phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-06-27 18:50:41 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

feat: consolidate most distros into "starter"

Browse source 
* Removes a bunch of distros
* Removed distros were added into the "starter" distribution
* Doc for "starter" has been added
* Partially reverts https://github.com/meta-llama/llama-stack/pull/2482
  since inference providers are disabled by default and can be turned on
  manually via env variable.
* Disables safety in starter distro

Closes: #2502
Signed-off-by: Sébastien Han <seb@redhat.com>
This commit is contained in:
Sébastien Han 2025-06-25 16:09:41 +02:00
parent 0ddb293d77
commit bedfea38c3
No known key found for this signature in database
127 changed files with 758 additions and 10771 deletions
Download patch file Download diff file Expand all files Collapse all files

13
.github/workflows/integration-tests.yml vendored
View file

@ -43,14 +43,17 @@ jobs:
- name: Build Llama Stack
run: |
uv run llama stack build --template ollama --image-type venv
uv run llama stack build --template starter --image-type venv
- name: Start Llama Stack server in background
if: matrix.client-type == 'http'
env:
INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct"
OLLAMA_INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct"
ENABLE_OLLAMA: "ollama"
OLLAMA_URL: "http://0.0.0.0:11434"
run: |
LLAMA_STACK_LOG_FILE=server.log nohup uv run llama stack run ./llama_stack/templates/ollama/run.yaml --image-type venv --env OLLAMA_URL="http://0.0.0.0:11434" &
LLAMA_STACK_LOG_FILE=server.log nohup uv run llama stack run ./llama_stack/templates/starter/run.yaml --image-type venv &
- name: Wait for Llama Stack server to be ready
if: matrix.client-type == 'http'
@ -87,16 +90,18 @@ jobs:
- name: Run Integration Tests
env:
INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct"
OLLAMA_INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct" # for library tests
ENABLE_OLLAMA: "ollama" # for library tests
OLLAMA_URL: "http://0.0.0.0:11434"
run: |
if [ "${{ matrix.client-type }}" == "library" ]; then
stack_config="ollama"
stack_config="starter"
else
stack_config="http://localhost:8321"
fi
uv run pytest -s -v tests/integration/${{ matrix.test-type }} --stack-config=${stack_config} \
-k "not(builtin_tool or safety_with_image or code_interpreter or test_rag)" \
--text-model="meta-llama/Llama-3.2-3B-Instruct" \
--text-model="ollama/meta-llama/Llama-3.2-3B-Instruct" \
--embedding-model=all-MiniLM-L6-v2
- name: Check Storage and Memory Available After Tests

8
README.md
View file

@ -139,13 +139,7 @@ A Llama Stack Distribution (or "distro") is a pre-configured bundle of provider
| **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/distributions/self_hosted_distro/meta-reference-gpu.html) |
| SambaNova | [llamastack/distribution-sambanova](https://hub.docker.com/repository/docker/llamastack/distribution-sambanova/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/sambanova.html) |
| Cerebras | [llamastack/distribution-cerebras](https://hub.docker.com/repository/docker/llamastack/distribution-cerebras/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/cerebras.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) |
| vLLM | [llamastack/distribution-remote-vllm](https://hub.docker.com/repository/docker/llamastack/distribution-remote-vllm/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/remote-vllm.html) |
| Starter Distribution | [llamastack/distribution-starter](https://hub.docker.com/repository/docker/llamastack/distribution-starter/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/starter.html) |
### Documentation

61
docs/source/distributions/building_distro.md
View file

@ -85,45 +85,13 @@ The following command will allow you to see the available templates and their co
llama stack build --list-templates
```
```
------------------------------+-----------------------------------------------------------------------------+
| Template Name | Description |
+------------------------------+-----------------------------------------------------------------------------+
| hf-serverless | Use (an external) Hugging Face Inference Endpoint for running LLM inference |
+------------------------------+-----------------------------------------------------------------------------+
| together | Use Together.AI for running LLM inference |
+------------------------------+-----------------------------------------------------------------------------+
| vllm-gpu | Use a built-in vLLM engine for running LLM inference |
+------------------------------+-----------------------------------------------------------------------------+
| experimental-post-training | Experimental template for post training |
+------------------------------+-----------------------------------------------------------------------------+
| remote-vllm | Use (an external) vLLM server for running LLM inference |
+------------------------------+-----------------------------------------------------------------------------+
| fireworks | Use Fireworks.AI for running LLM inference |
+------------------------------+-----------------------------------------------------------------------------+
| tgi | Use (an external) TGI server for running LLM inference |
+------------------------------+-----------------------------------------------------------------------------+
| bedrock | Use AWS Bedrock for running LLM inference and safety |
+------------------------------+-----------------------------------------------------------------------------+
| meta-reference-gpu | Use Meta Reference for running LLM inference |
+------------------------------+-----------------------------------------------------------------------------+
| nvidia | Use NVIDIA NIM for running LLM inference |
+------------------------------+-----------------------------------------------------------------------------+
| cerebras | Use Cerebras for running LLM inference |
+------------------------------+-----------------------------------------------------------------------------+
| ollama | Use (an external) Ollama server for running LLM inference |
+------------------------------+-----------------------------------------------------------------------------+
| hf-endpoint | Use (an external) Hugging Face Inference Endpoint for running LLM inference |
+------------------------------+-----------------------------------------------------------------------------+
```
You may then pick a template to build your distribution with providers fitted to your liking.
For example, to build a distribution with TGI as the inference provider, you can run:
```
$ llama stack build --template tgi
$ llama stack build --template starter
...
You can now edit ~/.llama/distributions/llamastack-tgi/tgi-run.yaml and run `llama stack run ~/.llama/distributions/llamastack-tgi/tgi-run.yaml`
You can now edit ~/.llama/distributions/llamastack-starter/starter-run.yaml and run `llama stack run ~/.llama/distributions/llamastack-starter/starter-run.yaml`
```
:::
:::{tab-item} Building from Scratch
@ -163,26 +131,7 @@ You can now edit ~/.llama/distributions/llamastack-my-local-stack/my-local-stack
- The config file will be of contents like the ones in `llama_stack/templates/*build.yaml`.
```
$ cat llama_stack/templates/ollama/build.yaml
name: ollama
distribution_spec:
description: Like local, but use ollama for running LLM inference
providers:
inference: remote::ollama
memory: inline::faiss
safety: inline::llama-guard
agents: inline::meta-reference
telemetry: inline::meta-reference
image_name: ollama
image_type: conda
# If some providers are external, you can specify the path to the implementation
external_providers_dir: ~/.llama/providers.d
```
```
llama stack build --config llama_stack/templates/ollama/build.yaml
llama stack build --config llama_stack/templates/starter/build.yaml
```
:::
@ -248,11 +197,11 @@ Podman is supported as an alternative to Docker. Set `CONTAINER_BINARY` to `podm
To build a container image, you may start off from a template and use the `--image-type container` flag to specify `container` as the build image type.
```
llama stack build --template ollama --image-type container
llama stack build --template starter --image-type container
```
```
$ llama stack build --template ollama --image-type container
$ llama stack build --template starter --image-type container
...
Containerfile created successfully in /tmp/tmp.viA3a3Rdsg/ContainerfileFROM python:3.10-slim
...

2
docs/source/distributions/importing_as_library.md
View file

@ -6,7 +6,7 @@ This avoids the overhead of setting up a server.
```bash
# setup
uv pip install llama-stack
llama stack build --template ollama --image-type venv
llama stack build --template starter --image-type venv
```
```python

79
docs/source/distributions/self_hosted_distro/bedrock.md
View file

@ -1,79 +0,0 @@
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
# Bedrock Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-bedrock` distribution consists of the following provider configurations:
| API | Provider(s) |
|-----|-------------|
| agents | `inline::meta-reference` |
| datasetio | `remote::huggingface`, `inline::localfs` |
| eval | `inline::meta-reference` |
| inference | `remote::bedrock` |
| safety | `remote::bedrock` |
| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
| telemetry | `inline::meta-reference` |
| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` |
| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
### Environment Variables
The following environment variables can be configured:
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
### Models
The following models are available by default:
- `meta.llama3-1-8b-instruct-v1:0 (aliases: meta-llama/Llama-3.1-8B-Instruct)`
- `meta.llama3-1-70b-instruct-v1:0 (aliases: meta-llama/Llama-3.1-70B-Instruct)`
- `meta.llama3-1-405b-instruct-v1:0 (aliases: meta-llama/Llama-3.1-405B-Instruct-FP8)`
### Prerequisite: API Keys
Make sure you have access to a AWS Bedrock API Key. You can get one by visiting [AWS Bedrock](https://aws.amazon.com/bedrock/).
## Running Llama Stack with AWS Bedrock
You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
llamastack/distribution-bedrock \
--port $LLAMA_STACK_PORT \
--env AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
--env AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
--env AWS_SESSION_TOKEN=$AWS_SESSION_TOKEN \
--env AWS_DEFAULT_REGION=$AWS_DEFAULT_REGION
```
### Via Conda
```bash
llama stack build --template bedrock --image-type conda
llama stack run ./run.yaml \
--port $LLAMA_STACK_PORT \
--env AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
--env AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
--env AWS_SESSION_TOKEN=$AWS_SESSION_TOKEN \
--env AWS_DEFAULT_REGION=$AWS_DEFAULT_REGION
```

67
docs/source/distributions/self_hosted_distro/cerebras.md
View file

@ -1,67 +0,0 @@
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
# Cerebras Distribution
The `llamastack/distribution-cerebras` distribution consists of the following provider configurations.
| API | Provider(s) |
|-----|-------------|
| agents | `inline::meta-reference` |
| datasetio | `remote::huggingface`, `inline::localfs` |
| eval | `inline::meta-reference` |
| inference | `remote::cerebras`, `inline::sentence-transformers` |
| safety | `inline::llama-guard` |
| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
| telemetry | `inline::meta-reference` |
| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime` |
| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
### Environment Variables
The following environment variables can be configured:
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `CEREBRAS_API_KEY`: Cerebras API Key (default: ``)
### Models
The following models are available by default:
- `llama3.1-8b (aliases: meta-llama/Llama-3.1-8B-Instruct)`
- `llama-3.3-70b (aliases: meta-llama/Llama-3.3-70B-Instruct)`
### Prerequisite: API Keys
Make sure you have access to a Cerebras API Key. You can get one by visiting [cloud.cerebras.ai](https://cloud.cerebras.ai/).
## Running Llama Stack with Cerebras
You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-cerebras \
--config /root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env CEREBRAS_API_KEY=$CEREBRAS_API_KEY
```
### Via Conda
```bash
llama stack build --template cerebras --image-type conda
llama stack run ./run.yaml \
--port 8321 \
--env CEREBRAS_API_KEY=$CEREBRAS_API_KEY
```

86
docs/source/distributions/self_hosted_distro/fireworks.md
View file

@ -1,86 +0,0 @@
---
orphan: true
---
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
# Fireworks Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-fireworks` distribution consists of the following provider configurations.
| API | Provider(s) |
|-----|-------------|
| agents | `inline::meta-reference` |
| datasetio | `remote::huggingface`, `inline::localfs` |
| eval | `inline::meta-reference` |
| files | `inline::localfs` |
| inference | `remote::fireworks`, `inline::sentence-transformers` |
| safety | `inline::llama-guard` |
| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
| telemetry | `inline::meta-reference` |
| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::rag-runtime`, `remote::model-context-protocol` |
| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
### Environment Variables
The following environment variables can be configured:
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `FIREWORKS_API_KEY`: Fireworks.AI API Key (default: ``)
### Models
The following models are available by default:
- `accounts/fireworks/models/llama-v3p1-8b-instruct (aliases: meta-llama/Llama-3.1-8B-Instruct)`
- `accounts/fireworks/models/llama-v3p1-70b-instruct (aliases: meta-llama/Llama-3.1-70B-Instruct)`
- `accounts/fireworks/models/llama-v3p1-405b-instruct (aliases: meta-llama/Llama-3.1-405B-Instruct-FP8)`
- `accounts/fireworks/models/llama-v3p2-3b-instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)`
- `accounts/fireworks/models/llama-v3p2-11b-vision-instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)`
- `accounts/fireworks/models/llama-v3p2-90b-vision-instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)`
- `accounts/fireworks/models/llama-v3p3-70b-instruct (aliases: meta-llama/Llama-3.3-70B-Instruct)`
- `accounts/fireworks/models/llama-guard-3-8b (aliases: meta-llama/Llama-Guard-3-8B)`
- `accounts/fireworks/models/llama-guard-3-11b-vision (aliases: meta-llama/Llama-Guard-3-11B-Vision)`
- `accounts/fireworks/models/llama4-scout-instruct-basic (aliases: meta-llama/Llama-4-Scout-17B-16E-Instruct)`
- `accounts/fireworks/models/llama4-maverick-instruct-basic (aliases: meta-llama/Llama-4-Maverick-17B-128E-Instruct)`
- `nomic-ai/nomic-embed-text-v1.5 `
### Prerequisite: API Keys
Make sure you have access to a Fireworks API Key. You can get one by visiting [fireworks.ai](https://fireworks.ai/).
## Running Llama Stack with Fireworks
You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
llamastack/distribution-fireworks \
--port $LLAMA_STACK_PORT \
--env FIREWORKS_API_KEY=$FIREWORKS_API_KEY
```
### Via Conda
```bash
llama stack build --template fireworks --image-type conda
llama stack run ./run.yaml \
--port $LLAMA_STACK_PORT \
--env FIREWORKS_API_KEY=$FIREWORKS_API_KEY
```

82
docs/source/distributions/self_hosted_distro/groq.md
View file

@ -1,82 +0,0 @@
---
orphan: true
---
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
# Groq Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-groq` distribution consists of the following provider configurations.
| API | Provider(s) |
|-----|-------------|
| agents | `inline::meta-reference` |
| datasetio | `remote::huggingface`, `inline::localfs` |
| eval | `inline::meta-reference` |
| inference | `remote::groq` |
| safety | `inline::llama-guard` |
| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
| telemetry | `inline::meta-reference` |
| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime` |
| vector_io | `inline::faiss` |
### Environment Variables
The following environment variables can be configured:
- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `GROQ_API_KEY`: Groq API Key (default: ``)
### Models
The following models are available by default:
- `groq/llama3-8b-8192 (aliases: meta-llama/Llama-3.1-8B-Instruct)`
- `groq/llama-3.1-8b-instant `
- `groq/llama3-70b-8192 (aliases: meta-llama/Llama-3-70B-Instruct)`
- `groq/llama-3.3-70b-versatile (aliases: meta-llama/Llama-3.3-70B-Instruct)`
- `groq/llama-3.2-3b-preview (aliases: meta-llama/Llama-3.2-3B-Instruct)`
- `groq/llama-4-scout-17b-16e-instruct (aliases: meta-llama/Llama-4-Scout-17B-16E-Instruct)`
- `groq/meta-llama/llama-4-scout-17b-16e-instruct (aliases: meta-llama/Llama-4-Scout-17B-16E-Instruct)`
- `groq/llama-4-maverick-17b-128e-instruct (aliases: meta-llama/Llama-4-Maverick-17B-128E-Instruct)`
- `groq/meta-llama/llama-4-maverick-17b-128e-instruct (aliases: meta-llama/Llama-4-Maverick-17B-128E-Instruct)`
### Prerequisite: API Keys
Make sure you have access to a Groq API Key. You can get one by visiting [Groq](https://api.groq.com/).
## Running Llama Stack with Groq
You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
llamastack/distribution-groq \
--port $LLAMA_STACK_PORT \
--env GROQ_API_KEY=$GROQ_API_KEY
```
### Via Conda
```bash
llama stack build --template groq --image-type conda
llama stack run ./run.yaml \
--port $LLAMA_STACK_PORT \
--env GROQ_API_KEY=$GROQ_API_KEY
```

177
docs/source/distributions/self_hosted_distro/nvidia.md
View file

@ -1,177 +0,0 @@
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
# NVIDIA Distribution
The `llamastack/distribution-nvidia` distribution consists of the following provider configurations.
| API | Provider(s) |
|-----|-------------|
| agents | `inline::meta-reference` |
| datasetio | `inline::localfs`, `remote::nvidia` |
| eval | `remote::nvidia` |
| inference | `remote::nvidia` |
| post_training | `remote::nvidia` |
| safety | `remote::nvidia` |
| scoring | `inline::basic` |
| telemetry | `inline::meta-reference` |
| tool_runtime | `inline::rag-runtime` |
| vector_io | `inline::faiss` |
### Environment Variables
The following environment variables can be configured:
- `NVIDIA_API_KEY`: NVIDIA API Key (default: ``)
- `NVIDIA_APPEND_API_VERSION`: Whether to append the API version to the base_url (default: `True`)
- `NVIDIA_DATASET_NAMESPACE`: NVIDIA Dataset Namespace (default: `default`)
- `NVIDIA_PROJECT_ID`: NVIDIA Project ID (default: `test-project`)
- `NVIDIA_CUSTOMIZER_URL`: NVIDIA Customizer URL (default: `https://customizer.api.nvidia.com`)
- `NVIDIA_OUTPUT_MODEL_DIR`: NVIDIA Output Model Directory (default: `test-example-model@v1`)
- `GUARDRAILS_SERVICE_URL`: URL for the NeMo Guardrails Service (default: `http://0.0.0.0:7331`)
- `NVIDIA_GUARDRAILS_CONFIG_ID`: NVIDIA Guardrail Configuration ID (default: `self-check`)
- `NVIDIA_EVALUATOR_URL`: URL for the NeMo Evaluator Service (default: `http://0.0.0.0:7331`)
- `INFERENCE_MODEL`: Inference model (default: `Llama3.1-8B-Instruct`)
- `SAFETY_MODEL`: Name of the model to use for safety (default: `meta/llama-3.1-8b-instruct`)
### Models
The following models are available by default:
- `meta/llama3-8b-instruct (aliases: meta-llama/Llama-3-8B-Instruct)`
- `meta/llama3-70b-instruct (aliases: meta-llama/Llama-3-70B-Instruct)`
- `meta/llama-3.1-8b-instruct (aliases: meta-llama/Llama-3.1-8B-Instruct)`
- `meta/llama-3.1-70b-instruct (aliases: meta-llama/Llama-3.1-70B-Instruct)`
- `meta/llama-3.1-405b-instruct (aliases: meta-llama/Llama-3.1-405B-Instruct-FP8)`
- `meta/llama-3.2-1b-instruct (aliases: meta-llama/Llama-3.2-1B-Instruct)`
- `meta/llama-3.2-3b-instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)`
- `meta/llama-3.2-11b-vision-instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)`
- `meta/llama-3.2-90b-vision-instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)`
- `meta/llama-3.3-70b-instruct (aliases: meta-llama/Llama-3.3-70B-Instruct)`
- `nvidia/llama-3.2-nv-embedqa-1b-v2 `
- `nvidia/nv-embedqa-e5-v5 `
- `nvidia/nv-embedqa-mistral-7b-v2 `
- `snowflake/arctic-embed-l `
## Prerequisites
### NVIDIA API Keys
Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/). Use this key for the `NVIDIA_API_KEY` environment variable.
### Deploy NeMo Microservices Platform
The NVIDIA NeMo microservices platform supports end-to-end microservice deployment of a complete AI flywheel on your Kubernetes cluster through the NeMo Microservices Helm Chart. Please reference the [NVIDIA NeMo Microservices documentation](https://docs.nvidia.com/nemo/microservices/latest/about/index.html) for platform prerequisites and instructions to install and deploy the platform.
## Supported Services
Each Llama Stack API corresponds to a specific NeMo microservice. The core microservices (Customizer, Evaluator, Guardrails) are exposed by the same endpoint. The platform components (Data Store) are each exposed by separate endpoints.
### Inference: NVIDIA NIM
NVIDIA NIM is used for running inference with registered models. There are two ways to access NVIDIA NIMs:
1. Hosted (default): Preview APIs hosted at https://integrate.api.nvidia.com (Requires an API key)
2. Self-hosted: NVIDIA NIMs that run on your own infrastructure.
The deployed platform includes the NIM Proxy microservice, which is the service that provides to access your NIMs (for example, to run inference on a model). Set the `NVIDIA_BASE_URL` environment variable to use your NVIDIA NIM Proxy deployment.
### Datasetio API: NeMo Data Store
The NeMo Data Store microservice serves as the default file storage solution for the NeMo microservices platform. It exposts APIs compatible with the Hugging Face Hub client (`HfApi`), so you can use the client to interact with Data Store. The `NVIDIA_DATASETS_URL` environment variable should point to your NeMo Data Store endpoint.
See the {repopath}`NVIDIA Datasetio docs::llama_stack/providers/remote/datasetio/nvidia/README.md` for supported features and example usage.
### Eval API: NeMo Evaluator
The NeMo Evaluator microservice supports evaluation of LLMs. Launching an Evaluation job with NeMo Evaluator requires an Evaluation Config (an object that contains metadata needed by the job). A Llama Stack Benchmark maps to an Evaluation Config, so registering a Benchmark creates an Evaluation Config in NeMo Evaluator. The `NVIDIA_EVALUATOR_URL` environment variable should point to your NeMo Microservices endpoint.
See the {repopath}`NVIDIA Eval docs::llama_stack/providers/remote/eval/nvidia/README.md` for supported features and example usage.
### Post-Training API: NeMo Customizer
The NeMo Customizer microservice supports fine-tuning models. You can reference {repopath}`this list of supported models::llama_stack/providers/remote/post_training/nvidia/models.py` that can be fine-tuned using Llama Stack. The `NVIDIA_CUSTOMIZER_URL` environment variable should point to your NeMo Microservices endpoint.
See the {repopath}`NVIDIA Post-Training docs::llama_stack/providers/remote/post_training/nvidia/README.md` for supported features and example usage.
### Safety API: NeMo Guardrails
The NeMo Guardrails microservice sits between your application and the LLM, and adds checks and content moderation to a model. The `GUARDRAILS_SERVICE_URL` environment variable should point to your NeMo Microservices endpoint.
See the {repopath}`NVIDIA Safety docs::llama_stack/providers/remote/safety/nvidia/README.md` for supported features and example usage.
## Deploying models
In order to use a registered model with the Llama Stack APIs, ensure the corresponding NIM is deployed to your environment. For example, you can use the NIM Proxy microservice to deploy `meta/llama-3.2-1b-instruct`.
Note: For improved inference speeds, we need to use NIM with `fast_outlines` guided decoding system (specified in the request body). This is the default if you deployed the platform with the NeMo Microservices Helm Chart.
```sh
# URL to NeMo NIM Proxy service
export NEMO_URL="http://nemo.test"
curl --location "$NEMO_URL/v1/deployment/model-deployments" \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"name": "llama-3.2-1b-instruct",
"namespace": "meta",
"config": {
"model": "meta/llama-3.2-1b-instruct",
"nim_deployment": {
"image_name": "nvcr.io/nim/meta/llama-3.2-1b-instruct",
"image_tag": "1.8.3",
"pvc_size": "25Gi",
"gpu": 1,
"additional_envs": {
"NIM_GUIDED_DECODING_BACKEND": "fast_outlines"
}
}
}
}'
```
This NIM deployment should take approximately 10 minutes to go live. [See the docs](https://docs.nvidia.com/nemo/microservices/latest/get-started/tutorials/deploy-nims.html) for more information on how to deploy a NIM and verify it's available for inference.
You can also remove a deployed NIM to free up GPU resources, if needed.
```sh
export NEMO_URL="http://nemo.test"
curl -X DELETE "$NEMO_URL/v1/deployment/model-deployments/meta/llama-3.1-8b-instruct"
```
## Running Llama Stack with NVIDIA
You can do this via Conda or venv (build code), or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-nvidia \
--config /root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env NVIDIA_API_KEY=$NVIDIA_API_KEY
```
### Via Conda
```bash
INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct
llama stack build --template nvidia --image-type conda
llama stack run ./run.yaml \
--port 8321 \
--env NVIDIA_API_KEY=$NVIDIA_API_KEY \
--env INFERENCE_MODEL=$INFERENCE_MODEL
```
### Via venv
If you've set up your local development environment, you can also build the image using your local virtual environment.
```bash
INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct
llama stack build --template nvidia --image-type venv
llama stack run ./run.yaml \
--port 8321 \
--env NVIDIA_API_KEY=$NVIDIA_API_KEY \
--env INFERENCE_MODEL=$INFERENCE_MODEL
```
## Example Notebooks
For examples of how to use the NVIDIA Distribution to run inference, fine-tune, evaluate, and run safety checks on your LLMs, you can reference the example notebooks in {repopath}`docs/notebooks/nvidia`.

