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# Bedrock Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
### Connect to a Llama Stack Bedrock Endpoint
- You may connect to Amazon Bedrock APIs for running LLM inference
The `llamastack/distribution-bedrock` distribution consists of the following provider configurations.
| **API** | **Inference** | **Agents** | **Memory** | **Safety** | **Telemetry** |
|----------------- |--------------- |---------------- |---------------- |---------------- |---------------- |
| **Provider(s)** | remote::bedrock | meta-reference | meta-reference | remote::bedrock | meta-reference |
### Docker: Start the Distribution (Single Node CPU)
> [!NOTE]
> This assumes you have valid AWS credentials configured with access to Amazon Bedrock.
```
$ cd distributions/bedrock && docker compose up
```
Make sure in your `run.yaml` file, your inference provider is pointing to the correct AWS configuration. E.g.
```
inference:
- provider_id: bedrock0
provider_type: remote::bedrock
config:
aws_access_key_id: <AWS_ACCESS_KEY_ID>
aws_secret_access_key: <AWS_SECRET_ACCESS_KEY>
aws_session_token: <AWS_SESSION_TOKEN>
region_name: <AWS_REGION>
```
### Conda llama stack run (Single Node CPU)
```bash
llama stack build --template bedrock --image-type conda
# -- modify run.yaml with valid AWS credentials
llama stack run ./run.yaml
```
### (Optional) Update Model Serving Configuration
Use `llama-stack-client models list` to check the available models served by Amazon Bedrock.
```
$ llama-stack-client models list
+------------------------------+------------------------------+---------------+------------+
| identifier | llama_model | provider_id | metadata |
+==============================+==============================+===============+============+
| Llama3.1-8B-Instruct | meta.llama3-1-8b-instruct-v1:0 | bedrock0 | {} |
+------------------------------+------------------------------+---------------+------------+
| Llama3.1-70B-Instruct | meta.llama3-1-70b-instruct-v1:0 | bedrock0 | {} |
+------------------------------+------------------------------+---------------+------------+
| Llama3.1-405B-Instruct | meta.llama3-1-405b-instruct-v1:0 | bedrock0 | {} |
+------------------------------+------------------------------+---------------+------------+
```

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# Dell-TGI Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-tgi` distribution consists of the following provider configurations.
| **API** | **Inference** | **Agents** | **Memory** | **Safety** | **Telemetry** |
|----------------- |--------------- |---------------- |-------------------------------------------------- |---------------- |---------------- |
| **Provider(s)** | remote::tgi | meta-reference | meta-reference, remote::pgvector, remote::chroma | meta-reference | meta-reference |
The only difference vs. the `tgi` distribution is that it runs the Dell-TGI server for inference.
### Start the Distribution (Single Node GPU)
> [!NOTE]
> This assumes you have access to GPU to start a TGI server with access to your GPU.
```
$ cd distributions/dell-tgi/
$ ls
compose.yaml README.md run.yaml
$ docker compose up
```
The script will first start up TGI server, then start up Llama Stack distribution server hooking up to the remote TGI provider for inference. You should be able to see the following outputs --
```
[text-generation-inference] | 2024-10-15T18:56:33.810397Z INFO text_generation_router::server: router/src/server.rs:1813: Using config Some(Llama)
[text-generation-inference] | 2024-10-15T18:56:33.810448Z WARN text_generation_router::server: router/src/server.rs:1960: Invalid hostname, defaulting to 0.0.0.0
[text-generation-inference] | 2024-10-15T18:56:33.864143Z INFO text_generation_router::server: router/src/server.rs:2353: Connected
INFO: Started server process [1]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://[::]:5000 (Press CTRL+C to quit)
```
To kill the server
```
docker compose down
```
### (Alternative) Dell-TGI server + llama stack run (Single Node GPU)
#### Start Dell-TGI server locally
```
docker run -it --shm-size 1g -p 80:80 --gpus 4 \
-e NUM_SHARD=4
-e MAX_BATCH_PREFILL_TOKENS=32768 \
-e MAX_INPUT_TOKENS=8000 \
-e MAX_TOTAL_TOKENS=8192 \
registry.dell.huggingface.co/enterprise-dell-inference-meta-llama-meta-llama-3.1-8b-instruct
```
#### Start Llama Stack server pointing to TGI server
```
docker run --network host -it -p 5000:5000 -v ./run.yaml:/root/my-run.yaml --gpus=all llamastack/distribution-tgi --yaml_config /root/my-run.yaml
```
Make sure in you `run.yaml` file, you inference provider is pointing to the correct TGI server endpoint. E.g.
```
inference:
- provider_id: tgi0
provider_type: remote::tgi
config:
url: http://127.0.0.1:5009
```

