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docs/source/getting_started/distributions/self_hosted_distro/ollama.md
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The `llamastack/distribution-ollama` distribution consists of the following provider configurations.
Provider Configuration
┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ 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` │
└───────────┴─────────────────────────────────────────────────────────┘
| 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`)
- `INFERENCE_MODEL`: Inference model loaded into the TGI server (default: `meta-llama/Llama-3.2-3B-Instruct`)
- `OLLAMA_URL`: URL of the Ollama server (default: `http://host.docker.internal:11434`)
- `SAFETY_MODEL`: Name of the safety (Llama-Guard) model to use (default: `meta-llama/Llama-Guard-3-1B`)
### Models
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.### Models
The following models are configured by default:
- `${env.INFERENCE_MODEL}`

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docs/source/getting_started/distributions/self_hosted_distro/remote-vllm.md
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The `llamastack/distribution-remote-vllm` distribution consists of the following provider configurations:
Provider Configuration
┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ 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` │
└───────────┴─────────────────────────────────────────────────────────┘
| 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`)
You can use this distribution if you have GPUs and want to run an independent vLLM server container for running inference.
### Models
The following models are configured by default:

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docs/source/getting_started/distributions/self_hosted_distro/tgi.md
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The `llamastack/distribution-tgi` distribution consists of the following provider configurations.
Provider Configuration
┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ 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` │
└───────────┴─────────────────────────────────────────────────────────┘
| 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
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://host.docker.internal:8080}/v1`)
- `SAFETY_TGI_URL`: URL of the TGI server with the safety model (default: `http://host.docker.internal:8081/v1`)
- `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`)
### Models
The following models are configured by default:
- `${env.INFERENCE_MODEL}`
- `${env.SAFETY_MODEL}`
## Using Docker Compose
## Setting up TGI server
You can use `docker compose` to start a TGI container and Llama Stack server container together.
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
$ cd distributions/tgi; docker compose up
export TGI_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 $TGI_INFERENCE_PORT:$TGI_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 $TGI_INFERENCE_PORT
```
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 --
```bash
[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://[::]:5001 (Press CTRL+C to quit)
```
To kill the server
```bash
docker compose down
```
### Conda: TGI server + llama stack run
If you wish to separately spin up a TGI server, and connect with Llama Stack, you may use the following commands.
#### Start TGI server locally
- 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.
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
docker run --rm -it -v $HOME/.cache/huggingface:/data \
-p 5009:5009 --gpus all \
ghcr.io/huggingface/text-generation-inference:latest \
--dtype bfloat16 --usage-stats on --sharded false \
--model-id meta-llama/Llama-3.2-3B-Instruct --port 5009
export TGI_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 $TGI_SAFETY_PORT:$TGI_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 $TGI_SAFETY_PORT
```
#### Start Llama Stack server pointing to TGI server
## Running Llama Stack with TGI as the inference provider
**Via Conda**
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 \
--network host \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-tgi \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env TGI_URL=http://host.docker.internal:$TGI_INFERENCE_PORT
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
docker run \
--network host \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run-with-safety.yaml:/root/my-run.yaml \
llamastack/distribution-tgi \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env TGI_URL=http://host.docker.internal:$TGI_INFERENCE_PORT \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env TGI_SAFETY_URL=http://host.docker.internal:$TGI_SAFETY_PORT
```
### Via Conda
Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
```bash
llama stack build --template tgi --image-type conda
# -- start a TGI server endpoint
llama stack run ./gpu/run.yaml
llama stack run ./run.yaml
--port 5001
--env INFERENCE_MODEL=$INFERENCE_MODEL
--env TGI_URL=http://127.0.0.1:$TGI_INFERENCE_PORT
```
**Via Docker**
If you are using Llama Stack Safety / Shield APIs, use:
```bash
docker run --network host -it -p 5001:5001 \
-v ./run.yaml:/root/my-run.yaml --gpus=all \
llamastack/distribution-tgi \
--yaml_config /root/my-run.yaml
```
We have provided a template `run.yaml` file in the `distributions/tgi` directory. Make sure in your `run.yaml` file, you inference provider is pointing to the correct TGI server endpoint. E.g.
```yaml
inference:
- provider_id: tgi0
provider_type: remote::tgi
config:
url: http://127.0.0.1:5009
```
### (Optional) Update Model Serving Configuration
To serve a new model with `tgi`, change the docker command flag `--model-id <model-to-serve>`.
This can be done by edit the `command` args in `compose.yaml`. E.g. Replace "Llama-3.2-1B-Instruct" with the model you want to serve.
```yaml
command: >
--dtype bfloat16 --usage-stats on --sharded false
--model-id meta-llama/Llama-3.2-1B-Instruct
--port 5009 --cuda-memory-fraction 0.7
```
or by changing the docker run command's `--model-id` flag
```bash
docker run --rm -it -v $HOME/.cache/huggingface:/data \
-p 5009:5009 --gpus all \
ghcr.io/huggingface/text-generation-inference:latest \
--dtype bfloat16 --usage-stats off --sharded false \
--model-id meta-llama/Llama-3.2-3B-Instruct --port 5009
```
In `run.yaml`, make sure you point the correct server endpoint to the TGI server endpoint serving your model.
```yaml
inference:
- provider_id: tgi0
provider_type: remote::tgi
config:
url: http://127.0.0.1:5009
llama stack run ./run-with-safety.yaml
--port 5001
--env INFERENCE_MODEL=$INFERENCE_MODEL
--env TGI_URL=http://127.0.0.1:$TGI_INFERENCE_PORT
--env SAFETY_MODEL=$SAFETY_MODEL
--env TGI_SAFETY_URL=http://127.0.0.1:$TGI_SAFETY_PORT
```