165
docs/source/distributions/self_hosted_distro/ollama.md
View file

@ -1,165 +0,0 @@
---
orphan: true
---
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
# Ollama Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-ollama` distribution consists of the following provider configurations.
| API | Provider(s) |
|-----|-------------|
| agents | `inline::meta-reference` |
| datasetio | `remote::huggingface`, `inline::localfs` |
| eval | `inline::meta-reference` |
| files | `inline::localfs` |
| inference | `remote::ollama` |
| post_training | `inline::huggingface` |
| safety | `inline::llama-guard` |
| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
| telemetry | `inline::meta-reference` |
| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
You should use this distribution if you have a regular desktop machine without very powerful GPUs. Of course, if you have powerful GPUs, you can still continue using this distribution since Ollama supports GPU acceleration.
### Environment Variables
The following environment variables can be configured:
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `OLLAMA_URL`: URL of the Ollama server (default: `http://127.0.0.1:11434`)
- `INFERENCE_MODEL`: Inference model loaded into the Ollama server (default: `meta-llama/Llama-3.2-3B-Instruct`)
- `SAFETY_MODEL`: Safety model loaded into the Ollama server (default: `meta-llama/Llama-Guard-3-1B`)
## Setting up Ollama server
Please check the [Ollama Documentation](https://github.com/ollama/ollama) on how to install and run Ollama. After installing Ollama, you need to run `ollama serve` to start the server.
In order to load models, you can run:
```bash
export INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct"
# ollama names this model differently, and we must use the ollama name when loading the model
export OLLAMA_INFERENCE_MODEL="llama3.2:3b-instruct-fp16"
ollama run $OLLAMA_INFERENCE_MODEL --keepalive 60m
```
If you are using Llama Stack Safety / Shield APIs, you will also need to pull and run the safety model.
```bash
export SAFETY_MODEL="meta-llama/Llama-Guard-3-1B"
# ollama names this model differently, and we must use the ollama name when loading the model
export OLLAMA_SAFETY_MODEL="llama-guard3:1b"
ollama run $OLLAMA_SAFETY_MODEL --keepalive 60m
```
## Running Llama Stack
Now you are ready to run Llama Stack with Ollama as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
export LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
llamastack/distribution-ollama \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env OLLAMA_URL=http://host.docker.internal:11434
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
# You need a local checkout of llama-stack to run this, get it using
# git clone https://github.com/meta-llama/llama-stack.git
cd /path/to/llama-stack
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
-v ./llama_stack/templates/ollama/run-with-safety.yaml:/root/my-run.yaml \
llamastack/distribution-ollama \
--config /root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env OLLAMA_URL=http://host.docker.internal:11434
```
### Via Conda
Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
```bash
export LLAMA_STACK_PORT=8321
llama stack build --template ollama --image-type conda
llama stack run ./run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env OLLAMA_URL=http://localhost:11434
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
llama stack run ./run-with-safety.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env OLLAMA_URL=http://localhost:11434
```
### (Optional) Update Model Serving Configuration
```{note}
Please check the [model_entries](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/ollama/models.py) for the supported Ollama models.
```
To serve a new model with `ollama`
```bash
ollama run <model_name>
```
To make sure that the model is being served correctly, run `ollama ps` to get a list of models being served by ollama.
```
$ ollama ps
NAME ID SIZE PROCESSOR UNTIL
llama3.2:3b-instruct-fp16 195a8c01d91e 8.6 GB 100% GPU 9 minutes from now
```
To verify that the model served by ollama is correctly connected to Llama Stack server
```bash
$ llama-stack-client models list
Available Models
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ model_type ┃ identifier ┃ provider_resource_id ┃ metadata ┃ provider_id ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ llm │ meta-llama/Llama-3.2-3B-Instruct │ llama3.2:3b-instruct-fp16 │ │ ollama │
└──────────────┴──────────────────────────────────────┴──────────────────────────────┴───────────┴─────────────┘
Total models: 1
```

297
docs/source/distributions/self_hosted_distro/remote-vllm.md
View file

@ -1,297 +0,0 @@
---
orphan: true
---
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
# Remote vLLM Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-remote-vllm` distribution consists of the following provider configurations:
| API | Provider(s) |
|-----|-------------|
| agents | `inline::meta-reference` |
| datasetio | `remote::huggingface`, `inline::localfs` |
| eval | `inline::meta-reference` |
| inference | `remote::vllm`, `inline::sentence-transformers` |
| safety | `inline::llama-guard` |
| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
| telemetry | `inline::meta-reference` |
| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
You can use this distribution if you want to run an independent vLLM server for inference.
### Environment Variables
The following environment variables can be configured:
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `INFERENCE_MODEL`: Inference model loaded into the vLLM server (default: `meta-llama/Llama-3.2-3B-Instruct`)
- `VLLM_URL`: URL of the vLLM server with the main inference model (default: `http://host.docker.internal:5100/v1`)
- `MAX_TOKENS`: Maximum number of tokens for generation (default: `4096`)
- `SAFETY_VLLM_URL`: URL of the vLLM server with the safety model (default: `http://host.docker.internal:5101/v1`)
- `SAFETY_MODEL`: Name of the safety (Llama-Guard) model to use (default: `meta-llama/Llama-Guard-3-1B`)
## Setting up vLLM server
In the following sections, we'll use AMD, NVIDIA or Intel GPUs to serve as hardware accelerators for the vLLM
server, which acts as both the LLM inference provider and the safety provider. Note that vLLM also
[supports many other hardware accelerators](https://docs.vllm.ai/en/latest/getting_started/installation.html) and
that we only use GPUs here for demonstration purposes. Note that if you run into issues, you can include the environment variable `--env VLLM_DEBUG_LOG_API_SERVER_RESPONSE=true` (available in vLLM v0.8.3 and above) in the `docker run` command to enable log response from API server for debugging.
### Setting up vLLM server on AMD GPU
AMD provides two main vLLM container options:
- rocm/vllm: Production-ready container
- rocm/vllm-dev: Development container with the latest vLLM features
Please check the [Blog about ROCm vLLM Usage](https://rocm.blogs.amd.com/software-tools-optimization/vllm-container/README.html) to get more details.
Here is a sample script to start a ROCm vLLM server locally via Docker:
```bash
export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export CUDA_VISIBLE_DEVICES=0
export VLLM_DIMG="rocm/vllm-dev:main"
docker run \
--pull always \
--ipc=host \
--privileged \
--shm-size 16g \
--device=/dev/kfd \
--device=/dev/dri \
--group-add video \
--cap-add=SYS_PTRACE \
--cap-add=CAP_SYS_ADMIN \
--security-opt seccomp=unconfined \
--security-opt apparmor=unconfined \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
--env "HIP_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" \
-p $INFERENCE_PORT:$INFERENCE_PORT \
-v ~/.cache/huggingface:/root/.cache/huggingface \
$VLLM_DIMG \
python -m vllm.entrypoints.openai.api_server \
--model $INFERENCE_MODEL \
--port $INFERENCE_PORT
```
Note that you'll also need to set `--enable-auto-tool-choice` and `--tool-call-parser` to [enable tool calling in vLLM](https://docs.vllm.ai/en/latest/features/tool_calling.html).
If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a vLLM with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
```bash
export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
export CUDA_VISIBLE_DEVICES=1
export VLLM_DIMG="rocm/vllm-dev:main"
docker run \
--pull always \
--ipc=host \
--privileged \
--shm-size 16g \
--device=/dev/kfd \
--device=/dev/dri \
--group-add video \
--cap-add=SYS_PTRACE \
--cap-add=CAP_SYS_ADMIN \
--security-opt seccomp=unconfined \
--security-opt apparmor=unconfined \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
--env "HIP_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" \
-p $SAFETY_PORT:$SAFETY_PORT \
-v ~/.cache/huggingface:/root/.cache/huggingface \
$VLLM_DIMG \
python -m vllm.entrypoints.openai.api_server \
--model $SAFETY_MODEL \
--port $SAFETY_PORT
```
### Setting up vLLM server on NVIDIA GPU
Please check the [vLLM Documentation](https://docs.vllm.ai/en/v0.5.5/serving/deploying_with_docker.html) to get a vLLM endpoint. Here is a sample script to start a vLLM server locally via Docker:
```bash
export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export CUDA_VISIBLE_DEVICES=0
docker run \
--pull always \
--runtime nvidia \
--gpus $CUDA_VISIBLE_DEVICES \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
-p $INFERENCE_PORT:$INFERENCE_PORT \
--ipc=host \
vllm/vllm-openai:latest \
--gpu-memory-utilization 0.7 \
--model $INFERENCE_MODEL \
--port $INFERENCE_PORT
```
Note that you'll also need to set `--enable-auto-tool-choice` and `--tool-call-parser` to [enable tool calling in vLLM](https://docs.vllm.ai/en/latest/features/tool_calling.html).
If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a vLLM with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
```bash
export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
export CUDA_VISIBLE_DEVICES=1
docker run \
--pull always \
--runtime nvidia \
--gpus $CUDA_VISIBLE_DEVICES \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
-p $SAFETY_PORT:$SAFETY_PORT \
--ipc=host \
vllm/vllm-openai:latest \
--gpu-memory-utilization 0.7 \
--model $SAFETY_MODEL \
--port $SAFETY_PORT
```
### Setting up vLLM server on Intel GPU
Refer to [vLLM Documentation for XPU](https://docs.vllm.ai/en/v0.8.2/getting_started/installation/gpu.html?device=xpu) to get a vLLM endpoint. In addition to vLLM side setup which guides towards installing vLLM from sources orself-building vLLM Docker container, Intel provides prebuilt vLLM container to use on systems with Intel GPUs supported by PyTorch XPU backend:
- [intel/vllm](https://hub.docker.com/r/intel/vllm)
Here is a sample script to start a vLLM server locally via Docker using Intel provided container:
```bash
export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-1B-Instruct
export ZE_AFFINITY_MASK=0
docker run \
--pull always \
--device /dev/dri \
-v /dev/dri/by-path:/dev/dri/by-path \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
--env ZE_AFFINITY_MASK=$ZE_AFFINITY_MASK \
-p $INFERENCE_PORT:$INFERENCE_PORT \
--ipc=host \
intel/vllm:xpu \
--gpu-memory-utilization 0.7 \
--model $INFERENCE_MODEL \
--port $INFERENCE_PORT
```
If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a vLLM with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
```bash
export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
export ZE_AFFINITY_MASK=1
docker run \
--pull always \
--device /dev/dri \
-v /dev/dri/by-path:/dev/dri/by-path \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
--env ZE_AFFINITY_MASK=$ZE_AFFINITY_MASK \
-p $SAFETY_PORT:$SAFETY_PORT \
--ipc=host \
intel/vllm:xpu \
--gpu-memory-utilization 0.7 \
--model $SAFETY_MODEL \
--port $SAFETY_PORT
```
## Running Llama Stack
Now you are ready to run Llama Stack with vLLM as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export LLAMA_STACK_PORT=8321
# You need a local checkout of llama-stack to run this, get it using
# git clone https://github.com/meta-llama/llama-stack.git
cd /path/to/llama-stack
docker run \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./llama_stack/templates/remote-vllm/run.yaml:/root/my-run.yaml \
llamastack/distribution-remote-vllm \
--config /root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT/v1
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
# You need a local checkout of llama-stack to run this, get it using
# git clone https://github.com/meta-llama/llama-stack.git
cd /path/to/llama-stack
docker run \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
-v ./llama_stack/templates/remote-vllm/run-with-safety.yaml:/root/my-run.yaml \
llamastack/distribution-remote-vllm \
--config /root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT/v1 \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env SAFETY_VLLM_URL=http://host.docker.internal:$SAFETY_PORT/v1
```
### Via Conda
Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
```bash
export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export LLAMA_STACK_PORT=8321
cd distributions/remote-vllm
llama stack build --template remote-vllm --image-type conda
llama stack run ./run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_URL=http://localhost:$INFERENCE_PORT/v1
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
llama stack run ./run-with-safety.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_URL=http://localhost:$INFERENCE_PORT/v1 \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env SAFETY_VLLM_URL=http://localhost:$SAFETY_PORT/v1
```

91
docs/source/distributions/self_hosted_distro/sambanova.md
View file

@ -1,91 +0,0 @@
---
orphan: true
---
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
# SambaNova Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-sambanova` distribution consists of the following provider configurations.
| API | Provider(s) |
|-----|-------------|
| agents | `inline::meta-reference` |
| inference | `remote::sambanova`, `inline::sentence-transformers` |
| safety | `remote::sambanova` |
| telemetry | `inline::meta-reference` |
| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
### Environment Variables
The following environment variables can be configured:
- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `SAMBANOVA_API_KEY`: SambaNova API Key (default: ``)
### Models
The following models are available by default:
- `sambanova/Meta-Llama-3.1-8B-Instruct (aliases: meta-llama/Llama-3.1-8B-Instruct)`
- `sambanova/Meta-Llama-3.1-405B-Instruct (aliases: meta-llama/Llama-3.1-405B-Instruct-FP8)`
- `sambanova/Meta-Llama-3.2-1B-Instruct (aliases: meta-llama/Llama-3.2-1B-Instruct)`
- `sambanova/Meta-Llama-3.2-3B-Instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)`
- `sambanova/Meta-Llama-3.3-70B-Instruct (aliases: meta-llama/Llama-3.3-70B-Instruct)`
- `sambanova/Llama-3.2-11B-Vision-Instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)`
- `sambanova/Llama-3.2-90B-Vision-Instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)`
- `sambanova/Llama-4-Scout-17B-16E-Instruct (aliases: meta-llama/Llama-4-Scout-17B-16E-Instruct)`
- `sambanova/Llama-4-Maverick-17B-128E-Instruct (aliases: meta-llama/Llama-4-Maverick-17B-128E-Instruct)`
- `sambanova/Meta-Llama-Guard-3-8B (aliases: meta-llama/Llama-Guard-3-8B)`
### Prerequisite: API Keys
Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaNova.ai](http://cloud.sambanova.ai?utm_source=llamastack&utm_medium=external&utm_campaign=cloud_signup).
## Running Llama Stack with SambaNova
You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
```bash
LLAMA_STACK_PORT=8321
llama stack build --template sambanova --image-type container
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
distribution-sambanova \
--port $LLAMA_STACK_PORT \
--env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY
```
### Via Venv
```bash
llama stack build --template sambanova --image-type venv
llama stack run --image-type venv ~/.llama/distributions/sambanova/sambanova-run.yaml \
--port $LLAMA_STACK_PORT \
--env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY
```
### Via Conda
```bash
llama stack build --template sambanova --image-type conda
llama stack run --image-type conda ~/.llama/distributions/sambanova/sambanova-run.yaml \
--port $LLAMA_STACK_PORT \
--env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY
```

245
docs/source/distributions/self_hosted_distro/starter.md Normal file
View file