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# 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` |
| inference | `remote::fireworks` |
| memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
| safety | `inline::llama-guard` |
| telemetry | `inline::meta-reference` |
### Environment Variables
The following environment variables can be configured:
- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `FIREWORKS_API_KEY`: Fireworks.AI API Key (default: ``)
### Models
The following models are available by default:
- `meta-llama/Llama-3.1-8B-Instruct (fireworks/llama-v3p1-8b-instruct)`
- `meta-llama/Llama-3.1-70B-Instruct (fireworks/llama-v3p1-70b-instruct)`
- `meta-llama/Llama-3.1-405B-Instruct-FP8 (fireworks/llama-v3p1-405b-instruct)`
- `meta-llama/Llama-3.2-1B-Instruct (fireworks/llama-v3p2-1b-instruct)`
- `meta-llama/Llama-3.2-3B-Instruct (fireworks/llama-v3p2-3b-instruct)`
- `meta-llama/Llama-3.2-11B-Vision-Instruct (fireworks/llama-v3p2-11b-vision-instruct)`
- `meta-llama/Llama-3.2-90B-Vision-Instruct (fireworks/llama-v3p2-90b-vision-instruct)`
- `meta-llama/Llama-Guard-3-8B (fireworks/llama-guard-3-8b)`
- `meta-llama/Llama-Guard-3-11B-Vision (fireworks/llama-guard-3-11b-vision)`
### 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=5001
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-fireworks \
--yaml-config /root/my-run.yaml \
--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 5001 \
--env FIREWORKS_API_KEY=$FIREWORKS_API_KEY
```

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# Self-Hosted Distribution
```{toctree}
:maxdepth: 2
:hidden:
meta-reference-gpu
meta-reference-quantized-gpu
ollama
tgi
dell-tgi
together
fireworks
remote-vllm
bedrock
```
We offer deployable distributions where you can host your own Llama Stack server using local inference.
| **Distribution** | **Llama Stack Docker** | Start This Distribution |
|:----------------: |:------------------------------------------: |:-----------------------: |
| Meta Reference | [llamastack/distribution-meta-reference-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/meta-reference-gpu.html) |
| Meta Reference Quantized | [llamastack/distribution-meta-reference-quantized-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-quantized-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/meta-reference-quantized-gpu.html) |
| Ollama | [llamastack/distribution-ollama](https://hub.docker.com/repository/docker/llamastack/distribution-ollama/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/ollama.html) |
| TGI | [llamastack/distribution-tgi](https://hub.docker.com/repository/docker/llamastack/distribution-tgi/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/tgi.html) |
| Together | [llamastack/distribution-together](https://hub.docker.com/repository/docker/llamastack/distribution-together/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/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/getting_started/distributions/self_hosted_distro/fireworks.html) |
| Bedrock | [llamastack/distribution-bedrock](https://hub.docker.com/repository/docker/llamastack/distribution-bedrock/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/bedrock.html) |

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# Meta Reference Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-meta-reference-gpu` distribution consists of the following provider configurations:
| API | Provider(s) |
|-----|-------------|
| agents | `inline::meta-reference` |
| inference | `inline::meta-reference` |
| memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
| safety | `inline::llama-guard` |
| telemetry | `inline::meta-reference` |
Note that you need access to nvidia GPUs to run this distribution. This distribution is not compatible with CPU-only machines or machines with AMD GPUs.
### Environment Variables
The following environment variables can be configured:
- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `INFERENCE_MODEL`: Inference model loaded into the Meta Reference server (default: `meta-llama/Llama-3.2-3B-Instruct`)
- `INFERENCE_CHECKPOINT_DIR`: Directory containing the Meta Reference model checkpoint (default: `null`)
- `SAFETY_MODEL`: Name of the safety (Llama-Guard) model to use (default: `meta-llama/Llama-Guard-3-1B`)
- `SAFETY_CHECKPOINT_DIR`: Directory containing the Llama-Guard model checkpoint (default: `null`)
## Prerequisite: Downloading Models
Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
```
$ ls ~/.llama/checkpoints
Llama3.1-8B Llama3.2-11B-Vision-Instruct Llama3.2-1B-Instruct Llama3.2-90B-Vision-Instruct Llama-Guard-3-8B
Llama3.1-8B-Instruct Llama3.2-1B Llama3.2-3B-Instruct Llama-Guard-3-1B Prompt-Guard-86M
```
## Running the Distribution
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=5001
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-meta-reference-gpu \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run-with-safety.yaml:/root/my-run.yaml \
llamastack/distribution-meta-reference-gpu \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
```
### Via Conda
Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
```bash
llama stack build --template meta-reference-gpu --image-type conda
llama stack run ./run.yaml \
--port 5001 \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
llama stack run ./run-with-safety.yaml \
--port 5001 \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
```

