAI_API_KEY"),
).model_dump(),
)
],
)

5
llama_stack/providers/utils/scoring/__init__.py Normal file
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@ -0,0 +1,5 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

2
llama_stack/templates/meta-reference-gpu/doc_template.md
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@ -29,7 +29,7 @@ The following environment variables can be configured:
## Prerequisite: Downloading Models
Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
```
$ ls ~/.llama/checkpoints

2
llama_stack/templates/meta-reference-quantized-gpu/doc_template.md
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@ -31,7 +31,7 @@ The following environment variables can be configured:
## Prerequisite: Downloading Models
Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
```
$ ls ~/.llama/checkpoints

4
requirements.txt
View file

@ -2,8 +2,8 @@ blobfile
fire
httpx
huggingface-hub
llama-models>=0.0.55
llama-stack-client>=0.0.55
llama-models>=0.0.56
llama-stack-client>=0.0.56
prompt-toolkit
python-dotenv
pydantic>=2

2
setup.py
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@ -16,7 +16,7 @@ def read_requirements():
setup(
name="llama_stack",
version="0.0.55",
version="0.0.56",
author="Meta Llama",
author_email="llama-oss@meta.com",
description="Llama Stack",