@ -0,0 +1,245 @@
---
orphan: true
---
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
# Starter Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-starter` distribution is a comprehensive, multi-provider distribution that includes most of the available inference providers in Llama Stack. It's designed to be a one-stop solution for developers who want to experiment with different AI providers without having to configure each one individually.
## Provider Composition
The starter distribution consists of the following provider configurations:
| API | Provider(s) |
|-----|-------------|
| agents | `inline::meta-reference` |
| datasetio | `remote::huggingface`, `inline::localfs` |
| eval | `inline::meta-reference` |
| files | `inline::localfs` |
| inference | `remote::openai`, `remote::fireworks`, `remote::together`, `remote::ollama`, `remote::anthropic`, `remote::gemini`, `remote::groq`, `remote::sambanova`, `remote::vllm`, `remote::tgi`, `remote::cerebras`, `remote::llama-openai-compat`, `remote::nvidia`, `remote::hf::serverless`, `remote::hf::endpoint`, `inline::sentence-transformers` |
| safety | `inline::llama-guard` |
| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
| telemetry | `inline::meta-reference` |
| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` |
| vector_io | `inline::faiss`, `inline::sqlite-vec`, `remote::chromadb`, `remote::pgvector` |
## Inference Providers
The starter distribution includes a comprehensive set of inference providers:
### Hosted Providers
- **OpenAI**: GPT-4, GPT-3.5, O1, O3, O4 models and text embeddings
- **Fireworks**: Llama 3.1, 3.2, 3.3, 4 Scout, 4 Maverick models and embeddings
- **Together**: Llama 3.1, 3.2, 3.3, 4 Scout, 4 Maverick models and embeddings
- **Anthropic**: Claude 3.5 Sonnet, Claude 3.7 Sonnet, Claude 3.5 Haiku, and Voyage embeddings
- **Gemini**: Gemini 1.5, 2.0, 2.5 models and text embeddings
- **Groq**: Fast Llama models (3.1, 3.2, 3.3, 4 Scout, 4 Maverick)
- **SambaNova**: Llama 3.1, 3.2, 3.3, 4 Scout, 4 Maverick models
- **Cerebras**: Cerebras AI models
- **NVIDIA**: NVIDIA NIM models
- **HuggingFace**: Serverless and endpoint models
- **Bedrock**: AWS Bedrock models
### Local/Remote Providers
- **Ollama**: Local Ollama models
- **vLLM**: Local or remote vLLM server
- **TGI**: Text Generation Inference server - Dell Enterprise Hub's custom TGI container too (use `DEH_URL`)
- **Sentence Transformers**: Local embedding models
All providers are disabled by default. So you need to enable them by setting the environment variables.
## Environment Variables
The following environment variables can be configured:
### Server Configuration
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
### API Keys for Hosted Providers
- `OPENAI_API_KEY`: OpenAI API key
- `FIREWORKS_API_KEY`: Fireworks API key
- `TOGETHER_API_KEY`: Together API key
- `ANTHROPIC_API_KEY`: Anthropic API key
- `GEMINI_API_KEY`: Google Gemini API key
- `GROQ_API_KEY`: Groq API key
- `SAMBANOVA_API_KEY`: SambaNova API key
- `CEREBRAS_API_KEY`: Cerebras API key
- `LLAMA_API_KEY`: Llama API key
- `NVIDIA_API_KEY`: NVIDIA API key
- `HF_API_TOKEN`: HuggingFace API token
### Local Provider Configuration
- `OLLAMA_URL`: Ollama server URL (default: `http://localhost:11434`)
- `VLLM_URL`: vLLM server URL (default: `http://localhost:8000/v1`)
- `VLLM_MAX_TOKENS`: vLLM max tokens (default: `4096`)
- `VLLM_API_TOKEN`: vLLM API token (default: `fake`)
- `VLLM_TLS_VERIFY`: vLLM TLS verification (default: `true`)
- `TGI_URL`: TGI server URL
### Model Configuration
- `INFERENCE_MODEL`: HuggingFace model for serverless inference
- `INFERENCE_ENDPOINT_NAME`: HuggingFace endpoint name
- `OLLAMA_INFERENCE_MODEL`: Ollama model name
- `OLLAMA_EMBEDDING_MODEL`: Ollama embedding model name
- `OLLAMA_EMBEDDING_DIMENSION`: Ollama embedding dimension (default: `384`)
- `VLLM_INFERENCE_MODEL`: vLLM model name
### Vector Database Configuration
- `SQLITE_STORE_DIR`: SQLite store directory (default: `~/.llama/distributions/starter`)
- `ENABLE_SQLITE_VEC`: Enable SQLite vector provider
- `ENABLE_CHROMADB`: Enable ChromaDB provider
- `ENABLE_PGVECTOR`: Enable PGVector provider
- `CHROMADB_URL`: ChromaDB server URL
- `PGVECTOR_HOST`: PGVector host (default: `localhost`)
- `PGVECTOR_PORT`: PGVector port (default: `5432`)
- `PGVECTOR_DB`: PGVector database name
- `PGVECTOR_USER`: PGVector username
- `PGVECTOR_PASSWORD`: PGVector password
### Tool Configuration
- `BRAVE_SEARCH_API_KEY`: Brave Search API key
- `TAVILY_SEARCH_API_KEY`: Tavily Search API key
### Telemetry Configuration
- `OTEL_SERVICE_NAME`: OpenTelemetry service name
- `TELEMETRY_SINKS`: Telemetry sinks (default: `console,sqlite`)
## Enabling Providers
You can enable specific providers by setting their provider ID to a valid value using environment variables. This is useful when you want to use certain providers or don't have the required API keys.
### Examples of Enabling Providers
#### Enable FAISS Vector Provider
```bash
export ENABLE_FAISS=faiss
```
#### Enable Ollama Models
```bash
export ENABLE_OLLAMA=ollama
```
#### Disable vLLM Models
```bash
export VLLM_INFERENCE_MODEL=__disabled__
```
#### Disable Optional Vector Providers
```bash
export ENABLE_SQLITE_VEC=__disabled__
export ENABLE_CHROMADB=__disabled__
export ENABLE_PGVECTOR=__disabled__
```
### Provider ID Patterns
The starter distribution uses several patterns for provider IDs:
1. **Direct provider IDs**: `faiss`, `ollama`, `vllm`
2. **Environment-based provider IDs**: `${env.ENABLE_SQLITE_VEC+sqlite-vec}`
3. **Model-based provider IDs**: `${env.OLLAMA_INFERENCE_MODEL:__disabled__}`
When using the `+` pattern (like `${env.ENABLE_SQLITE_VEC+sqlite-vec}`), the provider is enabled by default and can be disabled by setting the environment variable to `__disabled__`.
When using the `:` pattern (like `${env.OLLAMA_INFERENCE_MODEL:__disabled__}`), the provider is disabled by default and can be enabled by setting the environment variable to a valid value.
## Running the Distribution
You can run the starter distribution via Docker or Conda.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-e OPENAI_API_KEY=your_openai_key \
-e FIREWORKS_API_KEY=your_fireworks_key \
-e TOGETHER_API_KEY=your_together_key \
llamastack/distribution-starter \
--port $LLAMA_STACK_PORT
```
### Via Conda
Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
```bash
llama stack build --template starter --image-type conda
llama stack run distributions/starter/run.yaml \
--port 8321 \
--env OPENAI_API_KEY=your_openai_key \
--env FIREWORKS_API_KEY=your_fireworks_key \
--env TOGETHER_API_KEY=your_together_key
```
## Example Usage
Once the distribution is running, you can use any of the available models. Here are some examples:
### Using OpenAI Models
```bash
llama-stack-client --endpoint http://localhost:8321 \
inference chat-completion \
--model-id openai/gpt-4o \
--message "Hello, how are you?"
```
### Using Fireworks Models
```bash
llama-stack-client --endpoint http://localhost:8321 \
inference chat-completion \
--model-id fireworks/meta-llama/Llama-3.2-3B-Instruct \
--message "Write a short story about a robot."
```
### Using Local Ollama Models
```bash
# First, make sure Ollama is running and you have a model
ollama run llama3.2:3b
# Then use it through Llama Stack
export OLLAMA_INFERENCE_MODEL=llama3.2:3b
llama-stack-client --endpoint http://localhost:8321 \
inference chat-completion \
--model-id ollama/llama3.2:3b \
--message "Explain quantum computing in simple terms."
```
## Storage
The starter distribution uses SQLite for local storage of various components:
- **Metadata store**: `~/.llama/distributions/starter/registry.db`
- **Inference store**: `~/.llama/distributions/starter/inference_store.db`
- **FAISS store**: `~/.llama/distributions/starter/faiss_store.db`
- **SQLite vector store**: `~/.llama/distributions/starter/sqlite_vec.db`
- **Files metadata**: `~/.llama/distributions/starter/files_metadata.db`
- **Agents store**: `~/.llama/distributions/starter/agents_store.db`
- **Responses store**: `~/.llama/distributions/starter/responses_store.db`
- **Trace store**: `~/.llama/distributions/starter/trace_store.db`
- **Evaluation store**: `~/.llama/distributions/starter/meta_reference_eval.db`
- **Dataset I/O stores**: Various HuggingFace and local filesystem stores
## Benefits of the Starter Distribution
1. **Comprehensive Coverage**: Includes most popular AI providers in one distribution
2. **Flexible Configuration**: Easy to enable/disable providers based on your needs
3. **No Local GPU Required**: Most providers are cloud-based, making it accessible to developers without high-end hardware
4. **Easy Migration**: Start with hosted providers and gradually move to local ones as needed
5. **Production Ready**: Includes safety, evaluation, and telemetry components
6. **Tool Integration**: Comes with web search, RAG, and model context protocol tools
The starter distribution is ideal for developers who want to experiment with different AI providers, build prototypes quickly, or create applications that can work with multiple AI backends.

149
docs/source/distributions/self_hosted_distro/tgi.md
View file

@ -1,149 +0,0 @@
---
orphan: true
---
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
# TGI Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-tgi` distribution consists of the following provider configurations.
| API | Provider(s) |
|-----|-------------|
| agents | `inline::meta-reference` |
| datasetio | `remote::huggingface`, `inline::localfs` |
| eval | `inline::meta-reference` |
| inference | `remote::tgi`, `inline::sentence-transformers` |
| safety | `inline::llama-guard` |
| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
| telemetry | `inline::meta-reference` |
| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` |
| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
You can use this distribution if you have GPUs and want to run an independent TGI server container for running inference.
### Environment Variables
The following environment variables can be configured:
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `INFERENCE_MODEL`: Inference model loaded into the TGI server (default: `meta-llama/Llama-3.2-3B-Instruct`)
- `TGI_URL`: URL of the TGI server with the main inference model (default: `http://127.0.0.1:8080/v1`)
- `TGI_SAFETY_URL`: URL of the TGI server with the safety model (default: `http://127.0.0.1:8081/v1`)
- `SAFETY_MODEL`: Name of the safety (Llama-Guard) model to use (default: `meta-llama/Llama-Guard-3-1B`)
## Setting up TGI server
Please check the [TGI Getting Started Guide](https://github.com/huggingface/text-generation-inference?tab=readme-ov-file#get-started) to get a TGI endpoint. Here is a sample script to start a TGI server locally via Docker:
```bash
export INFERENCE_PORT=8080
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export CUDA_VISIBLE_DEVICES=0
docker run --rm -it \
--pull always \
-v $HOME/.cache/huggingface:/data \
-p $INFERENCE_PORT:$INFERENCE_PORT \
--gpus $CUDA_VISIBLE_DEVICES \
ghcr.io/huggingface/text-generation-inference:2.3.1 \
--dtype bfloat16 \
--usage-stats off \
--sharded false \
--cuda-memory-fraction 0.7 \
--model-id $INFERENCE_MODEL \
--port $INFERENCE_PORT
```
If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a TGI with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
```bash
export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
export CUDA_VISIBLE_DEVICES=1
docker run --rm -it \
--pull always \
-v $HOME/.cache/huggingface:/data \
-p $SAFETY_PORT:$SAFETY_PORT \
--gpus $CUDA_VISIBLE_DEVICES \
ghcr.io/huggingface/text-generation-inference:2.3.1 \
--dtype bfloat16 \
--usage-stats off \
--sharded false \
--model-id $SAFETY_MODEL \
--port $SAFETY_PORT
```
## Running Llama Stack
Now you are ready to run Llama Stack with TGI as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
llamastack/distribution-tgi \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env TGI_URL=http://host.docker.internal:$INFERENCE_PORT
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
# You need a local checkout of llama-stack to run this, get it using
# git clone https://github.com/meta-llama/llama-stack.git
cd /path/to/llama-stack
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
-v ./llama_stack/templates/tgi/run-with-safety.yaml:/root/my-run.yaml \
llamastack/distribution-tgi \
--config /root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env TGI_URL=http://host.docker.internal:$INFERENCE_PORT \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env TGI_SAFETY_URL=http://host.docker.internal:$SAFETY_PORT
```
### Via Conda
Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
```bash
llama stack build --template tgi --image-type conda
llama stack run ./run.yaml
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env TGI_URL=http://127.0.0.1:$INFERENCE_PORT
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
llama stack run ./run-with-safety.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env TGI_URL=http://127.0.0.1:$INFERENCE_PORT \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env TGI_SAFETY_URL=http://127.0.0.1:$SAFETY_PORT
```

86
docs/source/distributions/self_hosted_distro/together.md
View file

@ -1,86 +0,0 @@
---
orphan: true
---
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
# Together Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-together` distribution consists of the following provider configurations.
| API | Provider(s) |
|-----|-------------|
| agents | `inline::meta-reference` |
| datasetio | `remote::huggingface`, `inline::localfs` |
| eval | `inline::meta-reference` |
| inference | `remote::together`, `inline::sentence-transformers` |
| safety | `inline::llama-guard` |
| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
| telemetry | `inline::meta-reference` |
| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
### Environment Variables
The following environment variables can be configured:
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `TOGETHER_API_KEY`: Together.AI API Key (default: ``)
### Models
The following models are available by default:
- `meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo (aliases: meta-llama/Llama-3.1-8B-Instruct)`
- `meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo (aliases: meta-llama/Llama-3.1-70B-Instruct)`
- `meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo (aliases: meta-llama/Llama-3.1-405B-Instruct-FP8)`
- `meta-llama/Llama-3.2-3B-Instruct-Turbo (aliases: meta-llama/Llama-3.2-3B-Instruct)`
- `meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)`
- `meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)`
- `meta-llama/Llama-3.3-70B-Instruct-Turbo (aliases: meta-llama/Llama-3.3-70B-Instruct)`
- `meta-llama/Meta-Llama-Guard-3-8B (aliases: meta-llama/Llama-Guard-3-8B)`
- `meta-llama/Llama-Guard-3-11B-Vision-Turbo (aliases: meta-llama/Llama-Guard-3-11B-Vision)`
- `togethercomputer/m2-bert-80M-8k-retrieval `
- `togethercomputer/m2-bert-80M-32k-retrieval `
- `meta-llama/Llama-4-Scout-17B-16E-Instruct (aliases: meta-llama/Llama-4-Scout-17B-16E-Instruct, together/meta-llama/Llama-4-Scout-17B-16E-Instruct)`
- `meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 (aliases: meta-llama/Llama-4-Maverick-17B-128E-Instruct, together/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8)`
### Prerequisite: API Keys
Make sure you have access to a Together API Key. You can get one by visiting [together.xyz](https://together.xyz/).
## Running Llama Stack with Together
You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
llamastack/distribution-together \
--port $LLAMA_STACK_PORT \
--env TOGETHER_API_KEY=$TOGETHER_API_KEY
```
### Via Conda
```bash
llama stack build --template together --image-type conda
llama stack run ./run.yaml \
--port $LLAMA_STACK_PORT \
--env TOGETHER_API_KEY=$TOGETHER_API_KEY
```

4
docs/source/getting_started/detailed_tutorial.md
View file

@ -58,7 +58,7 @@ which defines the providers and their settings.
Now let's build and run the Llama Stack config for Ollama.
```bash
INFERENCE_MODEL=llama3.2:3b llama stack build --template ollama --image-type venv --run
INFERENCE_MODEL=llama3.2:3b llama stack build --template starter --image-type venv --run
```
:::
:::{tab-item} Using `conda`
@ -69,7 +69,7 @@ which defines the providers and their settings.
Now let's build and run the Llama Stack config for Ollama.
```bash
INFERENCE_MODEL=llama3.2:3b llama stack build --template ollama --image-type conda --image-name llama3-3b-conda --run
INFERENCE_MODEL=llama3.2:3b llama stack build --template starter --image-type conda --image-name llama3-3b-conda --run
```
:::
:::{tab-item} Using a Container

2
docs/source/getting_started/index.md
View file

@ -17,7 +17,7 @@ ollama run llama3.2:3b --keepalive 60m
#### Step 2: Run the Llama Stack server
We will use `uv` to run the Llama Stack server.
```bash
INFERENCE_MODEL=llama3.2:3b uv run --with llama-stack llama stack build --template ollama --image-type venv --run
INFERENCE_MODEL=llama3.2:3b uv run --with llama-stack llama stack build --template starter --image-type venv --run
```
#### Step 3: Run the demo
Now open up a new terminal and copy the following script into a file named `demo_script.py`.

2
docs/source/providers/post_training/huggingface.md
View file

@ -23,7 +23,7 @@ To use the HF SFTTrainer in your Llama Stack project, follow these steps:
You can access the HuggingFace trainer via the `ollama` distribution:
```bash
llama stack build --template ollama --image-type venv
llama stack build --template starter --image-type venv
llama stack run --image-type venv ~/.llama/distributions/ollama/ollama-run.yaml
```

2
docs/zero_to_hero_guide/README.md
View file

@ -83,7 +83,7 @@ If you're looking for more specific topics, we have a [Zero to Hero Guide](#next
1. **Build the Llama Stack**:
Build the Llama Stack using the `ollama` template:
```bash
llama stack build --template ollama --image-type conda
llama stack build --template starter --image-type conda
```
**Expected Output:**
```bash

8
llama_stack/distribution/providers.py
View file

@ -84,7 +84,13 @@ class ProviderImpl(Providers):
Each API maps to a dictionary of provider IDs to their health responses.
"""
providers_health: dict[str, dict[str, HealthResponse]] = {}
timeout = 1.0
# The timeout has to be long enough to allow all the providers to be checked, especially in
# the case of the inference router health check since it checks all registered inference
# providers.
# The timeout must not be equal to the one set by health method for a given implementation,
# otherwise we will miss some providers.
timeout = 3.0
async def check_provider_health(impl: Any) -> tuple[str, HealthResponse] | None:
# Skip special implementations (inspect/providers) that don't have provider specs

5
llama_stack/distribution/stack.py
View file

@ -98,6 +98,10 @@ async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]):
method = getattr(impls[api], register_method)
for obj in objects:
# Do not register models on disabled providers
if hasattr(obj, "provider_id") and obj.provider_id is not None and obj.provider_id == "__disabled__":
logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled provider.")
continue
# In complex templates, like our starter template, we may have dynamic model ids
# given by environment variables. This allows those environment variables to have
# a default value of __disabled__ to skip registration of the model if not set.
@ -106,6 +110,7 @@ async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]):
and obj.provider_model_id is not None
and "__disabled__" in obj.provider_model_id
):
logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled model.")
continue
# we want to maintain the type information in arguments to method.
# instead of method(**obj.model_dump()), which may convert a typed attr to a dict,

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

@ -26,8 +26,8 @@ class CerebrasImplConfig(BaseModel):
)
@classmethod
def sample_run_config(cls, **kwargs) -> dict[str, Any]:
def sample_run_config(cls, api_key: str = "${env.CEREBRAS_API_KEY}", **kwargs) -> dict[str, Any]:
return {
"base_url": DEFAULT_BASE_URL,
"api_key": "${env.CEREBRAS_API_KEY}",
"api_key": api_key,
}

2
llama_stack/providers/remote/inference/llama_openai_compat/config.py
View file

@ -31,7 +31,7 @@ class LlamaCompatConfig(BaseModel):
)
@classmethod
def sample_run_config(cls, api_key: str = "${env.LLAMA_API_KEY}", **kwargs) -> dict[str, Any]:
def sample_run_config(cls, api_key: str = "${env.LLAMA_API_KEY:}", **kwargs) -> dict[str, Any]:
return {
"openai_compat_api_base": "https://api.llama.com/compat/v1/",
"api_key": api_key,

14
llama_stack/providers/remote/inference/nvidia/config.py
View file

@ -53,9 +53,15 @@ class NVIDIAConfig(BaseModel):
)
@classmethod
def sample_run_config(cls, **kwargs) -> dict[str, Any]:
def sample_run_config(
cls,
url: str = "${env.NVIDIA_BASE_URL:=https://integrate.api.nvidia.com}",
api_key: str = "${env.NVIDIA_API_KEY:+}",
append_api_version: bool = "${env.NVIDIA_APPEND_API_VERSION:=True}",
**kwargs,
) -> dict[str, Any]:
return {
"url": "${env.NVIDIA_BASE_URL:=https://integrate.api.nvidia.com}",
"api_key": "${env.NVIDIA_API_KEY:+}",
"append_api_version": "${env.NVIDIA_APPEND_API_VERSION:=True}",
"url": url,
"api_key": api_key,
"append_api_version": append_api_version,
}

6
llama_stack/providers/remote/inference/ollama/config.py
View file

@ -13,13 +13,9 @@ DEFAULT_OLLAMA_URL = "http://localhost:11434"
class OllamaImplConfig(BaseModel):
url: str = DEFAULT_OLLAMA_URL
raise_on_connect_error: bool = True
@classmethod
def sample_run_config(
cls, url: str = "${env.OLLAMA_URL:=http://localhost:11434}", raise_on_connect_error: bool = True, **kwargs
) -> dict[str, Any]:
def sample_run_config(cls, url: str = "${env.OLLAMA_URL:=http://localhost:11434}", **kwargs) -> dict[str, Any]:
return {
"url": url,
"raise_on_connect_error": raise_on_connect_error,
}

6
llama_stack/providers/remote/inference/ollama/ollama.py
View file

@ -91,7 +91,6 @@ class OllamaInferenceAdapter(
def __init__(self, config: OllamaImplConfig) -> None:
self.register_helper = ModelRegistryHelper(MODEL_ENTRIES)
self.url = config.url
self.raise_on_connect_error = config.raise_on_connect_error
@property
def client(self) -> AsyncClient:
@ -105,10 +104,7 @@ class OllamaInferenceAdapter(
logger.debug(f"checking connectivity to Ollama at `{self.url}`...")
health_response = await self.health()
if health_response["status"] == HealthStatus.ERROR:
if self.raise_on_connect_error:
raise RuntimeError("Ollama Server is not running, start it using `ollama serve` in a separate terminal")
else:
logger.warning("Ollama Server is not running, start it using `ollama serve` in a separate terminal")
raise RuntimeError("Ollama Server is not running, start it using `ollama serve` in a separate terminal")
async def health(self) -> HealthResponse:
"""

8
llama_stack/providers/remote/inference/passthrough/config.py
View file

@ -24,8 +24,10 @@ class PassthroughImplConfig(BaseModel):
)
@classmethod
def sample_run_config(cls, **kwargs) -> dict[str, Any]:
def sample_run_config(
cls, url: str = "${env.PASSTHROUGH_URL}", api_key: str = "${env.PASSTHROUGH_API_KEY}", **kwargs
) -> dict[str, Any]:
return {
"url": "${env.PASSTHROUGH_URL}",
"api_key": "${env.PASSTHROUGH_API_KEY}",
"url": url,
"api_key": api_key,
}

6
llama_stack/providers/remote/inference/tgi/config.py
View file

@ -17,7 +17,11 @@ class TGIImplConfig(BaseModel):
)
@classmethod
def sample_run_config(cls, url: str = "${env.TGI_URL}", **kwargs):
def sample_run_config(
cls,
url: str = "${env.TGI_URL}",
**kwargs,
):
return {
"url": url,
}

1
llama_stack/providers/remote/inference/tgi/tgi.py
View file

@ -327,7 +327,6 @@ class InferenceEndpointAdapter(_HfAdapter):
# Get the inference endpoint details
api = HfApi(token=config.api_token.get_secret_value())
endpoint = api.get_inference_endpoint(config.endpoint_name)
# Wait for the endpoint to be ready (if not already)
endpoint.wait(timeout=60)

7
llama_stack/templates/bedrock/__init__.py
View file

@ -1,7 +0,0 @@
# 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.
from .bedrock import get_distribution_template # noqa: F401

82
llama_stack/templates/bedrock/bedrock.py
View file

@ -1,82 +0,0 @@
# 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.
from pathlib import Path
from llama_stack.distribution.datatypes import Provider, ToolGroupInput
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.remote.inference.bedrock.models import MODEL_ENTRIES
from llama_stack.templates.template import (
DistributionTemplate,
RunConfigSettings,
get_model_registry,
)
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::bedrock"],
"vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["remote::bedrock"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
"eval": ["inline::meta-reference"],
"datasetio": ["remote::huggingface", "inline::localfs"],
"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
"tool_runtime": [
"remote::brave-search",
"remote::tavily-search",
"inline::rag-runtime",
"remote::model-context-protocol",
],
}
name = "bedrock"
vector_io_provider = Provider(
provider_id="faiss",
provider_type="inline::faiss",
config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
)
available_models = {
"bedrock": MODEL_ENTRIES,
}
default_models = get_model_registry(available_models)
default_tool_groups = [
ToolGroupInput(
toolgroup_id="builtin::websearch",
provider_id="tavily-search",
),
ToolGroupInput(
toolgroup_id="builtin::rag",
provider_id="rag-runtime",
),
]
return DistributionTemplate(
name=name,
distro_type="self_hosted",
description="Use AWS Bedrock for running LLM inference and safety",
container_image=None,
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
available_models_by_provider=available_models,
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"vector_io": [vector_io_provider],
},
default_models=default_models,
default_tool_groups=default_tool_groups,
),
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"8321",
"Port for the Llama Stack distribution server",
),
},
)

34
llama_stack/templates/bedrock/build.yaml
View file

@ -1,34 +0,0 @@
version: 2
distribution_spec:
description: Use AWS Bedrock for running LLM inference and safety
providers:
inference:
- remote::bedrock
vector_io:
- inline::faiss
- remote::chromadb
- remote::pgvector
safety:
- remote::bedrock
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
eval:
- inline::meta-reference
datasetio:
- remote::huggingface
- inline::localfs
scoring:
- inline::basic
- inline::llm-as-judge
- inline::braintrust
tool_runtime:
- remote::brave-search
- remote::tavily-search
- inline::rag-runtime
- remote::model-context-protocol
image_type: conda
additional_pip_packages:
- aiosqlite
- sqlalchemy[asyncio]