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# Meta Reference Quantized Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-meta-reference-quantized-gpu` distribution consists of the following provider configurations.
| **API** | **Inference** | **Agents** | **Memory** | **Safety** | **Telemetry** |
|----------------- |------------------------ |---------------- |-------------------------------------------------- |---------------- |---------------- |
| **Provider(s)** | meta-reference-quantized | meta-reference | meta-reference, remote::pgvector, remote::chroma | meta-reference | meta-reference |
The only difference vs. the `meta-reference-gpu` distribution is that it has support for more efficient inference -- with fp8, int4 quantization, etc.
### Step 0. Prerequisite - Downloading Models
Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/cli_reference/download_models.html) here to download the models.
```
$ ls ~/.llama/checkpoints
Llama3.2-3B-Instruct:int4-qlora-eo8
```
### Step 1. Start the Distribution
#### (Option 1) Start with Docker
```
$ cd distributions/meta-reference-quantized-gpu && docker compose up
```
> [!NOTE]
> This assumes you have access to GPU to start a local server with access to your GPU.
> [!NOTE]
> `~/.llama` should be the path containing downloaded weights of Llama models.
This will download and start running a pre-built docker container. Alternatively, you may use the following commands:
```
docker run -it -p 5000:5000 -v ~/.llama:/root/.llama -v ./run.yaml:/root/my-run.yaml --gpus=all distribution-meta-reference-quantized-gpu --yaml_config /root/my-run.yaml
```
#### (Option 2) Start with Conda
1. Install the `llama` CLI. See [CLI Reference](https://llama-stack.readthedocs.io/en/latest/cli_reference/index.html)
2. Build the `meta-reference-quantized-gpu` distribution
```
$ llama stack build --template meta-reference-quantized-gpu --image-type conda
```
3. Start running distribution
```
$ cd distributions/meta-reference-quantized-gpu
$ llama stack run ./run.yaml
```

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# 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` |
| inference | `remote::ollama` |
| memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
| safety | `inline::llama-guard` |
| telemetry | `inline::meta-reference` |
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:
- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `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=5001
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-ollama \
--yaml-config /root/my-run.yaml \
--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
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
-v ./run-with-safety.yaml:/root/my-run.yaml \
llamastack/distribution-ollama \
--yaml-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 `pip install llama-stack` and have the Llama Stack CLI available.
```bash
export LLAMA_STACK_PORT=5001
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 [OLLAMA_SUPPORTED_MODELS](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers.remote/inference/ollama/ollama.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
llama3.1:8b-instruct-fp16 4aacac419454 17 GB 100% GPU 4 minutes from now
```
To verify that the model served by ollama is correctly connected to Llama Stack server
```bash
$ llama-stack-client models list
+----------------------+----------------------+---------------+-----------------------------------------------+
| identifier | llama_model | provider_id | metadata |
+======================+======================+===============+===============================================+
| Llama3.1-8B-Instruct | Llama3.1-8B-Instruct | ollama0 | {'ollama_model': 'llama3.1:8b-instruct-fp16'} |
+----------------------+----------------------+---------------+-----------------------------------------------+
```

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# 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` |
| inference | `remote::vllm` |
| memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
| safety | `inline::llama-guard` |
| telemetry | `inline::meta-reference` |
You can use this distribution if you have GPUs and want to run an independent vLLM server container for running inference.
### Environment Variables
The following environment variables can be configured:
- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `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
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 \
--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
```
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 \
--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
```
## 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=5001
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-remote-vllm \
--yaml-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
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run-with-safety.yaml:/root/my-run.yaml \
llamastack/distribution-remote-vllm \
--yaml-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 `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=5001
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
```

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# 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` |
| inference | `remote::tgi` |
| memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
| safety | `inline::llama-guard` |
| telemetry | `inline::meta-reference` |
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:
- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `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 \
-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 \
-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=5001
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-tgi \
--yaml-config /root/my-run.yaml \
--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
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run-with-safety.yaml:/root/my-run.yaml \
llamastack/distribution-tgi \
--yaml-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:$SAFETY_PORT
```
### Via Conda
Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
```bash
llama stack build --template tgi --image-type conda
llama stack run ./run.yaml
--port 5001
--env INFERENCE_MODEL=$INFERENCE_MODEL
--env TGI_URL=http://127.0.0.1:$INFERENCE_PORT
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
llama stack run ./run-with-safety.yaml
--port 5001
--env INFERENCE_MODEL=$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
```

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# 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` |
| inference | `remote::together` |
| memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
| safety | `inline::llama-guard` |
| telemetry | `inline::meta-reference` |
### Environment Variables
The following environment variables can be configured:
- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `TOGETHER_API_KEY`: Together.AI API Key (default: ``)
### Models
The following models are available by default:
- `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-3.2-3B-Instruct`
- `meta-llama/Llama-3.2-11B-Vision-Instruct`
- `meta-llama/Llama-3.2-90B-Vision-Instruct`
- `meta-llama/Llama-Guard-3-8B`
- `meta-llama/Llama-Guard-3-11B-Vision`
### 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=5001
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-together \
--yaml-config /root/my-run.yaml \
--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 5001 \
--env TOGETHER_API_KEY=$TOGETHER_API_KEY
```