73
llama_stack/templates/bedrock/doc_template.md
View file

@ -1,73 +0,0 @@
# Bedrock Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations:
{{ providers_table }}
{% if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
{% endfor %}
{% endif %}
{% if default_models %}
### Models
The following models are available by default:
{% for model in default_models %}
- `{{ model.model_id }} {{ model.doc_string }}`
{% endfor %}
{% endif %}
### Prerequisite: API Keys
Make sure you have access to a AWS Bedrock API Key. You can get one by visiting [AWS Bedrock](https://aws.amazon.com/bedrock/).
## Running Llama Stack with AWS Bedrock
You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
llamastack/distribution-{{ name }} \
--port $LLAMA_STACK_PORT \
--env AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
--env AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
--env AWS_SESSION_TOKEN=$AWS_SESSION_TOKEN \
--env AWS_DEFAULT_REGION=$AWS_DEFAULT_REGION
```
### Via Conda
```bash
llama stack build --template {{ name }} --image-type conda
llama stack run ./run.yaml \
--port $LLAMA_STACK_PORT \
--env AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
--env AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
--env AWS_SESSION_TOKEN=$AWS_SESSION_TOKEN \
--env AWS_DEFAULT_REGION=$AWS_DEFAULT_REGION
```

147
llama_stack/templates/bedrock/run.yaml
View file

@ -1,147 +0,0 @@
version: 2
image_name: bedrock
apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: bedrock
provider_type: remote::bedrock
config: {}
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/bedrock}/faiss_store.db
safety:
- provider_id: bedrock
provider_type: remote::bedrock
config: {}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/bedrock}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/bedrock}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/bedrock}/trace_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/bedrock}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/bedrock}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/bedrock}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
- provider_id: model-context-protocol
provider_type: remote::model-context-protocol
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/bedrock}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/bedrock}/inference_store.db
models:
- metadata: {}
model_id: meta.llama3-1-8b-instruct-v1:0
provider_id: bedrock
provider_model_id: meta.llama3-1-8b-instruct-v1:0
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-8B-Instruct
provider_id: bedrock
provider_model_id: meta.llama3-1-8b-instruct-v1:0
model_type: llm
- metadata: {}
model_id: meta.llama3-1-70b-instruct-v1:0
provider_id: bedrock
provider_model_id: meta.llama3-1-70b-instruct-v1:0
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-70B-Instruct
provider_id: bedrock
provider_model_id: meta.llama3-1-70b-instruct-v1:0
model_type: llm
- metadata: {}
model_id: meta.llama3-1-405b-instruct-v1:0
provider_id: bedrock
provider_model_id: meta.llama3-1-405b-instruct-v1:0
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-405B-Instruct-FP8
provider_id: bedrock
provider_model_id: meta.llama3-1-405b-instruct-v1:0
model_type: llm
shields: []
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321

7
llama_stack/templates/cerebras/__init__.py
View file

@ -1,7 +0,0 @@
# 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.
from .cerebras import get_distribution_template # noqa: F401

34
llama_stack/templates/cerebras/build.yaml
View file

@ -1,34 +0,0 @@
version: 2
distribution_spec:
description: Use Cerebras for running LLM inference
providers:
inference:
- remote::cerebras
- inline::sentence-transformers
safety:
- inline::llama-guard
vector_io:
- inline::faiss
- remote::chromadb
- remote::pgvector
agents:
- inline::meta-reference
eval:
- inline::meta-reference
datasetio:
- remote::huggingface
- inline::localfs
scoring:
- inline::basic
- inline::llm-as-judge
- inline::braintrust
telemetry:
- inline::meta-reference
tool_runtime:
- remote::brave-search
- remote::tavily-search
- inline::rag-runtime
image_type: conda
additional_pip_packages:
- aiosqlite
- sqlalchemy[asyncio]

110
llama_stack/templates/cerebras/cerebras.py
View file

@ -1,110 +0,0 @@
# 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.
from pathlib import Path
from llama_stack.apis.models import ModelType
from llama_stack.distribution.datatypes import ModelInput, Provider, ToolGroupInput
from llama_stack.providers.inline.inference.sentence_transformers import (
SentenceTransformersInferenceConfig,
)
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.remote.inference.cerebras import CerebrasImplConfig
from llama_stack.providers.remote.inference.cerebras.models import MODEL_ENTRIES
from llama_stack.templates.template import (
DistributionTemplate,
RunConfigSettings,
get_model_registry,
)
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::cerebras", "inline::sentence-transformers"],
"safety": ["inline::llama-guard"],
"vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"agents": ["inline::meta-reference"],
"eval": ["inline::meta-reference"],
"datasetio": ["remote::huggingface", "inline::localfs"],
"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
"telemetry": ["inline::meta-reference"],
"tool_runtime": [
"remote::brave-search",
"remote::tavily-search",
"inline::rag-runtime",
],
}
name = "cerebras"
inference_provider = Provider(
provider_id="cerebras",
provider_type="remote::cerebras",
config=CerebrasImplConfig.sample_run_config(),
)
embedding_provider = Provider(
provider_id="sentence-transformers",
provider_type="inline::sentence-transformers",
config=SentenceTransformersInferenceConfig.sample_run_config(),
)
available_models = {
"cerebras": MODEL_ENTRIES,
}
default_models = get_model_registry(available_models)
embedding_model = ModelInput(
model_id="all-MiniLM-L6-v2",
provider_id="sentence-transformers",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 384,
},
)
vector_io_provider = Provider(
provider_id="faiss",
provider_type="inline::faiss",
config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
)
default_tool_groups = [
ToolGroupInput(
toolgroup_id="builtin::websearch",
provider_id="tavily-search",
),
ToolGroupInput(
toolgroup_id="builtin::rag",
provider_id="rag-runtime",
),
]
return DistributionTemplate(
name="cerebras",
distro_type="self_hosted",
description="Use Cerebras for running LLM inference",
container_image=None,
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
available_models_by_provider=available_models,
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider, embedding_provider],
"vector_io": [vector_io_provider],
},
default_models=default_models + [embedding_model],
default_shields=[],
default_tool_groups=default_tool_groups,
),
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"8321",
"Port for the Llama Stack distribution server",
),
"CEREBRAS_API_KEY": (
"",
"Cerebras API Key",
),
},
)

61
llama_stack/templates/cerebras/doc_template.md
View file

@ -1,61 +0,0 @@
# Cerebras Distribution
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
{{ providers_table }}
{% if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
{% endfor %}
{% endif %}
{% if default_models %}
### Models
The following models are available by default:
{% for model in default_models %}
- `{{ model.model_id }} {{ model.doc_string }}`
{% endfor %}
{% endif %}
### Prerequisite: API Keys
Make sure you have access to a Cerebras API Key. You can get one by visiting [cloud.cerebras.ai](https://cloud.cerebras.ai/).
## Running Llama Stack with Cerebras
You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-{{ name }} \
--config /root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env CEREBRAS_API_KEY=$CEREBRAS_API_KEY
```
### Via Conda
```bash
llama stack build --template cerebras --image-type conda
llama stack run ./run.yaml \
--port 8321 \
--env CEREBRAS_API_KEY=$CEREBRAS_API_KEY
```

145
llama_stack/templates/cerebras/run.yaml
View file

@ -1,145 +0,0 @@
version: 2
image_name: cerebras
apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: cerebras
provider_type: remote::cerebras
config:
base_url: https://api.cerebras.ai
api_key: ${env.CEREBRAS_API_KEY}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/cerebras}/faiss_store.db
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/cerebras}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/cerebras}/responses_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/cerebras}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/cerebras}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/cerebras}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/cerebras}/trace_store.db
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/cerebras}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/cerebras}/inference_store.db
models:
- metadata: {}
model_id: llama3.1-8b
provider_id: cerebras
provider_model_id: llama3.1-8b
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-8B-Instruct
provider_id: cerebras
provider_model_id: llama3.1-8b
model_type: llm
- metadata: {}
model_id: llama-3.3-70b
provider_id: cerebras
provider_model_id: llama-3.3-70b
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.3-70B-Instruct
provider_id: cerebras
provider_model_id: llama-3.3-70b
model_type: llm
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: sentence-transformers
model_type: embedding
shields: []
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321

7
llama_stack/templates/ci-tests/__init__.py
View file

@ -1,7 +0,0 @@
# 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.
from .ci_tests import get_distribution_template # noqa: F401

35
llama_stack/templates/ci-tests/build.yaml
View file

@ -1,35 +0,0 @@
version: 2
distribution_spec:
description: Distribution for running e2e tests in CI
providers:
inference:
- remote::fireworks
- inline::sentence-transformers
vector_io:
- inline::sqlite-vec
- remote::chromadb
- remote::pgvector
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
eval:
- inline::meta-reference
datasetio:
- remote::huggingface
- inline::localfs
scoring:
- inline::basic
- inline::llm-as-judge
- inline::braintrust
tool_runtime:
- remote::brave-search
- remote::tavily-search
- inline::rag-runtime
- remote::model-context-protocol
image_type: conda
additional_pip_packages:
- aiosqlite
- sqlalchemy[asyncio]

116
llama_stack/templates/ci-tests/ci_tests.py
View file

@ -1,116 +0,0 @@
# 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.
from llama_stack.apis.models import ModelType
from llama_stack.distribution.datatypes import (
ModelInput,
Provider,
ShieldInput,
ToolGroupInput,
)
from llama_stack.providers.inline.inference.sentence_transformers import (
SentenceTransformersInferenceConfig,
)
from llama_stack.providers.inline.vector_io.sqlite_vec.config import (
SQLiteVectorIOConfig,
)
from llama_stack.providers.remote.inference.fireworks.config import FireworksImplConfig
from llama_stack.providers.remote.inference.fireworks.models import MODEL_ENTRIES
from llama_stack.templates.template import (
DistributionTemplate,
RunConfigSettings,
get_model_registry,
)
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::fireworks", "inline::sentence-transformers"],
"vector_io": ["inline::sqlite-vec", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
"eval": ["inline::meta-reference"],
"datasetio": ["remote::huggingface", "inline::localfs"],
"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
"tool_runtime": [
"remote::brave-search",
"remote::tavily-search",
"inline::rag-runtime",
"remote::model-context-protocol",
],
}
name = "ci-tests"
inference_provider = Provider(
provider_id="fireworks",
provider_type="remote::fireworks",
config=FireworksImplConfig.sample_run_config(),
)
vector_io_provider = Provider(
provider_id="sqlite-vec",
provider_type="inline::sqlite-vec",
config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
)
embedding_provider = Provider(
provider_id="sentence-transformers",
provider_type="inline::sentence-transformers",
config=SentenceTransformersInferenceConfig.sample_run_config(),
)
default_tool_groups = [
ToolGroupInput(
toolgroup_id="builtin::websearch",
provider_id="tavily-search",
),
ToolGroupInput(
toolgroup_id="builtin::rag",
provider_id="rag-runtime",
),
]
available_models = {
"fireworks": MODEL_ENTRIES,
}
default_models = get_model_registry(available_models)
embedding_model = ModelInput(
model_id="all-MiniLM-L6-v2",
provider_id="sentence-transformers",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 384,
},
)
return DistributionTemplate(
name=name,
distro_type="self_hosted",
description="Distribution for running e2e tests in CI",
container_image=None,
template_path=None,
providers=providers,
available_models_by_provider=available_models,
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider, embedding_provider],
"vector_io": [vector_io_provider],
},
default_models=default_models + [embedding_model],
default_tool_groups=default_tool_groups,
default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
),
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"8321",
"Port for the Llama Stack distribution server",
),
"FIREWORKS_API_KEY": (
"",
"Fireworks API Key",
),
},
)

243
llama_stack/templates/ci-tests/run.yaml
View file

@ -1,243 +0,0 @@
version: 2
image_name: ci-tests
apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: fireworks
provider_type: remote::fireworks
config:
url: https://api.fireworks.ai/inference/v1
api_key: ${env.FIREWORKS_API_KEY}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
vector_io:
- provider_id: sqlite-vec
provider_type: inline::sqlite-vec
config:
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/sqlite_vec.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/trace_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
- provider_id: model-context-protocol
provider_type: remote::model-context-protocol
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/inference_store.db
models:
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p1-8b-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-8b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-8B-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-8b-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p1-70b-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-70b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-70B-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-70b-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p1-405b-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-405b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-405B-Instruct-FP8
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-405b-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p2-3b-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-3b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-3B-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-3b-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p2-11b-vision-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-11b-vision-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-11B-Vision-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-11b-vision-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p2-90b-vision-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-90b-vision-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-90B-Vision-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-90b-vision-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p3-70b-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p3-70b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.3-70B-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p3-70b-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-guard-3-8b
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-guard-3-8b
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-Guard-3-8B
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-guard-3-8b
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-guard-3-11b-vision
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-guard-3-11b-vision
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-Guard-3-11B-Vision
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-guard-3-11b-vision
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama4-scout-instruct-basic
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama4-scout-instruct-basic
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-4-Scout-17B-16E-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama4-scout-instruct-basic
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama4-maverick-instruct-basic
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama4-maverick-instruct-basic
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-4-Maverick-17B-128E-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama4-maverick-instruct-basic
model_type: llm
- metadata:
embedding_dimension: 768
context_length: 8192
model_id: nomic-ai/nomic-embed-text-v1.5
provider_id: fireworks
provider_model_id: nomic-ai/nomic-embed-text-v1.5
model_type: embedding
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: sentence-transformers
model_type: embedding
shields:
- shield_id: meta-llama/Llama-Guard-3-8B
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321

7
llama_stack/templates/dell/__init__.py
View file

@ -1,7 +0,0 @@
# 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.
from .dell import get_distribution_template # noqa: F401

35
llama_stack/templates/dell/build.yaml
View file

@ -1,35 +0,0 @@
version: 2
distribution_spec:
description: Dell's distribution of Llama Stack. TGI inference via Dell's custom
container
providers:
inference:
- remote::tgi
- inline::sentence-transformers
vector_io:
- inline::faiss
- remote::chromadb
- remote::pgvector
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
eval:
- inline::meta-reference
datasetio:
- remote::huggingface
- inline::localfs
scoring:
- inline::basic
- inline::llm-as-judge
- inline::braintrust
tool_runtime:
- remote::brave-search
- remote::tavily-search
- inline::rag-runtime
image_type: conda
additional_pip_packages:
- aiosqlite
- sqlalchemy[asyncio]

142
llama_stack/templates/dell/dell.py
View file

@ -1,142 +0,0 @@
# 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.
from llama_stack.apis.models import ModelType
from llama_stack.distribution.datatypes import (
ModelInput,
Provider,
ShieldInput,
ToolGroupInput,
)
from llama_stack.providers.inline.inference.sentence_transformers import (
SentenceTransformersInferenceConfig,
)
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::tgi", "inline::sentence-transformers"],
"vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
"eval": ["inline::meta-reference"],
"datasetio": ["remote::huggingface", "inline::localfs"],
"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
"tool_runtime": [
"remote::brave-search",
"remote::tavily-search",
"inline::rag-runtime",
],
}
name = "dell"
inference_provider = Provider(
provider_id="tgi0",
provider_type="remote::tgi",
config={
"url": "${env.DEH_URL}",
},
)
safety_inference_provider = Provider(
provider_id="tgi1",
provider_type="remote::tgi",
config={
"url": "${env.DEH_SAFETY_URL}",
},
)
embedding_provider = Provider(
provider_id="sentence-transformers",
provider_type="inline::sentence-transformers",
config=SentenceTransformersInferenceConfig.sample_run_config(),
)
chromadb_provider = Provider(
provider_id="chromadb",
provider_type="remote::chromadb",
config={
"url": "${env.CHROMA_URL}",
},
)
inference_model = ModelInput(
model_id="${env.INFERENCE_MODEL}",
provider_id="tgi0",
)
safety_model = ModelInput(
model_id="${env.SAFETY_MODEL}",
provider_id="tgi1",
)
embedding_model = ModelInput(
model_id="all-MiniLM-L6-v2",
provider_id="sentence-transformers",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 384,
},
)
default_tool_groups = [
ToolGroupInput(
toolgroup_id="builtin::websearch",
provider_id="brave-search",
),
ToolGroupInput(
toolgroup_id="builtin::rag",
provider_id="rag-runtime",
),
]
return DistributionTemplate(
name=name,
distro_type="self_hosted",
description="Dell's distribution of Llama Stack. TGI inference via Dell's custom container",
container_image=None,
providers=providers,
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider, embedding_provider],
"vector_io": [chromadb_provider],
},
default_models=[inference_model, embedding_model],
default_tool_groups=default_tool_groups,
),
"run-with-safety.yaml": RunConfigSettings(
provider_overrides={
"inference": [
inference_provider,
safety_inference_provider,
embedding_provider,
],
"vector_io": [chromadb_provider],
},
default_models=[inference_model, safety_model, embedding_model],
default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}")],
default_tool_groups=default_tool_groups,
),
},
run_config_env_vars={
"DEH_URL": (
"http://0.0.0.0:8181",
"URL for the Dell inference server",
),
"DEH_SAFETY_URL": (
"http://0.0.0.0:8282",
"URL for the Dell safety inference server",
),
"CHROMA_URL": (
"http://localhost:6601",
"URL for the Chroma server",
),
"INFERENCE_MODEL": (
"meta-llama/Llama-3.2-3B-Instruct",
"Inference model loaded into the TGI server",
),
"SAFETY_MODEL": (
"meta-llama/Llama-Guard-3-1B",
"Name of the safety (Llama-Guard) model to use",
),
},
)

178
llama_stack/templates/dell/doc_template.md
View file

@ -1,178 +0,0 @@
---
orphan: true
---
# Dell Distribution of Llama Stack
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
{{ providers_table }}
You can use this distribution if you have GPUs and want to run an independent TGI or Dell Enterprise Hub container for running inference.
{% if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
{% endfor %}
{% endif %}
## Setting up Inference server using Dell Enterprise Hub's custom TGI container.
NOTE: This is a placeholder to run inference with TGI. This will be updated to use [Dell Enterprise Hub's containers](https://dell.huggingface.co/authenticated/models) once verified.
```bash
export INFERENCE_PORT=8181
export DEH_URL=http://0.0.0.0:$INFERENCE_PORT
export INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct
export CHROMADB_HOST=localhost
export CHROMADB_PORT=6601
export CHROMA_URL=http://$CHROMADB_HOST:$CHROMADB_PORT
export CUDA_VISIBLE_DEVICES=0
export LLAMA_STACK_PORT=8321
docker run --rm -it \
--pull always \
--network host \
-v $HOME/.cache/huggingface:/data \
-e HF_TOKEN=$HF_TOKEN \
-p $INFERENCE_PORT:$INFERENCE_PORT \
--gpus $CUDA_VISIBLE_DEVICES \
ghcr.io/huggingface/text-generation-inference \
--dtype bfloat16 \
--usage-stats off \
--sharded false \
--cuda-memory-fraction 0.7 \
--model-id $INFERENCE_MODEL \
--port $INFERENCE_PORT --hostname 0.0.0.0
```
If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a TGI with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
```bash
export SAFETY_INFERENCE_PORT=8282
export DEH_SAFETY_URL=http://0.0.0.0:$SAFETY_INFERENCE_PORT
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
export CUDA_VISIBLE_DEVICES=1
docker run --rm -it \
--pull always \
--network host \
-v $HOME/.cache/huggingface:/data \
-e HF_TOKEN=$HF_TOKEN \
-p $SAFETY_INFERENCE_PORT:$SAFETY_INFERENCE_PORT \
--gpus $CUDA_VISIBLE_DEVICES \
ghcr.io/huggingface/text-generation-inference \
--dtype bfloat16 \
--usage-stats off \
--sharded false \
--cuda-memory-fraction 0.7 \
--model-id $SAFETY_MODEL \
--hostname 0.0.0.0 \
--port $SAFETY_INFERENCE_PORT
```
## Dell distribution relies on ChromaDB for vector database usage
You can start a chroma-db easily using docker.
```bash
# This is where the indices are persisted
mkdir -p $HOME/chromadb
podman run --rm -it \
--network host \
--name chromadb \
-v $HOME/chromadb:/chroma/chroma \
-e IS_PERSISTENT=TRUE \
chromadb/chroma:latest \
--port $CHROMADB_PORT \
--host $CHROMADB_HOST
```
## Running Llama Stack
Now you are ready to run Llama Stack with TGI as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
docker run -it \
--pull always \
--network host \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v $HOME/.llama:/root/.llama \
# NOTE: mount the llama-stack directory if testing local changes else not needed
-v /home/hjshah/git/llama-stack:/app/llama-stack-source \
# localhost/distribution-dell:dev if building / testing locally
llamastack/distribution-{{ name }}\
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env DEH_URL=$DEH_URL \
--env CHROMA_URL=$CHROMA_URL
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
# You need a local checkout of llama-stack to run this, get it using
# git clone https://github.com/meta-llama/llama-stack.git
cd /path/to/llama-stack
export SAFETY_INFERENCE_PORT=8282
export DEH_SAFETY_URL=http://0.0.0.0:$SAFETY_INFERENCE_PORT
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v $HOME/.llama:/root/.llama \
-v ./llama_stack/templates/tgi/run-with-safety.yaml:/root/my-run.yaml \
llamastack/distribution-{{ name }} \
--config /root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env DEH_URL=$DEH_URL \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env DEH_SAFETY_URL=$DEH_SAFETY_URL \
--env CHROMA_URL=$CHROMA_URL
```
### Via Conda
Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
```bash
llama stack build --template {{ name }} --image-type conda
llama stack run {{ name }}
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env DEH_URL=$DEH_URL \
--env CHROMA_URL=$CHROMA_URL
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
llama stack run ./run-with-safety.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env DEH_URL=$DEH_URL \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env DEH_SAFETY_URL=$DEH_SAFETY_URL \
--env CHROMA_URL=$CHROMA_URL
```

134
llama_stack/templates/dell/run-with-safety.yaml
View file

@ -1,134 +0,0 @@
version: 2
image_name: dell
apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: tgi0
provider_type: remote::tgi
config:
url: ${env.DEH_URL}
- provider_id: tgi1
provider_type: remote::tgi
config:
url: ${env.DEH_SAFETY_URL}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
vector_io:
- provider_id: chromadb
provider_type: remote::chromadb
config:
url: ${env.CHROMA_URL}
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/trace_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/inference_store.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: tgi0
model_type: llm
- metadata: {}
model_id: ${env.SAFETY_MODEL}
provider_id: tgi1
model_type: llm
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: sentence-transformers
model_type: embedding
shields:
- shield_id: ${env.SAFETY_MODEL}
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: brave-search
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321

125
llama_stack/templates/dell/run.yaml
View file

@ -1,125 +0,0 @@
version: 2
image_name: dell
apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: tgi0
provider_type: remote::tgi
config:
url: ${env.DEH_URL}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
vector_io:
- provider_id: chromadb
provider_type: remote::chromadb
config:
url: ${env.CHROMA_URL}
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/trace_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/inference_store.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: tgi0
model_type: llm
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: sentence-transformers
model_type: embedding
shields: []
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: brave-search
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321

30
llama_stack/templates/experimental-post-training/build.yaml
View file

@ -1,30 +0,0 @@
version: '2'
name: experimental-post-training
distribution_spec:
description: Experimental template for post training
container_image: null
providers:
inference:
- inline::meta-reference
- remote::ollama
eval:
- inline::meta-reference
scoring:
- inline::basic
- inline::braintrust
post_training:
- inline::huggingface
datasetio:
- inline::localfs
- remote::huggingface
telemetry:
- inline::meta-reference
agents:
- inline::meta-reference
safety:
- inline::llama-guard
vector_io:
- inline::faiss
tool_runtime:
- remote::brave-search
image_type: conda

107
llama_stack/templates/experimental-post-training/run.yaml
View file

@ -1,107 +0,0 @@
version: '2'
image_name: experimental-post-training
container_image: null
conda_env: experimental-post-training
apis:
- agents
- datasetio
- eval
- inference
- vector_io
- safety
- scoring
- telemetry
- post_training
- tool_runtime
providers:
inference:
- provider_id: meta-reference-inference
provider_type: inline::meta-reference
config:
max_seq_len: 4096
checkpoint_dir: null
create_distributed_process_group: False
- provider_id: ollama
provider_type: remote::ollama
config:
url: ${env.OLLAMA_URL:=http://localhost:11434}
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/meta_reference_eval.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
datasetio:
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/experimental-post-training}/localfs_datasetio.db
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/huggingface}/huggingface_datasetio.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config: {}
post_training:
- provider_id: huggingface
provider_type: inline::huggingface
config:
checkpoint_format: huggingface
distributed_backend: null
device: cpu
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/experimental-post-training}/agents_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/experimental-post-training}/faiss_store.db
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
metadata_store:
namespace: null
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/experimental-post-training}/registry.db
models: []
shields: []
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []

7
llama_stack/templates/fireworks/__init__.py
View file

@ -1,7 +0,0 @@
# 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.
from .fireworks import get_distribution_template # noqa: F401

38
llama_stack/templates/fireworks/build.yaml
View file

@ -1,38 +0,0 @@
version: 2
distribution_spec:
description: Use Fireworks.AI for running LLM inference
providers:
inference:
- remote::fireworks
- inline::sentence-transformers
vector_io:
- inline::faiss
- remote::chromadb
- remote::pgvector
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
eval:
- inline::meta-reference
datasetio:
- remote::huggingface
- inline::localfs
scoring:
- inline::basic
- inline::llm-as-judge
- inline::braintrust
files:
- inline::localfs
tool_runtime:
- remote::brave-search
- remote::tavily-search
- remote::wolfram-alpha
- inline::rag-runtime
- remote::model-context-protocol
image_type: conda
additional_pip_packages:
- aiosqlite
- sqlalchemy[asyncio]

69
llama_stack/templates/fireworks/doc_template.md
View file

@ -1,69 +0,0 @@
---
orphan: true
---
# Fireworks Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
{{ providers_table }}
{% if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
{% endfor %}
{% endif %}
{% if default_models %}
### Models
The following models are available by default:
{% for model in default_models %}
- `{{ model.model_id }} {{ model.doc_string }}`
{% endfor %}
{% endif %}
### Prerequisite: API Keys
Make sure you have access to a Fireworks API Key. You can get one by visiting [fireworks.ai](https://fireworks.ai/).
## Running Llama Stack with Fireworks
You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
llamastack/distribution-{{ name }} \
--port $LLAMA_STACK_PORT \
--env FIREWORKS_API_KEY=$FIREWORKS_API_KEY
```
### Via Conda
```bash
llama stack build --template fireworks --image-type conda
llama stack run ./run.yaml \
--port $LLAMA_STACK_PORT \
--env FIREWORKS_API_KEY=$FIREWORKS_API_KEY
```

177
llama_stack/templates/fireworks/fireworks.py
View file

@ -1,177 +0,0 @@
# 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.
from pathlib import Path
from llama_stack.apis.models import ModelType
from llama_stack.distribution.datatypes import (
ModelInput,
Provider,
ShieldInput,
ToolGroupInput,
)
from llama_stack.providers.inline.files.localfs.config import LocalfsFilesImplConfig
from llama_stack.providers.inline.inference.sentence_transformers import (
SentenceTransformersInferenceConfig,
)
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.remote.inference.fireworks.config import FireworksImplConfig
from llama_stack.providers.remote.inference.fireworks.models import MODEL_ENTRIES
from llama_stack.templates.template import (
DistributionTemplate,
RunConfigSettings,
get_model_registry,
)
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::fireworks", "inline::sentence-transformers"],
"vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
"eval": ["inline::meta-reference"],
"datasetio": ["remote::huggingface", "inline::localfs"],
"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
"files": ["inline::localfs"],
"tool_runtime": [
"remote::brave-search",
"remote::tavily-search",
"remote::wolfram-alpha",
"inline::rag-runtime",
"remote::model-context-protocol",
],
}
name = "fireworks"
inference_provider = Provider(
provider_id="fireworks",
provider_type="remote::fireworks",
config=FireworksImplConfig.sample_run_config(),
)
embedding_provider = Provider(
provider_id="sentence-transformers",
provider_type="inline::sentence-transformers",
config=SentenceTransformersInferenceConfig.sample_run_config(),
)
vector_io_provider = Provider(
provider_id="faiss",
provider_type="inline::faiss",
config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
)
files_provider = Provider(
provider_id="meta-reference-files",
provider_type="inline::localfs",
config=LocalfsFilesImplConfig.sample_run_config(f"~/.llama/distributions/{name}"),
)
available_models = {
"fireworks": MODEL_ENTRIES,
}
default_models = get_model_registry(available_models)
embedding_model = ModelInput(
model_id="all-MiniLM-L6-v2",
provider_id="sentence-transformers",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 384,
},
)
default_tool_groups = [
ToolGroupInput(
toolgroup_id="builtin::websearch",
provider_id="tavily-search",
),
ToolGroupInput(
toolgroup_id="builtin::wolfram_alpha",
provider_id="wolfram-alpha",
),
ToolGroupInput(
toolgroup_id="builtin::rag",
provider_id="rag-runtime",
),
]
return DistributionTemplate(
name=name,
distro_type="self_hosted",
description="Use Fireworks.AI for running LLM inference",
container_image=None,
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
available_models_by_provider=available_models,
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider, embedding_provider],
"vector_io": [vector_io_provider],
"files": [files_provider],
},
default_models=default_models + [embedding_model],
default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
default_tool_groups=default_tool_groups,
),
"run-with-safety.yaml": RunConfigSettings(
provider_overrides={
"inference": [
inference_provider,
embedding_provider,
],
"vector_io": [vector_io_provider],
"files": [files_provider],
"safety": [
Provider(
provider_id="llama-guard",
provider_type="inline::llama-guard",
config={},
),
Provider(
provider_id="llama-guard-vision",
provider_type="inline::llama-guard",
config={},
),
Provider(
provider_id="code-scanner",
provider_type="inline::code-scanner",
config={},
),
],
},
default_models=[
*default_models,
embedding_model,
],
default_shields=[
ShieldInput(
shield_id="meta-llama/Llama-Guard-3-8B",
provider_id="llama-guard",
),
ShieldInput(
shield_id="meta-llama/Llama-Guard-3-11B-Vision",
provider_id="llama-guard-vision",
),
ShieldInput(
shield_id="CodeScanner",
provider_id="code-scanner",
),
],
default_tool_groups=default_tool_groups,
),
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"8321",
"Port for the Llama Stack distribution server",
),
"FIREWORKS_API_KEY": (
"",
"Fireworks.AI API Key",
),
},
)

45
llama_stack/templates/fireworks/remote-hosted-report.md
View file

@ -1,45 +0,0 @@
# Report for fireworks distribution
## Supported Models
| Model Descriptor | fireworks |
|:---|:---|
| meta-llama/Llama-3-8B-Instruct | ❌ |
| meta-llama/Llama-3-70B-Instruct | ❌ |
| meta-llama/Llama-3.1-8B-Instruct | ❌ |
| meta-llama/Llama-3.1-70B-Instruct | ❌ |
| meta-llama/Llama-3.1-405B-Instruct-FP8 | ❌ |
| meta-llama/Llama-3.2-1B-Instruct | ❌ |
| meta-llama/Llama-3.2-3B-Instruct | ❌ |
| meta-llama/Llama-3.2-11B-Vision-Instruct | ❌ |
| meta-llama/Llama-3.2-90B-Vision-Instruct | ❌ |
| meta-llama/Llama-3.3-70B-Instruct | ❌ |
| meta-llama/Llama-Guard-3-11B-Vision | ❌ |
| meta-llama/Llama-Guard-3-1B | ❌ |
| meta-llama/Llama-Guard-3-8B | ❌ |
| meta-llama/Llama-Guard-2-8B | ❌ |
## Inference
| Model | API | Capability | Test | Status |
|:----- |:-----|:-----|:-----|:-----|
| Text | /chat_completion | streaming | test_text_chat_completion_streaming | ❌ |
| Vision | /chat_completion | streaming | test_image_chat_completion_streaming | ❌ |
| Vision | /chat_completion | non_streaming | test_image_chat_completion_non_streaming | ❌ |
| Text | /chat_completion | non_streaming | test_text_chat_completion_non_streaming | ❌ |
| Text | /chat_completion | tool_calling | test_text_chat_completion_with_tool_calling_and_streaming | ❌ |
| Text | /chat_completion | tool_calling | test_text_chat_completion_with_tool_calling_and_non_streaming | ❌ |
| Text | /completion | streaming | test_text_completion_streaming | ❌ |
| Text | /completion | non_streaming | test_text_completion_non_streaming | ❌ |
| Text | /completion | structured_output | test_text_completion_structured_output | ❌ |
## Memory:
| API | Capability | Test | Status |
|:-----|:-----|:-----|:-----|
| /insert, /query | inline | test_memory_bank_insert_inline_and_query | ❌ |
| /insert, /query | url | test_memory_bank_insert_from_url_and_query | ❌ |
## Agents
| API | Capability | Test | Status |
|:-----|:-----|:-----|:-----|
| create_agent_turn | rag | test_rag_agent | ❌ |
| create_agent_turn | custom_tool | test_custom_tool | ❌ |
| create_agent_turn | code_execution | test_code_execution | ❌ |

271
llama_stack/templates/fireworks/run-with-safety.yaml
View file

@ -1,271 +0,0 @@
version: 2
image_name: fireworks
apis:
- agents
- datasetio
- eval
- files
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: fireworks
provider_type: remote::fireworks
config:
url: https://api.fireworks.ai/inference/v1
api_key: ${env.FIREWORKS_API_KEY}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
- provider_id: llama-guard-vision
provider_type: inline::llama-guard
config: {}
- provider_id: code-scanner
provider_type: inline::code-scanner
config: {}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/trace_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
files:
- provider_id: meta-reference-files
provider_type: inline::localfs
config:
storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/fireworks/files}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/files_metadata.db
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: wolfram-alpha
provider_type: remote::wolfram-alpha
config:
api_key: ${env.WOLFRAM_ALPHA_API_KEY:+}
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
- provider_id: model-context-protocol
provider_type: remote::model-context-protocol
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/inference_store.db
models:
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p1-8b-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-8b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-8B-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-8b-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p1-70b-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-70b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-70B-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-70b-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p1-405b-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-405b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-405B-Instruct-FP8
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-405b-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p2-3b-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-3b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-3B-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-3b-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p2-11b-vision-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-11b-vision-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-11B-Vision-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-11b-vision-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p2-90b-vision-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-90b-vision-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-90B-Vision-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-90b-vision-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p3-70b-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p3-70b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.3-70B-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p3-70b-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-guard-3-8b
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-guard-3-8b
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-Guard-3-8B
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-guard-3-8b
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-guard-3-11b-vision
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-guard-3-11b-vision
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-Guard-3-11B-Vision
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-guard-3-11b-vision
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama4-scout-instruct-basic
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama4-scout-instruct-basic
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-4-Scout-17B-16E-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama4-scout-instruct-basic
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama4-maverick-instruct-basic
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama4-maverick-instruct-basic
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-4-Maverick-17B-128E-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama4-maverick-instruct-basic
model_type: llm
- metadata:
embedding_dimension: 768
context_length: 8192
model_id: nomic-ai/nomic-embed-text-v1.5
provider_id: fireworks
provider_model_id: nomic-ai/nomic-embed-text-v1.5
model_type: embedding
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: sentence-transformers
model_type: embedding
shields:
- shield_id: meta-llama/Llama-Guard-3-8B
provider_id: llama-guard
- shield_id: meta-llama/Llama-Guard-3-11B-Vision
provider_id: llama-guard-vision
- shield_id: CodeScanner
provider_id: code-scanner
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::wolfram_alpha
provider_id: wolfram-alpha
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321

261
llama_stack/templates/fireworks/run.yaml
View file

@ -1,261 +0,0 @@
version: 2
image_name: fireworks
apis:
- agents
- datasetio
- eval
- files
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: fireworks
provider_type: remote::fireworks
config:
url: https://api.fireworks.ai/inference/v1
api_key: ${env.FIREWORKS_API_KEY}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/trace_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
files:
- provider_id: meta-reference-files
provider_type: inline::localfs
config:
storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/fireworks/files}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/files_metadata.db
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: wolfram-alpha
provider_type: remote::wolfram-alpha
config:
api_key: ${env.WOLFRAM_ALPHA_API_KEY:+}
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
- provider_id: model-context-protocol
provider_type: remote::model-context-protocol
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/fireworks}/inference_store.db
models:
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p1-8b-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-8b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-8B-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-8b-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p1-70b-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-70b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-70B-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-70b-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p1-405b-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-405b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-405B-Instruct-FP8
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p1-405b-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p2-3b-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-3b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-3B-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-3b-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p2-11b-vision-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-11b-vision-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-11B-Vision-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-11b-vision-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p2-90b-vision-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-90b-vision-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-90B-Vision-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p2-90b-vision-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-v3p3-70b-instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p3-70b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.3-70B-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-v3p3-70b-instruct
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-guard-3-8b
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-guard-3-8b
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-Guard-3-8B
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-guard-3-8b
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama-guard-3-11b-vision
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-guard-3-11b-vision
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-Guard-3-11B-Vision
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama-guard-3-11b-vision
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama4-scout-instruct-basic
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama4-scout-instruct-basic
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-4-Scout-17B-16E-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama4-scout-instruct-basic
model_type: llm
- metadata: {}
model_id: accounts/fireworks/models/llama4-maverick-instruct-basic
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama4-maverick-instruct-basic
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-4-Maverick-17B-128E-Instruct
provider_id: fireworks
provider_model_id: accounts/fireworks/models/llama4-maverick-instruct-basic
model_type: llm
- metadata:
embedding_dimension: 768
context_length: 8192
model_id: nomic-ai/nomic-embed-text-v1.5
provider_id: fireworks
provider_model_id: nomic-ai/nomic-embed-text-v1.5
model_type: embedding
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: sentence-transformers
model_type: embedding
shields:
- shield_id: meta-llama/Llama-Guard-3-8B
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::wolfram_alpha
provider_id: wolfram-alpha
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321

7
llama_stack/templates/groq/__init__.py
View file

@ -1,7 +0,0 @@
# 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.
from .groq import get_distribution_template # noqa: F401

31
llama_stack/templates/groq/build.yaml
View file

@ -1,31 +0,0 @@
version: 2
distribution_spec:
description: Use Groq for running LLM inference
providers:
inference:
- remote::groq
vector_io:
- inline::faiss
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
eval:
- inline::meta-reference
datasetio:
- remote::huggingface
- inline::localfs
scoring:
- inline::basic
- inline::llm-as-judge
- inline::braintrust
tool_runtime:
- remote::brave-search
- remote::tavily-search
- inline::rag-runtime
image_type: conda
additional_pip_packages:
- aiosqlite
- sqlalchemy[asyncio]

69
llama_stack/templates/groq/doc_template.md
View file

@ -1,69 +0,0 @@
---
orphan: true
---
# Groq Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
{{ providers_table }}
{% if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
{% endfor %}
{% endif %}
{% if default_models %}
### Models
The following models are available by default:
{% for model in default_models %}
- `{{ model.model_id }} {{ model.doc_string }}`
{% endfor %}
{% endif %}
### Prerequisite: API Keys
Make sure you have access to a Groq API Key. You can get one by visiting [Groq](https://api.groq.com/).
## Running Llama Stack with Groq
You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
llamastack/distribution-{{ name }} \
--port $LLAMA_STACK_PORT \
--env GROQ_API_KEY=$GROQ_API_KEY
```
### Via Conda
```bash
llama stack build --template groq --image-type conda
llama stack run ./run.yaml \
--port $LLAMA_STACK_PORT \
--env GROQ_API_KEY=$GROQ_API_KEY
```

103
llama_stack/templates/groq/groq.py
View file

@ -1,103 +0,0 @@
# 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.
from pathlib import Path
from llama_stack.apis.models import ModelType
from llama_stack.distribution.datatypes import ModelInput, Provider, ToolGroupInput
from llama_stack.providers.inline.inference.sentence_transformers import (
SentenceTransformersInferenceConfig,
)
from llama_stack.providers.remote.inference.groq import GroqConfig
from llama_stack.providers.remote.inference.groq.models import MODEL_ENTRIES
from llama_stack.templates.template import (
DistributionTemplate,
RunConfigSettings,
get_model_registry,
)
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::groq"],
"vector_io": ["inline::faiss"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
"eval": ["inline::meta-reference"],
"datasetio": ["remote::huggingface", "inline::localfs"],
"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
"tool_runtime": [
"remote::brave-search",
"remote::tavily-search",
"inline::rag-runtime",
],
}
name = "groq"
inference_provider = Provider(
provider_id=name,
provider_type=f"remote::{name}",
config=GroqConfig.sample_run_config(),
)
embedding_provider = Provider(
provider_id="sentence-transformers",
provider_type="inline::sentence-transformers",
config=SentenceTransformersInferenceConfig.sample_run_config(),
)
embedding_model = ModelInput(
model_id="all-MiniLM-L6-v2",
provider_id="sentence-transformers",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 384,
},
)
available_models = {
"groq": MODEL_ENTRIES,
}
default_models = get_model_registry(available_models)
default_tool_groups = [
ToolGroupInput(
toolgroup_id="builtin::websearch",
provider_id="tavily-search",
),
ToolGroupInput(
toolgroup_id="builtin::rag",
provider_id="rag-runtime",
),
]
return DistributionTemplate(
name=name,
distro_type="self_hosted",
description="Use Groq for running LLM inference",
docker_image=None,
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
available_models_by_provider=available_models,
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider, embedding_provider],
},
default_models=default_models + [embedding_model],
default_tool_groups=default_tool_groups,
),
},
run_config_env_vars={
"LLAMASTACK_PORT": (
"8321",
"Port for the Llama Stack distribution server",
),
"GROQ_API_KEY": (
"",
"Groq API Key",
),
},
)

210
llama_stack/templates/groq/run.yaml
View file

@ -1,210 +0,0 @@
version: 2
image_name: groq
apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: groq
provider_type: remote::groq
config:
url: https://api.groq.com
api_key: ${env.GROQ_API_KEY}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/groq}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/groq}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/groq}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/groq}/trace_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/groq}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/groq}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/groq}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/groq}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/groq}/inference_store.db
models:
- metadata: {}
model_id: groq/llama3-8b-8192
provider_id: groq
provider_model_id: groq/llama3-8b-8192
model_type: llm
- metadata: {}
model_id: groq/meta-llama/Llama-3.1-8B-Instruct
provider_id: groq
provider_model_id: groq/llama3-8b-8192
model_type: llm
- metadata: {}
model_id: groq/llama-3.1-8b-instant
provider_id: groq
provider_model_id: groq/llama-3.1-8b-instant
model_type: llm
- metadata: {}
model_id: groq/llama3-70b-8192
provider_id: groq
provider_model_id: groq/llama3-70b-8192
model_type: llm
- metadata: {}
model_id: groq/meta-llama/Llama-3-70B-Instruct
provider_id: groq
provider_model_id: groq/llama3-70b-8192
model_type: llm
- metadata: {}
model_id: groq/llama-3.3-70b-versatile
provider_id: groq
provider_model_id: groq/llama-3.3-70b-versatile
model_type: llm
- metadata: {}
model_id: groq/meta-llama/Llama-3.3-70B-Instruct
provider_id: groq
provider_model_id: groq/llama-3.3-70b-versatile
model_type: llm
- metadata: {}
model_id: groq/llama-3.2-3b-preview
provider_id: groq
provider_model_id: groq/llama-3.2-3b-preview
model_type: llm
- metadata: {}
model_id: groq/meta-llama/Llama-3.2-3B-Instruct
provider_id: groq
provider_model_id: groq/llama-3.2-3b-preview
model_type: llm
- metadata: {}
model_id: groq/llama-4-scout-17b-16e-instruct
provider_id: groq
provider_model_id: groq/llama-4-scout-17b-16e-instruct
model_type: llm
- metadata: {}
model_id: groq/meta-llama/Llama-4-Scout-17B-16E-Instruct
provider_id: groq
provider_model_id: groq/llama-4-scout-17b-16e-instruct
model_type: llm
- metadata: {}
model_id: groq/meta-llama/llama-4-scout-17b-16e-instruct
provider_id: groq
provider_model_id: groq/meta-llama/llama-4-scout-17b-16e-instruct
model_type: llm
- metadata: {}
model_id: groq/meta-llama/Llama-4-Scout-17B-16E-Instruct
provider_id: groq
provider_model_id: groq/meta-llama/llama-4-scout-17b-16e-instruct
model_type: llm
- metadata: {}
model_id: groq/llama-4-maverick-17b-128e-instruct
provider_id: groq
provider_model_id: groq/llama-4-maverick-17b-128e-instruct
model_type: llm
- metadata: {}
model_id: groq/meta-llama/Llama-4-Maverick-17B-128E-Instruct
provider_id: groq
provider_model_id: groq/llama-4-maverick-17b-128e-instruct
model_type: llm
- metadata: {}
model_id: groq/meta-llama/llama-4-maverick-17b-128e-instruct
provider_id: groq
provider_model_id: groq/meta-llama/llama-4-maverick-17b-128e-instruct
model_type: llm
- metadata: {}
model_id: groq/meta-llama/Llama-4-Maverick-17B-128E-Instruct
provider_id: groq
provider_model_id: groq/meta-llama/llama-4-maverick-17b-128e-instruct
model_type: llm
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: sentence-transformers
model_type: embedding
shields: []
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321

7
llama_stack/templates/hf-endpoint/__init__.py
View file

@ -1,7 +0,0 @@
# 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.
from .hf_endpoint import get_distribution_template # noqa: F401

34
llama_stack/templates/hf-endpoint/build.yaml
View file

@ -1,34 +0,0 @@
version: 2
distribution_spec:
description: Use (an external) Hugging Face Inference Endpoint for running LLM inference
providers:
inference:
- remote::hf::endpoint
vector_io:
- inline::faiss
- remote::chromadb
- remote::pgvector
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
eval:
- inline::meta-reference
datasetio:
- remote::huggingface
- inline::localfs
scoring:
- inline::basic
- inline::llm-as-judge
- inline::braintrust
tool_runtime:
- remote::brave-search
- remote::tavily-search
- inline::rag-runtime
- remote::model-context-protocol
image_type: conda
additional_pip_packages:
- aiosqlite
- sqlalchemy[asyncio]

149
llama_stack/templates/hf-endpoint/hf_endpoint.py
View file

@ -1,149 +0,0 @@
# 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.
from llama_stack.apis.models import ModelType
from llama_stack.distribution.datatypes import (
ModelInput,
Provider,
ShieldInput,
ToolGroupInput,
)
from llama_stack.providers.inline.inference.sentence_transformers import (
SentenceTransformersInferenceConfig,
)
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.remote.inference.tgi import InferenceEndpointImplConfig
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::hf::endpoint"],
"vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
"eval": ["inline::meta-reference"],
"datasetio": ["remote::huggingface", "inline::localfs"],
"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
"tool_runtime": [
"remote::brave-search",
"remote::tavily-search",
"inline::rag-runtime",
"remote::model-context-protocol",
],
}
name = "hf-endpoint"
inference_provider = Provider(
provider_id="hf-endpoint",
provider_type="remote::hf::endpoint",
config=InferenceEndpointImplConfig.sample_run_config(),
)
embedding_provider = Provider(
provider_id="sentence-transformers",
provider_type="inline::sentence-transformers",
config=SentenceTransformersInferenceConfig.sample_run_config(),
)
vector_io_provider = Provider(
provider_id="faiss",
provider_type="inline::faiss",
config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
)
inference_model = ModelInput(
model_id="${env.INFERENCE_MODEL}",
provider_id="hf-endpoint",
)
safety_model = ModelInput(
model_id="${env.SAFETY_MODEL}",
provider_id="hf-endpoint-safety",
)
embedding_model = ModelInput(
model_id="all-MiniLM-L6-v2",
provider_id="sentence-transformers",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 384,
},
)
default_tool_groups = [
ToolGroupInput(
toolgroup_id="builtin::websearch",
provider_id="tavily-search",
),
ToolGroupInput(
toolgroup_id="builtin::rag",
provider_id="rag-runtime",
),
]
return DistributionTemplate(
name=name,
distro_type="self_hosted",
description="Use (an external) Hugging Face Inference Endpoint for running LLM inference",
container_image=None,
template_path=None,
providers=providers,
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider, embedding_provider],
"vector_io": [vector_io_provider],
},
default_models=[inference_model, embedding_model],
default_tool_groups=default_tool_groups,
),
"run-with-safety.yaml": RunConfigSettings(
provider_overrides={
"inference": [
inference_provider,
embedding_provider,
Provider(
provider_id="hf-endpoint-safety",
provider_type="remote::hf::endpoint",
config=InferenceEndpointImplConfig.sample_run_config(
endpoint_name="${env.SAFETY_INFERENCE_ENDPOINT_NAME}",
),
),
],
"vector_io": [vector_io_provider],
},
default_models=[
inference_model,
safety_model,
embedding_model,
],
default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}")],
default_tool_groups=default_tool_groups,
),
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"8321",
"Port for the Llama Stack distribution server",
),
"HF_API_TOKEN": (
"hf_...",
"Hugging Face API token",
),
"INFERENCE_ENDPOINT_NAME": (
"",
"HF Inference endpoint name for the main inference model",
),
"SAFETY_INFERENCE_ENDPOINT_NAME": (
"",
"HF Inference endpoint for the safety model",
),
"INFERENCE_MODEL": (
"meta-llama/Llama-3.2-3B-Instruct",
"Inference model served by the HF Inference Endpoint",
),
"SAFETY_MODEL": (
"meta-llama/Llama-Guard-3-1B",
"Safety model served by the HF Inference Endpoint",
),
},
)

142
llama_stack/templates/hf-endpoint/run-with-safety.yaml
View file

@ -1,142 +0,0 @@
version: 2
image_name: hf-endpoint
apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: hf-endpoint
provider_type: remote::hf::endpoint
config:
endpoint_name: ${env.INFERENCE_ENDPOINT_NAME}
api_token: ${env.HF_API_TOKEN}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
- provider_id: hf-endpoint-safety
provider_type: remote::hf::endpoint
config:
endpoint_name: ${env.SAFETY_INFERENCE_ENDPOINT_NAME}
api_token: ${env.HF_API_TOKEN}
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/trace_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
- provider_id: model-context-protocol
provider_type: remote::model-context-protocol
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/inference_store.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: hf-endpoint
model_type: llm
- metadata: {}
model_id: ${env.SAFETY_MODEL}
provider_id: hf-endpoint-safety
model_type: llm
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: sentence-transformers
model_type: embedding
shields:
- shield_id: ${env.SAFETY_MODEL}
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321

132
llama_stack/templates/hf-endpoint/run.yaml
View file

@ -1,132 +0,0 @@
version: 2
image_name: hf-endpoint
apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: hf-endpoint
provider_type: remote::hf::endpoint
config:
endpoint_name: ${env.INFERENCE_ENDPOINT_NAME}
api_token: ${env.HF_API_TOKEN}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/trace_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
- provider_id: model-context-protocol
provider_type: remote::model-context-protocol
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-endpoint}/inference_store.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: hf-endpoint
model_type: llm
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: sentence-transformers
model_type: embedding
shields: []
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321

7
llama_stack/templates/hf-serverless/__init__.py
View file

@ -1,7 +0,0 @@
# 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.
from .hf_serverless import get_distribution_template # noqa: F401

35
llama_stack/templates/hf-serverless/build.yaml
View file

@ -1,35 +0,0 @@
version: 2
distribution_spec:
description: Use (an external) Hugging Face Inference Endpoint for running LLM inference
providers:
inference:
- remote::hf::serverless
- inline::sentence-transformers
vector_io:
- inline::faiss
- remote::chromadb
- remote::pgvector
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
eval:
- inline::meta-reference
datasetio:
- remote::huggingface
- inline::localfs
scoring:
- inline::basic
- inline::llm-as-judge
- inline::braintrust
tool_runtime:
- remote::brave-search
- remote::tavily-search
- inline::rag-runtime
- remote::model-context-protocol
image_type: conda
additional_pip_packages:
- aiosqlite
- sqlalchemy[asyncio]

142
llama_stack/templates/hf-serverless/hf_serverless.py
View file

@ -1,142 +0,0 @@
# 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.
from llama_stack.apis.models import ModelType
from llama_stack.distribution.datatypes import (
ModelInput,
Provider,
ShieldInput,
ToolGroupInput,
)
from llama_stack.providers.inline.inference.sentence_transformers import (
SentenceTransformersInferenceConfig,
)
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.remote.inference.tgi import InferenceAPIImplConfig
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::hf::serverless", "inline::sentence-transformers"],
"vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
"eval": ["inline::meta-reference"],
"datasetio": ["remote::huggingface", "inline::localfs"],
"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
"tool_runtime": [
"remote::brave-search",
"remote::tavily-search",
"inline::rag-runtime",
"remote::model-context-protocol",
],
}
name = "hf-serverless"
inference_provider = Provider(
provider_id="hf-serverless",
provider_type="remote::hf::serverless",
config=InferenceAPIImplConfig.sample_run_config(),
)
embedding_provider = Provider(
provider_id="sentence-transformers",
provider_type="inline::sentence-transformers",
config=SentenceTransformersInferenceConfig.sample_run_config(),
)
vector_io_provider = Provider(
provider_id="faiss",
provider_type="inline::faiss",
config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
)
inference_model = ModelInput(
model_id="${env.INFERENCE_MODEL}",
provider_id="hf-serverless",
)
safety_model = ModelInput(
model_id="${env.SAFETY_MODEL}",
provider_id="hf-serverless-safety",
)
embedding_model = ModelInput(
model_id="all-MiniLM-L6-v2",
provider_id="sentence-transformers",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 384,
},
)
default_tool_groups = [
ToolGroupInput(
toolgroup_id="builtin::websearch",
provider_id="tavily-search",
),
ToolGroupInput(
toolgroup_id="builtin::rag",
provider_id="rag-runtime",
),
]
return DistributionTemplate(
name=name,
distro_type="self_hosted",
description="Use (an external) Hugging Face Inference Endpoint for running LLM inference",
container_image=None,
template_path=None,
providers=providers,
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider, embedding_provider],
"vector_io": [vector_io_provider],
},
default_models=[inference_model, embedding_model],
default_tool_groups=default_tool_groups,
),
"run-with-safety.yaml": RunConfigSettings(
provider_overrides={
"inference": [
inference_provider,
embedding_provider,
Provider(
provider_id="hf-serverless-safety",
provider_type="remote::hf::serverless",
config=InferenceAPIImplConfig.sample_run_config(
repo="${env.SAFETY_MODEL}",
),
),
],
"vector_io": [vector_io_provider],
},
default_models=[
inference_model,
safety_model,
embedding_model,
],
default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}")],
default_tool_groups=default_tool_groups,
),
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"8321",
"Port for the Llama Stack distribution server",
),
"HF_API_TOKEN": (
"hf_...",
"Hugging Face API token",
),
"INFERENCE_MODEL": (
"meta-llama/Llama-3.2-3B-Instruct",
"Inference model to be served by the HF Serverless endpoint",
),
"SAFETY_MODEL": (
"meta-llama/Llama-Guard-3-1B",
"Safety model to be served by the HF Serverless endpoint",
),
},
)

142
llama_stack/templates/hf-serverless/run-with-safety.yaml
View file

@ -1,142 +0,0 @@
version: 2
image_name: hf-serverless
apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: hf-serverless
provider_type: remote::hf::serverless
config:
huggingface_repo: ${env.INFERENCE_MODEL}
api_token: ${env.HF_API_TOKEN}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
- provider_id: hf-serverless-safety
provider_type: remote::hf::serverless
config:
huggingface_repo: ${env.SAFETY_MODEL}
api_token: ${env.HF_API_TOKEN}
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/trace_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
- provider_id: model-context-protocol
provider_type: remote::model-context-protocol
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/inference_store.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: hf-serverless
model_type: llm
- metadata: {}
model_id: ${env.SAFETY_MODEL}
provider_id: hf-serverless-safety
model_type: llm
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: sentence-transformers
model_type: embedding
shields:
- shield_id: ${env.SAFETY_MODEL}
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321

132
llama_stack/templates/hf-serverless/run.yaml
View file

@ -1,132 +0,0 @@
version: 2
image_name: hf-serverless
apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: hf-serverless
provider_type: remote::hf::serverless
config:
huggingface_repo: ${env.INFERENCE_MODEL}
api_token: ${env.HF_API_TOKEN}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/trace_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
- provider_id: model-context-protocol
provider_type: remote::model-context-protocol
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/hf-serverless}/inference_store.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: hf-serverless
model_type: llm
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: sentence-transformers
model_type: embedding
shields: []
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321

7
llama_stack/templates/llama_api/__init__.py
View file

@ -1,7 +0,0 @@
# 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.
from .llama_api import get_distribution_template # noqa: F401

35
llama_stack/templates/llama_api/build.yaml
View file

@ -1,35 +0,0 @@
version: 2
distribution_spec:
description: Distribution for running e2e tests in CI
providers:
inference:
- remote::llama-openai-compat
- inline::sentence-transformers
vector_io:
- inline::sqlite-vec
- remote::chromadb
- remote::pgvector
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
eval:
- inline::meta-reference
datasetio:
- remote::huggingface
- inline::localfs
scoring:
- inline::basic
- inline::llm-as-judge
- inline::braintrust
tool_runtime:
- remote::brave-search
- remote::tavily-search
- inline::rag-runtime
- remote::model-context-protocol
image_type: conda
additional_pip_packages:
- aiosqlite
- sqlalchemy[asyncio]

153
llama_stack/templates/llama_api/llama_api.py
View file

@ -1,153 +0,0 @@
# 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.
from llama_stack.apis.models import ModelType
from llama_stack.distribution.datatypes import (
ModelInput,
Provider,
ShieldInput,
ToolGroupInput,
)
from llama_stack.providers.inline.inference.sentence_transformers import (
SentenceTransformersInferenceConfig,
)
from llama_stack.providers.inline.vector_io.sqlite_vec.config import (
SQLiteVectorIOConfig,
)
from llama_stack.providers.remote.inference.llama_openai_compat.config import (
LlamaCompatConfig,
)
from llama_stack.providers.remote.inference.llama_openai_compat.models import (
MODEL_ENTRIES as LLLAMA_MODEL_ENTRIES,
)
from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
from llama_stack.providers.remote.vector_io.pgvector.config import (
PGVectorVectorIOConfig,
)
from llama_stack.templates.template import (
DistributionTemplate,
RunConfigSettings,
get_model_registry,
)
def get_inference_providers() -> tuple[list[Provider], list[ModelInput]]:
# in this template, we allow each API key to be optional
providers = [
(
"llama-openai-compat",
LLLAMA_MODEL_ENTRIES,
LlamaCompatConfig.sample_run_config(api_key="${env.LLAMA_API_KEY:+}"),
),
]
inference_providers = []
available_models = {}
for provider_id, model_entries, config in providers:
inference_providers.append(
Provider(
provider_id=provider_id,
provider_type=f"remote::{provider_id}",
config=config,
)
)
available_models[provider_id] = model_entries
return inference_providers, available_models
def get_distribution_template() -> DistributionTemplate:
inference_providers, available_models = get_inference_providers()
providers = {
"inference": ([p.provider_type for p in inference_providers] + ["inline::sentence-transformers"]),
"vector_io": ["inline::sqlite-vec", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
"eval": ["inline::meta-reference"],
"datasetio": ["remote::huggingface", "inline::localfs"],
"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
"tool_runtime": [
"remote::brave-search",
"remote::tavily-search",
"inline::rag-runtime",
"remote::model-context-protocol",
],
}
name = "llama_api"
vector_io_providers = [
Provider(
provider_id="sqlite-vec",
provider_type="inline::sqlite-vec",
config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
),
Provider(
provider_id="${env.ENABLE_CHROMADB:+chromadb}",
provider_type="remote::chromadb",
config=ChromaVectorIOConfig.sample_run_config(url="${env.CHROMADB_URL:+}"),
),
Provider(
provider_id="${env.ENABLE_PGVECTOR:+pgvector}",
provider_type="remote::pgvector",
config=PGVectorVectorIOConfig.sample_run_config(
db="${env.PGVECTOR_DB:+}",
user="${env.PGVECTOR_USER:+}",
password="${env.PGVECTOR_PASSWORD:+}",
),
),
]
embedding_provider = Provider(
provider_id="sentence-transformers",
provider_type="inline::sentence-transformers",
config=SentenceTransformersInferenceConfig.sample_run_config(),
)
default_tool_groups = [
ToolGroupInput(
toolgroup_id="builtin::websearch",
provider_id="tavily-search",
),
ToolGroupInput(
toolgroup_id="builtin::rag",
provider_id="rag-runtime",
),
]
embedding_model = ModelInput(
model_id="all-MiniLM-L6-v2",
provider_id=embedding_provider.provider_id,
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 384,
},
)
default_models = get_model_registry(available_models)
return DistributionTemplate(
name=name,
distro_type="self_hosted",
description="Distribution for running e2e tests in CI",
container_image=None,
template_path=None,
providers=providers,
available_models_by_provider=available_models,
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": inference_providers + [embedding_provider],
"vector_io": vector_io_providers,
},
default_models=default_models + [embedding_model],
default_tool_groups=default_tool_groups,
default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
),
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"8321",
"Port for the Llama Stack distribution server",
),
},
)

168
llama_stack/templates/llama_api/run.yaml
View file

@ -1,168 +0,0 @@
version: 2
image_name: llama_api
apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: llama-openai-compat
provider_type: remote::llama-openai-compat
config:
openai_compat_api_base: https://api.llama.com/compat/v1/
api_key: ${env.LLAMA_API_KEY:+}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
vector_io:
- provider_id: sqlite-vec
provider_type: inline::sqlite-vec
config:
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/llama_api}/sqlite_vec.db
- provider_id: ${env.ENABLE_CHROMADB:+chromadb}
provider_type: remote::chromadb
config:
url: ${env.CHROMADB_URL:+}
- provider_id: ${env.ENABLE_PGVECTOR:+pgvector}
provider_type: remote::pgvector
config:
host: ${env.PGVECTOR_HOST:=localhost}
port: ${env.PGVECTOR_PORT:=5432}
db: ${env.PGVECTOR_DB:+}
user: ${env.PGVECTOR_USER:+}
password: ${env.PGVECTOR_PASSWORD:+}
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/llama_api}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/llama_api}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/llama_api}/trace_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/llama_api}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/llama_api}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/llama_api}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
- provider_id: model-context-protocol
provider_type: remote::model-context-protocol
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/llama_api}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/llama_api}/inference_store.db
models:
- metadata: {}
model_id: Llama-3.3-70B-Instruct
provider_id: llama-openai-compat
provider_model_id: Llama-3.3-70B-Instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.3-70B-Instruct
provider_id: llama-openai-compat
provider_model_id: Llama-3.3-70B-Instruct
model_type: llm
- metadata: {}
model_id: Llama-4-Scout-17B-16E-Instruct-FP8
provider_id: llama-openai-compat
provider_model_id: Llama-4-Scout-17B-16E-Instruct-FP8
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-4-Scout-17B-16E-Instruct
provider_id: llama-openai-compat
provider_model_id: Llama-4-Scout-17B-16E-Instruct-FP8
model_type: llm
- metadata: {}
model_id: Llama-4-Maverick-17B-128E-Instruct-FP8
provider_id: llama-openai-compat
provider_model_id: Llama-4-Maverick-17B-128E-Instruct-FP8
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-4-Maverick-17B-128E-Instruct
provider_id: llama-openai-compat
provider_model_id: Llama-4-Maverick-17B-128E-Instruct-FP8
model_type: llm
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: sentence-transformers
model_type: embedding
shields:
- shield_id: meta-llama/Llama-Guard-3-8B
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321

7
llama_stack/templates/nvidia/__init__.py
View file

@ -1,7 +0,0 @@
# 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.
from .nvidia import get_distribution_template # noqa: F401

29
llama_stack/templates/nvidia/build.yaml
View file

@ -1,29 +0,0 @@
version: 2
distribution_spec:
description: Use NVIDIA NIM for running LLM inference, evaluation and safety
providers:
inference:
- remote::nvidia
vector_io:
- inline::faiss
safety:
- remote::nvidia
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
eval:
- remote::nvidia
post_training:
- remote::nvidia
datasetio:
- inline::localfs
- remote::nvidia
scoring:
- inline::basic
tool_runtime:
- inline::rag-runtime
image_type: conda
additional_pip_packages:
- aiosqlite
- sqlalchemy[asyncio]

149
llama_stack/templates/nvidia/doc_template.md
View file

@ -1,149 +0,0 @@
# NVIDIA Distribution
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
{{ providers_table }}
{% if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
{% endfor %}
{% endif %}
{% if default_models %}
### Models
The following models are available by default:
{% for model in default_models %}
- `{{ model.model_id }} {{ model.doc_string }}`
{% endfor %}
{% endif %}
## Prerequisites
### NVIDIA API Keys
Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/). Use this key for the `NVIDIA_API_KEY` environment variable.
### Deploy NeMo Microservices Platform
The NVIDIA NeMo microservices platform supports end-to-end microservice deployment of a complete AI flywheel on your Kubernetes cluster through the NeMo Microservices Helm Chart. Please reference the [NVIDIA NeMo Microservices documentation](https://docs.nvidia.com/nemo/microservices/latest/about/index.html) for platform prerequisites and instructions to install and deploy the platform.
## Supported Services
Each Llama Stack API corresponds to a specific NeMo microservice. The core microservices (Customizer, Evaluator, Guardrails) are exposed by the same endpoint. The platform components (Data Store) are each exposed by separate endpoints.
### Inference: NVIDIA NIM
NVIDIA NIM is used for running inference with registered models. There are two ways to access NVIDIA NIMs:
1. Hosted (default): Preview APIs hosted at https://integrate.api.nvidia.com (Requires an API key)
2. Self-hosted: NVIDIA NIMs that run on your own infrastructure.
The deployed platform includes the NIM Proxy microservice, which is the service that provides to access your NIMs (for example, to run inference on a model). Set the `NVIDIA_BASE_URL` environment variable to use your NVIDIA NIM Proxy deployment.
### Datasetio API: NeMo Data Store
The NeMo Data Store microservice serves as the default file storage solution for the NeMo microservices platform. It exposts APIs compatible with the Hugging Face Hub client (`HfApi`), so you can use the client to interact with Data Store. The `NVIDIA_DATASETS_URL` environment variable should point to your NeMo Data Store endpoint.
See the {repopath}`NVIDIA Datasetio docs::llama_stack/providers/remote/datasetio/nvidia/README.md` for supported features and example usage.
### Eval API: NeMo Evaluator
The NeMo Evaluator microservice supports evaluation of LLMs. Launching an Evaluation job with NeMo Evaluator requires an Evaluation Config (an object that contains metadata needed by the job). A Llama Stack Benchmark maps to an Evaluation Config, so registering a Benchmark creates an Evaluation Config in NeMo Evaluator. The `NVIDIA_EVALUATOR_URL` environment variable should point to your NeMo Microservices endpoint.
See the {repopath}`NVIDIA Eval docs::llama_stack/providers/remote/eval/nvidia/README.md` for supported features and example usage.
### Post-Training API: NeMo Customizer
The NeMo Customizer microservice supports fine-tuning models. You can reference {repopath}`this list of supported models::llama_stack/providers/remote/post_training/nvidia/models.py` that can be fine-tuned using Llama Stack. The `NVIDIA_CUSTOMIZER_URL` environment variable should point to your NeMo Microservices endpoint.
See the {repopath}`NVIDIA Post-Training docs::llama_stack/providers/remote/post_training/nvidia/README.md` for supported features and example usage.
### Safety API: NeMo Guardrails
The NeMo Guardrails microservice sits between your application and the LLM, and adds checks and content moderation to a model. The `GUARDRAILS_SERVICE_URL` environment variable should point to your NeMo Microservices endpoint.
See the {repopath}`NVIDIA Safety docs::llama_stack/providers/remote/safety/nvidia/README.md` for supported features and example usage.
## Deploying models
In order to use a registered model with the Llama Stack APIs, ensure the corresponding NIM is deployed to your environment. For example, you can use the NIM Proxy microservice to deploy `meta/llama-3.2-1b-instruct`.
Note: For improved inference speeds, we need to use NIM with `fast_outlines` guided decoding system (specified in the request body). This is the default if you deployed the platform with the NeMo Microservices Helm Chart.
```sh
# URL to NeMo NIM Proxy service
export NEMO_URL="http://nemo.test"
curl --location "$NEMO_URL/v1/deployment/model-deployments" \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"name": "llama-3.2-1b-instruct",
"namespace": "meta",
"config": {
"model": "meta/llama-3.2-1b-instruct",
"nim_deployment": {
"image_name": "nvcr.io/nim/meta/llama-3.2-1b-instruct",
"image_tag": "1.8.3",
"pvc_size": "25Gi",
"gpu": 1,
"additional_envs": {
"NIM_GUIDED_DECODING_BACKEND": "fast_outlines"
}
}
}
}'
```
This NIM deployment should take approximately 10 minutes to go live. [See the docs](https://docs.nvidia.com/nemo/microservices/latest/get-started/tutorials/deploy-nims.html) for more information on how to deploy a NIM and verify it's available for inference.
You can also remove a deployed NIM to free up GPU resources, if needed.
```sh
export NEMO_URL="http://nemo.test"
curl -X DELETE "$NEMO_URL/v1/deployment/model-deployments/meta/llama-3.1-8b-instruct"
```
## Running Llama Stack with NVIDIA
You can do this via Conda or venv (build code), or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-{{ name }} \
--config /root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env NVIDIA_API_KEY=$NVIDIA_API_KEY
```
### Via Conda
```bash
INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct
llama stack build --template nvidia --image-type conda
llama stack run ./run.yaml \
--port 8321 \
--env NVIDIA_API_KEY=$NVIDIA_API_KEY \
--env INFERENCE_MODEL=$INFERENCE_MODEL
```
### Via venv
If you've set up your local development environment, you can also build the image using your local virtual environment.
```bash
INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct
llama stack build --template nvidia --image-type venv
llama stack run ./run.yaml \
--port 8321 \
--env NVIDIA_API_KEY=$NVIDIA_API_KEY \
--env INFERENCE_MODEL=$INFERENCE_MODEL
```
## Example Notebooks
For examples of how to use the NVIDIA Distribution to run inference, fine-tune, evaluate, and run safety checks on your LLMs, you can reference the example notebooks in {repopath}`docs/notebooks/nvidia`.

150
llama_stack/templates/nvidia/nvidia.py
View file

@ -1,150 +0,0 @@
# 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.
from pathlib import Path
from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput, ToolGroupInput
from llama_stack.providers.remote.datasetio.nvidia import NvidiaDatasetIOConfig
from llama_stack.providers.remote.eval.nvidia import NVIDIAEvalConfig
from llama_stack.providers.remote.inference.nvidia import NVIDIAConfig
from llama_stack.providers.remote.inference.nvidia.models import MODEL_ENTRIES
from llama_stack.providers.remote.safety.nvidia import NVIDIASafetyConfig
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings, get_model_registry
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::nvidia"],
"vector_io": ["inline::faiss"],
"safety": ["remote::nvidia"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
"eval": ["remote::nvidia"],
"post_training": ["remote::nvidia"],
"datasetio": ["inline::localfs", "remote::nvidia"],
"scoring": ["inline::basic"],
"tool_runtime": ["inline::rag-runtime"],
}
inference_provider = Provider(
provider_id="nvidia",
provider_type="remote::nvidia",
config=NVIDIAConfig.sample_run_config(),
)
safety_provider = Provider(
provider_id="nvidia",
provider_type="remote::nvidia",
config=NVIDIASafetyConfig.sample_run_config(),
)
datasetio_provider = Provider(
provider_id="nvidia",
provider_type="remote::nvidia",
config=NvidiaDatasetIOConfig.sample_run_config(),
)
eval_provider = Provider(
provider_id="nvidia",
provider_type="remote::nvidia",
config=NVIDIAEvalConfig.sample_run_config(),
)
inference_model = ModelInput(
model_id="${env.INFERENCE_MODEL}",
provider_id="nvidia",
)
safety_model = ModelInput(
model_id="${env.SAFETY_MODEL}",
provider_id="nvidia",
)
available_models = {
"nvidia": MODEL_ENTRIES,
}
default_tool_groups = [
ToolGroupInput(
toolgroup_id="builtin::rag",
provider_id="rag-runtime",
),
]
default_models = get_model_registry(available_models)
return DistributionTemplate(
name="nvidia",
distro_type="self_hosted",
description="Use NVIDIA NIM for running LLM inference, evaluation and safety",
container_image=None,
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
available_models_by_provider=available_models,
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider],
"datasetio": [datasetio_provider],
"eval": [eval_provider],
},
default_models=default_models,
default_tool_groups=default_tool_groups,
),
"run-with-safety.yaml": RunConfigSettings(
provider_overrides={
"inference": [
inference_provider,
safety_provider,
],
"eval": [eval_provider],
},
default_models=[inference_model, safety_model],
default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}", provider_id="nvidia")],
default_tool_groups=default_tool_groups,
),
},
run_config_env_vars={
"NVIDIA_API_KEY": (
"",
"NVIDIA API Key",
),
"NVIDIA_APPEND_API_VERSION": (
"True",
"Whether to append the API version to the base_url",
),
## Nemo Customizer related variables
"NVIDIA_DATASET_NAMESPACE": (
"default",
"NVIDIA Dataset Namespace",
),
"NVIDIA_PROJECT_ID": (
"test-project",
"NVIDIA Project ID",
),
"NVIDIA_CUSTOMIZER_URL": (
"https://customizer.api.nvidia.com",
"NVIDIA Customizer URL",
),
"NVIDIA_OUTPUT_MODEL_DIR": (
"test-example-model@v1",
"NVIDIA Output Model Directory",
),
"GUARDRAILS_SERVICE_URL": (
"http://0.0.0.0:7331",
"URL for the NeMo Guardrails Service",
),
"NVIDIA_GUARDRAILS_CONFIG_ID": (
"self-check",
"NVIDIA Guardrail Configuration ID",
),
"NVIDIA_EVALUATOR_URL": (
"http://0.0.0.0:7331",
"URL for the NeMo Evaluator Service",
),
"INFERENCE_MODEL": (
"Llama3.1-8B-Instruct",
"Inference model",
),
"SAFETY_MODEL": (
"meta/llama-3.1-8b-instruct",
"Name of the model to use for safety",
),
},
)

121
llama_stack/templates/nvidia/run-with-safety.yaml
View file

@ -1,121 +0,0 @@
version: 2
image_name: nvidia
apis:
- agents
- datasetio
- eval
- inference
- post_training
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: nvidia
provider_type: remote::nvidia
config:
url: ${env.NVIDIA_BASE_URL:=https://integrate.api.nvidia.com}
api_key: ${env.NVIDIA_API_KEY:+}
append_api_version: ${env.NVIDIA_APPEND_API_VERSION:=True}
- provider_id: nvidia
provider_type: remote::nvidia
config:
guardrails_service_url: ${env.GUARDRAILS_SERVICE_URL:=http://localhost:7331}
config_id: ${env.NVIDIA_GUARDRAILS_CONFIG_ID:=self-check}
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/faiss_store.db
safety:
- provider_id: nvidia
provider_type: remote::nvidia
config:
guardrails_service_url: ${env.GUARDRAILS_SERVICE_URL:=http://localhost:7331}
config_id: ${env.NVIDIA_GUARDRAILS_CONFIG_ID:=self-check}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/trace_store.db
eval:
- provider_id: nvidia
provider_type: remote::nvidia
config:
evaluator_url: ${env.NVIDIA_EVALUATOR_URL:=http://localhost:7331}
post_training:
- provider_id: nvidia
provider_type: remote::nvidia
config:
api_key: ${env.NVIDIA_API_KEY:+}
dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:=default}
project_id: ${env.NVIDIA_PROJECT_ID:=test-project}
customizer_url: ${env.NVIDIA_CUSTOMIZER_URL:=http://nemo.test}
datasetio:
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/localfs_datasetio.db
- provider_id: nvidia
provider_type: remote::nvidia
config:
api_key: ${env.NVIDIA_API_KEY:+}
dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:=default}
project_id: ${env.NVIDIA_PROJECT_ID:=test-project}
datasets_url: ${env.NVIDIA_DATASETS_URL:=http://nemo.test}
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
tool_runtime:
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/inference_store.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: nvidia
model_type: llm
- metadata: {}
model_id: ${env.SAFETY_MODEL}
provider_id: nvidia
model_type: llm
shields:
- shield_id: ${env.SAFETY_MODEL}
provider_id: nvidia
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321

227
llama_stack/templates/nvidia/run.yaml
View file

@ -1,227 +0,0 @@
version: 2
image_name: nvidia
apis:
- agents
- datasetio
- eval
- inference
- post_training
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: nvidia
provider_type: remote::nvidia
config:
url: ${env.NVIDIA_BASE_URL:=https://integrate.api.nvidia.com}
api_key: ${env.NVIDIA_API_KEY:+}
append_api_version: ${env.NVIDIA_APPEND_API_VERSION:=True}
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/faiss_store.db
safety:
- provider_id: nvidia
provider_type: remote::nvidia
config:
guardrails_service_url: ${env.GUARDRAILS_SERVICE_URL:=http://localhost:7331}
config_id: ${env.NVIDIA_GUARDRAILS_CONFIG_ID:=self-check}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/trace_store.db
eval:
- provider_id: nvidia
provider_type: remote::nvidia
config:
evaluator_url: ${env.NVIDIA_EVALUATOR_URL:=http://localhost:7331}
post_training:
- provider_id: nvidia
provider_type: remote::nvidia
config:
api_key: ${env.NVIDIA_API_KEY:+}
dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:=default}
project_id: ${env.NVIDIA_PROJECT_ID:=test-project}
customizer_url: ${env.NVIDIA_CUSTOMIZER_URL:=http://nemo.test}
datasetio:
- provider_id: nvidia
provider_type: remote::nvidia
config:
api_key: ${env.NVIDIA_API_KEY:+}
dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:=default}
project_id: ${env.NVIDIA_PROJECT_ID:=test-project}
datasets_url: ${env.NVIDIA_DATASETS_URL:=http://nemo.test}
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
tool_runtime:
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/inference_store.db
models:
- metadata: {}
model_id: meta/llama3-8b-instruct
provider_id: nvidia
provider_model_id: meta/llama3-8b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3-8B-Instruct
provider_id: nvidia
provider_model_id: meta/llama3-8b-instruct
model_type: llm
- metadata: {}
model_id: meta/llama3-70b-instruct
provider_id: nvidia
provider_model_id: meta/llama3-70b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3-70B-Instruct
provider_id: nvidia
provider_model_id: meta/llama3-70b-instruct
model_type: llm
- metadata: {}
model_id: meta/llama-3.1-8b-instruct
provider_id: nvidia
provider_model_id: meta/llama-3.1-8b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-8B-Instruct
provider_id: nvidia
provider_model_id: meta/llama-3.1-8b-instruct
model_type: llm
- metadata: {}
model_id: meta/llama-3.1-70b-instruct
provider_id: nvidia
provider_model_id: meta/llama-3.1-70b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-70B-Instruct
provider_id: nvidia
provider_model_id: meta/llama-3.1-70b-instruct
model_type: llm
- metadata: {}
model_id: meta/llama-3.1-405b-instruct
provider_id: nvidia
provider_model_id: meta/llama-3.1-405b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-405B-Instruct-FP8
provider_id: nvidia
provider_model_id: meta/llama-3.1-405b-instruct
model_type: llm
- metadata: {}
model_id: meta/llama-3.2-1b-instruct
provider_id: nvidia
provider_model_id: meta/llama-3.2-1b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-1B-Instruct
provider_id: nvidia
provider_model_id: meta/llama-3.2-1b-instruct
model_type: llm
- metadata: {}
model_id: meta/llama-3.2-3b-instruct
provider_id: nvidia
provider_model_id: meta/llama-3.2-3b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-3B-Instruct
provider_id: nvidia
provider_model_id: meta/llama-3.2-3b-instruct
model_type: llm
- metadata: {}
model_id: meta/llama-3.2-11b-vision-instruct
provider_id: nvidia
provider_model_id: meta/llama-3.2-11b-vision-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-11B-Vision-Instruct
provider_id: nvidia
provider_model_id: meta/llama-3.2-11b-vision-instruct
model_type: llm
- metadata: {}
model_id: meta/llama-3.2-90b-vision-instruct
provider_id: nvidia
provider_model_id: meta/llama-3.2-90b-vision-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-90B-Vision-Instruct
provider_id: nvidia
provider_model_id: meta/llama-3.2-90b-vision-instruct
model_type: llm
- metadata: {}
model_id: meta/llama-3.3-70b-instruct
provider_id: nvidia
provider_model_id: meta/llama-3.3-70b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.3-70B-Instruct
provider_id: nvidia
provider_model_id: meta/llama-3.3-70b-instruct
model_type: llm
- metadata:
embedding_dimension: 2048
context_length: 8192
model_id: nvidia/llama-3.2-nv-embedqa-1b-v2
provider_id: nvidia
provider_model_id: nvidia/llama-3.2-nv-embedqa-1b-v2
model_type: embedding
- metadata:
embedding_dimension: 1024
context_length: 512
model_id: nvidia/nv-embedqa-e5-v5
provider_id: nvidia
provider_model_id: nvidia/nv-embedqa-e5-v5
model_type: embedding
- metadata:
embedding_dimension: 4096
context_length: 512
model_id: nvidia/nv-embedqa-mistral-7b-v2
provider_id: nvidia
provider_model_id: nvidia/nv-embedqa-mistral-7b-v2
model_type: embedding
- metadata:
embedding_dimension: 1024
context_length: 512
model_id: snowflake/arctic-embed-l
provider_id: nvidia
provider_model_id: snowflake/arctic-embed-l
model_type: embedding
shields: []
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321

7
llama_stack/templates/ollama/__init__.py
View file

@ -1,7 +0,0 @@
# 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.
from .ollama import get_distribution_template # noqa: F401

39
llama_stack/templates/ollama/build.yaml
View file

@ -1,39 +0,0 @@
version: 2
distribution_spec:
description: Use (an external) Ollama server for running LLM inference
providers:
inference:
- remote::ollama
vector_io:
- inline::faiss
- remote::chromadb
- remote::pgvector
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
eval:
- inline::meta-reference
datasetio:
- remote::huggingface
- inline::localfs
scoring:
- inline::basic
- inline::llm-as-judge
- inline::braintrust
files:
- inline::localfs
post_training:
- inline::huggingface
tool_runtime:
- remote::brave-search
- remote::tavily-search
- inline::rag-runtime
- remote::model-context-protocol
- remote::wolfram-alpha
image_type: conda
additional_pip_packages:
- aiosqlite
- sqlalchemy[asyncio]

152
llama_stack/templates/ollama/doc_template.md
View file

@ -1,152 +0,0 @@
---
orphan: true
---
# Ollama Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
{{ providers_table }}
You should use this distribution if you have a regular desktop machine without very powerful GPUs. Of course, if you have powerful GPUs, you can still continue using this distribution since Ollama supports GPU acceleration.
{% if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
{% endfor %}
{% endif %}
## Setting up Ollama server
Please check the [Ollama Documentation](https://github.com/ollama/ollama) on how to install and run Ollama. After installing Ollama, you need to run `ollama serve` to start the server.
In order to load models, you can run:
```bash
export INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct"
# ollama names this model differently, and we must use the ollama name when loading the model
export OLLAMA_INFERENCE_MODEL="llama3.2:3b-instruct-fp16"
ollama run $OLLAMA_INFERENCE_MODEL --keepalive 60m
```
If you are using Llama Stack Safety / Shield APIs, you will also need to pull and run the safety model.
```bash
export SAFETY_MODEL="meta-llama/Llama-Guard-3-1B"
# ollama names this model differently, and we must use the ollama name when loading the model
export OLLAMA_SAFETY_MODEL="llama-guard3:1b"
ollama run $OLLAMA_SAFETY_MODEL --keepalive 60m
```
## Running Llama Stack
Now you are ready to run Llama Stack with Ollama as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
export LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
llamastack/distribution-{{ name }} \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env OLLAMA_URL=http://host.docker.internal:11434
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
# You need a local checkout of llama-stack to run this, get it using
# git clone https://github.com/meta-llama/llama-stack.git
cd /path/to/llama-stack
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
-v ./llama_stack/templates/ollama/run-with-safety.yaml:/root/my-run.yaml \
llamastack/distribution-{{ name }} \
--config /root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env OLLAMA_URL=http://host.docker.internal:11434
```
### Via Conda
Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
```bash
export LLAMA_STACK_PORT=8321
llama stack build --template {{ name }} --image-type conda
llama stack run ./run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env OLLAMA_URL=http://localhost:11434
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
llama stack run ./run-with-safety.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env OLLAMA_URL=http://localhost:11434
```
### (Optional) Update Model Serving Configuration
```{note}
Please check the [model_entries](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/ollama/models.py) for the supported Ollama models.
```
To serve a new model with `ollama`
```bash
ollama run <model_name>
```
To make sure that the model is being served correctly, run `ollama ps` to get a list of models being served by ollama.
```
$ ollama ps
NAME ID SIZE PROCESSOR UNTIL
llama3.2:3b-instruct-fp16 195a8c01d91e 8.6 GB 100% GPU 9 minutes from now
```
To verify that the model served by ollama is correctly connected to Llama Stack server
```bash
$ llama-stack-client models list
Available Models
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ model_type ┃ identifier ┃ provider_resource_id ┃ metadata ┃ provider_id ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ llm │ meta-llama/Llama-3.2-3B-Instruct │ llama3.2:3b-instruct-fp16 │ │ ollama │
└──────────────┴──────────────────────────────────────┴──────────────────────────────┴───────────┴─────────────┘
Total models: 1
```

169
llama_stack/templates/ollama/ollama.py
View file

@ -1,169 +0,0 @@
# 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.
from pathlib import Path
from llama_stack.apis.models import ModelType
from llama_stack.distribution.datatypes import (
ModelInput,
Provider,
ShieldInput,
ToolGroupInput,
)
from llama_stack.providers.inline.files.localfs.config import LocalfsFilesImplConfig
from llama_stack.providers.inline.post_training.huggingface import HuggingFacePostTrainingConfig
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.remote.inference.ollama import OllamaImplConfig
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::ollama"],
"vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
"eval": ["inline::meta-reference"],
"datasetio": ["remote::huggingface", "inline::localfs"],
"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
"files": ["inline::localfs"],
"post_training": ["inline::huggingface"],
"tool_runtime": [
"remote::brave-search",
"remote::tavily-search",
"inline::rag-runtime",
"remote::model-context-protocol",
"remote::wolfram-alpha",
],
}
name = "ollama"
inference_provider = Provider(
provider_id="ollama",
provider_type="remote::ollama",
config=OllamaImplConfig.sample_run_config(),
)
vector_io_provider_faiss = Provider(
provider_id="faiss",
provider_type="inline::faiss",
config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
)
files_provider = Provider(
provider_id="meta-reference-files",
provider_type="inline::localfs",
config=LocalfsFilesImplConfig.sample_run_config(f"~/.llama/distributions/{name}"),
)
posttraining_provider = Provider(
provider_id="huggingface",
provider_type="inline::huggingface",
config=HuggingFacePostTrainingConfig.sample_run_config(f"~/.llama/distributions/{name}"),
)
inference_model = ModelInput(
model_id="${env.INFERENCE_MODEL}",
provider_id="ollama",
)
safety_model = ModelInput(
model_id="${env.SAFETY_MODEL}",
provider_id="ollama",
)
embedding_model = ModelInput(
model_id="all-MiniLM-L6-v2",
provider_id="ollama",
provider_model_id="all-minilm:latest",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 384,
},
)
default_tool_groups = [
ToolGroupInput(
toolgroup_id="builtin::websearch",
provider_id="tavily-search",
),
ToolGroupInput(
toolgroup_id="builtin::rag",
provider_id="rag-runtime",
),
ToolGroupInput(
toolgroup_id="builtin::wolfram_alpha",
provider_id="wolfram-alpha",
),
]
return DistributionTemplate(
name=name,
distro_type="self_hosted",
description="Use (an external) Ollama server for running LLM inference",
container_image=None,
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider],
"vector_io": [vector_io_provider_faiss],
"files": [files_provider],
"post_training": [posttraining_provider],
},
default_models=[inference_model, embedding_model],
default_tool_groups=default_tool_groups,
),
"run-with-safety.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider],
"vector_io": [vector_io_provider_faiss],
"files": [files_provider],
"post_training": [posttraining_provider],
"safety": [
Provider(
provider_id="llama-guard",
provider_type="inline::llama-guard",
config={},
),
Provider(
provider_id="code-scanner",
provider_type="inline::code-scanner",
config={},
),
],
},
default_models=[
inference_model,
safety_model,
embedding_model,
],
default_shields=[
ShieldInput(
shield_id="${env.SAFETY_MODEL}",
provider_id="llama-guard",
),
ShieldInput(
shield_id="CodeScanner",
provider_id="code-scanner",
),
],
default_tool_groups=default_tool_groups,
),
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"8321",
"Port for the Llama Stack distribution server",
),
"OLLAMA_URL": (
"http://127.0.0.1:11434",
"URL of the Ollama server",
),
"INFERENCE_MODEL": (
"meta-llama/Llama-3.2-3B-Instruct",
"Inference model loaded into the Ollama server",
),
"SAFETY_MODEL": (
"meta-llama/Llama-Guard-3-1B",
"Safety model loaded into the Ollama server",
),
},
)

163
llama_stack/templates/ollama/run-with-safety.yaml
View file

@ -1,163 +0,0 @@
version: 2
image_name: ollama
apis:
- agents
- datasetio
- eval
- files
- inference
- post_training
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: ollama
provider_type: remote::ollama
config:
url: ${env.OLLAMA_URL:=http://localhost:11434}
raise_on_connect_error: true
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
- provider_id: code-scanner
provider_type: inline::code-scanner
config: {}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/trace_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
files:
- provider_id: meta-reference-files
provider_type: inline::localfs
config:
storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/ollama/files}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/files_metadata.db
post_training:
- provider_id: huggingface
provider_type: inline::huggingface
config:
checkpoint_format: huggingface
distributed_backend: null
device: cpu
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
- provider_id: model-context-protocol
provider_type: remote::model-context-protocol
config: {}
- provider_id: wolfram-alpha
provider_type: remote::wolfram-alpha
config:
api_key: ${env.WOLFRAM_ALPHA_API_KEY:+}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/inference_store.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: ollama
model_type: llm
- metadata: {}
model_id: ${env.SAFETY_MODEL}
provider_id: ollama
model_type: llm
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: ollama
provider_model_id: all-minilm:latest
model_type: embedding
shields:
- shield_id: ${env.SAFETY_MODEL}
provider_id: llama-guard
- shield_id: CodeScanner
provider_id: code-scanner
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::rag
provider_id: rag-runtime
- toolgroup_id: builtin::wolfram_alpha
provider_id: wolfram-alpha
server:
port: 8321

153
llama_stack/templates/ollama/run.yaml
View file

@ -1,153 +0,0 @@
version: 2
image_name: ollama
apis:
- agents
- datasetio
- eval
- files
- inference
- post_training
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: ollama
provider_type: remote::ollama
config:
url: ${env.OLLAMA_URL:=http://localhost:11434}
raise_on_connect_error: true
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/trace_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
files:
- provider_id: meta-reference-files
provider_type: inline::localfs
config:
storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/ollama/files}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/files_metadata.db
post_training:
- provider_id: huggingface
provider_type: inline::huggingface
config:
checkpoint_format: huggingface
distributed_backend: null
device: cpu
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
- provider_id: model-context-protocol
provider_type: remote::model-context-protocol
config: {}
- provider_id: wolfram-alpha
provider_type: remote::wolfram-alpha
config:
api_key: ${env.WOLFRAM_ALPHA_API_KEY:+}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/inference_store.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: ollama
model_type: llm
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: ollama
provider_model_id: all-minilm:latest
model_type: embedding
shields: []
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::rag
provider_id: rag-runtime
- toolgroup_id: builtin::wolfram_alpha
provider_id: wolfram-alpha
server:
port: 8321

7
llama_stack/templates/passthrough/__init__.py
View file

@ -1,7 +0,0 @@
# 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.
from .passthrough import get_distribution_template # noqa: F401

36
llama_stack/templates/passthrough/build.yaml
View file

@ -1,36 +0,0 @@
version: 2
distribution_spec:
description: Use Passthrough hosted llama-stack endpoint for LLM inference
providers:
inference:
- remote::passthrough
- inline::sentence-transformers
vector_io:
- inline::faiss
- remote::chromadb
- remote::pgvector
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
eval:
- inline::meta-reference
datasetio:
- remote::huggingface
- inline::localfs
scoring:
- inline::basic
- inline::llm-as-judge
- inline::braintrust
tool_runtime:
- remote::brave-search
- remote::tavily-search
- remote::wolfram-alpha
- inline::rag-runtime
- remote::model-context-protocol
image_type: conda
additional_pip_packages:
- aiosqlite
- sqlalchemy[asyncio]

35
llama_stack/templates/passthrough/doc_template.md
View file

@ -1,35 +0,0 @@
---
orphan: true
---
# Passthrough Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
{{ providers_table }}
{% if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
{% endfor %}
{% endif %}
{% if default_models %}
### Models
The following models are available by default:
{% for model in default_models %}
- `{{ model.model_id }} {{ model.doc_string }}`
{% endfor %}
{% endif %}

193
llama_stack/templates/passthrough/passthrough.py
View file

@ -1,193 +0,0 @@
# 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.
from pathlib import Path
from llama_stack.apis.models import ModelType
from llama_stack.distribution.datatypes import (
ModelInput,
Provider,
ShieldInput,
ToolGroupInput,
)
from llama_stack.providers.inline.inference.sentence_transformers import (
SentenceTransformersInferenceConfig,
)
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.remote.inference.passthrough.config import (
PassthroughImplConfig,
)
from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::passthrough", "inline::sentence-transformers"],
"vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
"eval": ["inline::meta-reference"],
"datasetio": ["remote::huggingface", "inline::localfs"],
"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
"tool_runtime": [
"remote::brave-search",
"remote::tavily-search",
"remote::wolfram-alpha",
"inline::rag-runtime",
"remote::model-context-protocol",
],
}
name = "passthrough"
inference_provider = Provider(
provider_id="passthrough",
provider_type="remote::passthrough",
config=PassthroughImplConfig.sample_run_config(),
)
embedding_provider = Provider(
provider_id="sentence-transformers",
provider_type="inline::sentence-transformers",
config=SentenceTransformersInferenceConfig.sample_run_config(),
)
vector_io_provider = Provider(
provider_id="faiss",
provider_type="inline::faiss",
config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
)
default_models = [
ModelInput(
metadata={},
model_id="meta-llama/Llama-3.1-8B-Instruct",
provider_id="passthrough",
provider_model_id="llama3.1-8b-instruct",
model_type=ModelType.llm,
),
ModelInput(
metadata={},
model_id="meta-llama/Llama-3.2-11B-Vision-Instruct",
provider_id="passthrough",
provider_model_id="llama3.2-11b-vision-instruct",
model_type=ModelType.llm,
),
]
embedding_model = ModelInput(
model_id="all-MiniLM-L6-v2",
provider_id="sentence-transformers",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 384,
},
)
default_tool_groups = [
ToolGroupInput(
toolgroup_id="builtin::websearch",
provider_id="tavily-search",
),
ToolGroupInput(
toolgroup_id="builtin::wolfram_alpha",
provider_id="wolfram-alpha",
),
ToolGroupInput(
toolgroup_id="builtin::rag",
provider_id="rag-runtime",
),
]
return DistributionTemplate(
name=name,
distro_type="self_hosted",
description="Use Passthrough hosted llama-stack endpoint for LLM inference",
container_image=None,
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
available_models_by_provider={
"passthrough": [
ProviderModelEntry(
provider_model_id="llama3.1-8b-instruct",
model_type=ModelType.llm,
),
ProviderModelEntry(
provider_model_id="llama3.2-11b-vision-instruct",
model_type=ModelType.llm,
),
],
},
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider, embedding_provider],
"vector_io": [vector_io_provider],
},
default_models=default_models + [embedding_model],
default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
default_tool_groups=default_tool_groups,
),
"run-with-safety.yaml": RunConfigSettings(
provider_overrides={
"inference": [
inference_provider,
embedding_provider,
],
"vector_io": [vector_io_provider],
"safety": [
Provider(
provider_id="llama-guard",
provider_type="inline::llama-guard",
config={},
),
Provider(
provider_id="llama-guard-vision",
provider_type="inline::llama-guard",
config={},
),
Provider(
provider_id="code-scanner",
provider_type="inline::code-scanner",
config={},
),
],
},
default_models=[
*default_models,
embedding_model,
],
default_shields=[
ShieldInput(
shield_id="meta-llama/Llama-Guard-3-8B",
provider_id="llama-guard",
),
ShieldInput(
shield_id="meta-llama/Llama-Guard-3-11B-Vision",
provider_id="llama-guard-vision",
),
ShieldInput(
shield_id="CodeScanner",
provider_id="code-scanner",
),
],
default_tool_groups=default_tool_groups,
),
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"8321",
"Port for the Llama Stack distribution server",
),
"PASSTHROUGH_API_KEY": (
"",
"Passthrough API Key",
),
"PASSTHROUGH_URL": (
"",
"Passthrough URL",
),
},
)

155
llama_stack/templates/passthrough/run-with-safety.yaml
View file

@ -1,155 +0,0 @@
version: 2
image_name: passthrough
apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: passthrough
provider_type: remote::passthrough
config:
url: ${env.PASSTHROUGH_URL}
api_key: ${env.PASSTHROUGH_API_KEY}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
- provider_id: llama-guard-vision
provider_type: inline::llama-guard
config: {}
- provider_id: code-scanner
provider_type: inline::code-scanner
config: {}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/trace_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: wolfram-alpha
provider_type: remote::wolfram-alpha
config:
api_key: ${env.WOLFRAM_ALPHA_API_KEY:+}
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
- provider_id: model-context-protocol
provider_type: remote::model-context-protocol
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/inference_store.db
models:
- metadata: {}
model_id: meta-llama/Llama-3.1-8B-Instruct
provider_id: passthrough
provider_model_id: llama3.1-8b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-11B-Vision-Instruct
provider_id: passthrough
provider_model_id: llama3.2-11b-vision-instruct
model_type: llm
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: sentence-transformers
model_type: embedding
shields:
- shield_id: meta-llama/Llama-Guard-3-8B
provider_id: llama-guard
- shield_id: meta-llama/Llama-Guard-3-11B-Vision
provider_id: llama-guard-vision
- shield_id: CodeScanner
provider_id: code-scanner
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::wolfram_alpha
provider_id: wolfram-alpha
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321

145
llama_stack/templates/passthrough/run.yaml
View file

@ -1,145 +0,0 @@
version: 2
image_name: passthrough
apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: passthrough
provider_type: remote::passthrough
config:
url: ${env.PASSTHROUGH_URL}
api_key: ${env.PASSTHROUGH_API_KEY}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/responses_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/trace_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: wolfram-alpha
provider_type: remote::wolfram-alpha
config:
api_key: ${env.WOLFRAM_ALPHA_API_KEY:+}
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
- provider_id: model-context-protocol
provider_type: remote::model-context-protocol
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/passthrough}/inference_store.db
models:
- metadata: {}
model_id: meta-llama/Llama-3.1-8B-Instruct
provider_id: passthrough
provider_model_id: llama3.1-8b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-11B-Vision-Instruct
provider_id: passthrough
provider_model_id: llama3.2-11b-vision-instruct
model_type: llm
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: sentence-transformers
model_type: embedding
shields:
- shield_id: meta-llama/Llama-Guard-3-8B
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::wolfram_alpha
provider_id: wolfram-alpha
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321

7
llama_stack/templates/remote-vllm/__init__.py
View file

@ -1,7 +0,0 @@
# 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.
from .vllm import get_distribution_template # noqa: F401

36
llama_stack/templates/remote-vllm/build.yaml
View file

@ -1,36 +0,0 @@
version: 2
distribution_spec:
description: Use (an external) vLLM server for running LLM inference
providers:
inference:
- remote::vllm
- inline::sentence-transformers
vector_io:
- inline::faiss
- remote::chromadb
- remote::pgvector
safety:
- inline::llama-guard
agents:
- inline::meta-reference
eval:
- inline::meta-reference
datasetio:
- remote::huggingface
- inline::localfs
scoring:
- inline::basic
- inline::llm-as-judge
- inline::braintrust
telemetry:
- inline::meta-reference
tool_runtime:
- remote::brave-search
- remote::tavily-search
- inline::rag-runtime
- remote::model-context-protocol
- remote::wolfram-alpha
image_type: conda
additional_pip_packages:
- aiosqlite
- sqlalchemy[asyncio]

284
llama_stack/templates/remote-vllm/doc_template.md
View file

@ -1,284 +0,0 @@
---
orphan: true
---
# Remote vLLM Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations:
{{ providers_table }}
You can use this distribution if you want to run an independent vLLM server for inference.
{% if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
{% endfor %}
{% endif %}
## Setting up vLLM server
In the following sections, we'll use AMD, NVIDIA or Intel GPUs to serve as hardware accelerators for the vLLM
server, which acts as both the LLM inference provider and the safety provider. Note that vLLM also
[supports many other hardware accelerators](https://docs.vllm.ai/en/latest/getting_started/installation.html) and
that we only use GPUs here for demonstration purposes. Note that if you run into issues, you can include the environment variable `--env VLLM_DEBUG_LOG_API_SERVER_RESPONSE=true` (available in vLLM v0.8.3 and above) in the `docker run` command to enable log response from API server for debugging.
### Setting up vLLM server on AMD GPU
AMD provides two main vLLM container options:
- rocm/vllm: Production-ready container
- rocm/vllm-dev: Development container with the latest vLLM features
Please check the [Blog about ROCm vLLM Usage](https://rocm.blogs.amd.com/software-tools-optimization/vllm-container/README.html) to get more details.
Here is a sample script to start a ROCm vLLM server locally via Docker:
```bash
export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export CUDA_VISIBLE_DEVICES=0
export VLLM_DIMG="rocm/vllm-dev:main"
docker run \
--pull always \
--ipc=host \
--privileged \
--shm-size 16g \
--device=/dev/kfd \
--device=/dev/dri \
--group-add video \
--cap-add=SYS_PTRACE \
--cap-add=CAP_SYS_ADMIN \
--security-opt seccomp=unconfined \
--security-opt apparmor=unconfined \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
--env "HIP_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" \
-p $INFERENCE_PORT:$INFERENCE_PORT \
-v ~/.cache/huggingface:/root/.cache/huggingface \
$VLLM_DIMG \
python -m vllm.entrypoints.openai.api_server \
--model $INFERENCE_MODEL \
--port $INFERENCE_PORT
```
Note that you'll also need to set `--enable-auto-tool-choice` and `--tool-call-parser` to [enable tool calling in vLLM](https://docs.vllm.ai/en/latest/features/tool_calling.html).
If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a vLLM with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
```bash
export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
export CUDA_VISIBLE_DEVICES=1
export VLLM_DIMG="rocm/vllm-dev:main"
docker run \
--pull always \
--ipc=host \
--privileged \
--shm-size 16g \
--device=/dev/kfd \
--device=/dev/dri \
--group-add video \
--cap-add=SYS_PTRACE \
--cap-add=CAP_SYS_ADMIN \
--security-opt seccomp=unconfined \
--security-opt apparmor=unconfined \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
--env "HIP_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" \
-p $SAFETY_PORT:$SAFETY_PORT \
-v ~/.cache/huggingface:/root/.cache/huggingface \
$VLLM_DIMG \
python -m vllm.entrypoints.openai.api_server \
--model $SAFETY_MODEL \
--port $SAFETY_PORT
```
### Setting up vLLM server on NVIDIA GPU
Please check the [vLLM Documentation](https://docs.vllm.ai/en/v0.5.5/serving/deploying_with_docker.html) to get a vLLM endpoint. Here is a sample script to start a vLLM server locally via Docker:
```bash
export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export CUDA_VISIBLE_DEVICES=0
docker run \
--pull always \
--runtime nvidia \
--gpus $CUDA_VISIBLE_DEVICES \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
-p $INFERENCE_PORT:$INFERENCE_PORT \
--ipc=host \
vllm/vllm-openai:latest \
--gpu-memory-utilization 0.7 \
--model $INFERENCE_MODEL \
--port $INFERENCE_PORT
```
Note that you'll also need to set `--enable-auto-tool-choice` and `--tool-call-parser` to [enable tool calling in vLLM](https://docs.vllm.ai/en/latest/features/tool_calling.html).
If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a vLLM with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
```bash
export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
export CUDA_VISIBLE_DEVICES=1
docker run \
--pull always \
--runtime nvidia \
--gpus $CUDA_VISIBLE_DEVICES \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
-p $SAFETY_PORT:$SAFETY_PORT \
--ipc=host \
vllm/vllm-openai:latest \
--gpu-memory-utilization 0.7 \
--model $SAFETY_MODEL \
--port $SAFETY_PORT
```
### Setting up vLLM server on Intel GPU
Refer to [vLLM Documentation for XPU](https://docs.vllm.ai/en/v0.8.2/getting_started/installation/gpu.html?device=xpu) to get a vLLM endpoint. In addition to vLLM side setup which guides towards installing vLLM from sources orself-building vLLM Docker container, Intel provides prebuilt vLLM container to use on systems with Intel GPUs supported by PyTorch XPU backend:
- [intel/vllm](https://hub.docker.com/r/intel/vllm)
Here is a sample script to start a vLLM server locally via Docker using Intel provided container:
```bash
export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-1B-Instruct
export ZE_AFFINITY_MASK=0
docker run \
--pull always \
--device /dev/dri \
-v /dev/dri/by-path:/dev/dri/by-path \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
--env ZE_AFFINITY_MASK=$ZE_AFFINITY_MASK \
-p $INFERENCE_PORT:$INFERENCE_PORT \
--ipc=host \
intel/vllm:xpu \
--gpu-memory-utilization 0.7 \
--model $INFERENCE_MODEL \
--port $INFERENCE_PORT
```
If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a vLLM with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
```bash
export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
export ZE_AFFINITY_MASK=1
docker run \
--pull always \
--device /dev/dri \
-v /dev/dri/by-path:/dev/dri/by-path \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
--env ZE_AFFINITY_MASK=$ZE_AFFINITY_MASK \
-p $SAFETY_PORT:$SAFETY_PORT \
--ipc=host \
intel/vllm:xpu \
--gpu-memory-utilization 0.7 \
--model $SAFETY_MODEL \
--port $SAFETY_PORT
```
## Running Llama Stack
Now you are ready to run Llama Stack with vLLM as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export LLAMA_STACK_PORT=8321
# You need a local checkout of llama-stack to run this, get it using
# git clone https://github.com/meta-llama/llama-stack.git
cd /path/to/llama-stack
docker run \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./llama_stack/templates/remote-vllm/run.yaml:/root/my-run.yaml \
llamastack/distribution-{{ name }} \
--config /root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT/v1
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
# You need a local checkout of llama-stack to run this, get it using
# git clone https://github.com/meta-llama/llama-stack.git
cd /path/to/llama-stack
docker run \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
-v ./llama_stack/templates/remote-vllm/run-with-safety.yaml:/root/my-run.yaml \
llamastack/distribution-{{ name }} \
--config /root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT/v1 \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env SAFETY_VLLM_URL=http://host.docker.internal:$SAFETY_PORT/v1
```
### Via Conda
Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
```bash
export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export LLAMA_STACK_PORT=8321
cd distributions/remote-vllm
llama stack build --template remote-vllm --image-type conda
llama stack run ./run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_URL=http://localhost:$INFERENCE_PORT/v1
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
llama stack run ./run-with-safety.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_URL=http://localhost:$INFERENCE_PORT/v1 \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env SAFETY_VLLM_URL=http://localhost:$SAFETY_PORT/v1
```

152
llama_stack/templates/remote-vllm/run-with-safety.yaml
View file

@ -1,152 +0,0 @@
version: 2
image_name: remote-vllm
apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: vllm-inference
provider_type: remote::vllm
config:
url: ${env.VLLM_URL:=http://localhost:8000/v1}
max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
api_token: ${env.VLLM_API_TOKEN:=fake}
tls_verify: ${env.VLLM_TLS_VERIFY:=true}
- provider_id: vllm-safety
provider_type: remote::vllm
config:
url: ${env.SAFETY_VLLM_URL}
max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
api_token: ${env.VLLM_API_TOKEN:=fake}
tls_verify: ${env.VLLM_TLS_VERIFY:=true}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/responses_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/trace_store.db
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
- provider_id: model-context-protocol
provider_type: remote::model-context-protocol
config: {}
- provider_id: wolfram-alpha
provider_type: remote::wolfram-alpha
config:
api_key: ${env.WOLFRAM_ALPHA_API_KEY:+}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/inference_store.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: vllm-inference
model_type: llm
- metadata: {}
model_id: ${env.SAFETY_MODEL}
provider_id: vllm-safety
model_type: llm
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: sentence-transformers
model_type: embedding
shields:
- shield_id: ${env.SAFETY_MODEL}
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::rag
provider_id: rag-runtime
- toolgroup_id: builtin::wolfram_alpha
provider_id: wolfram-alpha
server:
port: 8321

140
llama_stack/templates/remote-vllm/run.yaml
View file

@ -1,140 +0,0 @@
version: 2
image_name: remote-vllm
apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: vllm-inference
provider_type: remote::vllm
config:
url: ${env.VLLM_URL:=http://localhost:8000/v1}
max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
api_token: ${env.VLLM_API_TOKEN:=fake}
tls_verify: ${env.VLLM_TLS_VERIFY:=true}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/agents_store.db
responses_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/responses_store.db
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/meta_reference_eval.db
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/huggingface_datasetio.db
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/localfs_datasetio.db
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config:
openai_api_key: ${env.OPENAI_API_KEY:+}
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/trace_store.db
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search
config:
api_key: ${env.BRAVE_SEARCH_API_KEY:+}
max_results: 3
- provider_id: tavily-search
provider_type: remote::tavily-search
config:
api_key: ${env.TAVILY_SEARCH_API_KEY:+}
max_results: 3
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
- provider_id: model-context-protocol
provider_type: remote::model-context-protocol
config: {}
- provider_id: wolfram-alpha
provider_type: remote::wolfram-alpha
config:
api_key: ${env.WOLFRAM_ALPHA_API_KEY:+}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/registry.db
inference_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/inference_store.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: vllm-inference
model_type: llm
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: sentence-transformers
model_type: embedding
shields: []
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::websearch
provider_id: tavily-search
- toolgroup_id: builtin::rag
provider_id: rag-runtime
- toolgroup_id: builtin::wolfram_alpha
provider_id: wolfram-alpha
server:
port: 8321

Some files were not shown because too many files have changed in this diff Show more