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Update Fireworks + Togther documentation

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Ashwin Bharambe 2024-11-18 12:52:23 -08:00
parent 1ecaf2cb3c
commit a562668dcd
27 changed files with 879 additions and 445 deletions
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99
distributions/fireworks/run.yaml
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@ -1,50 +1,91 @@
version: '2'
image_name: local
image_name: fireworks
docker_image: null
conda_env: local
conda_env: null
apis:
- shields
- agents
- models
- memory
- memory_banks
- inference
- memory
- safety
- telemetry
providers:
inference:
- provider_id: fireworks0
- provider_id: fireworks
provider_type: remote::fireworks
config:
url: https://api.fireworks.ai/inference
# api_key: <ENTER_YOUR_API_KEY>
safety:
safety:
- provider_id: meta0
provider_type: inline::llama-guard
config:
model: Llama-Guard-3-1B
excluded_categories: []
- provider_id: meta1
provider_type: inline::prompt-guard
config:
model: Prompt-Guard-86M
api_key: ${env.FIREWORKS_API_KEY}
memory:
- provider_id: meta0
provider_type: inline::meta-reference
- 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: {}
# Uncomment to use weaviate memory provider
# - provider_id: weaviate0
# provider_type: remote::weaviate
# config: {}
agents:
- provider_id: meta0
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
namespace: null
type: sqlite
db_path: ~/.llama/runtime/kvstore.db
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/agents_store.db
telemetry:
- provider_id: meta0
- provider_id: meta-reference
provider_type: inline::meta-reference
config: {}
metadata_store:
namespace: null
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/registry.db
models:
- metadata: {}
model_id: fireworks/llama-v3p1-8b-instruct
provider_id: null
provider_model_id: null
- metadata: {}
model_id: fireworks/llama-v3p1-70b-instruct
provider_id: null
provider_model_id: null
- metadata: {}
model_id: fireworks/llama-v3p1-405b-instruct
provider_id: null
provider_model_id: null
- metadata: {}
model_id: fireworks/llama-v3p2-1b-instruct
provider_id: null
provider_model_id: null
- metadata: {}
model_id: fireworks/llama-v3p2-3b-instruct
provider_id: null
provider_model_id: null
- metadata: {}
model_id: fireworks/llama-v3p2-11b-vision-instruct
provider_id: null
provider_model_id: null
- metadata: {}
model_id: fireworks/llama-v3p2-90b-vision-instruct
provider_id: null
provider_model_id: null
- metadata: {}
model_id: fireworks/llama-guard-3-8b
provider_id: null
provider_model_id: null
- metadata: {}
model_id: fireworks/llama-guard-3-11b-vision
provider_id: null
provider_model_id: null
shields:
- params: null
shield_id: meta-llama/Llama-Guard-3-8B
provider_id: null
provider_shield_id: null
memory_banks: []
datasets: []
scoring_fns: []
eval_tasks: []

2
distributions/remote-vllm/run-with-safety.yaml
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@ -1,6 +1,6 @@
version: '2'
image_name: remote-vllm
docker_image: llamastack/distribution-remote-vllm:test-0.0.52rc3
docker_image: null
conda_env: null
apis:
- agents

2
distributions/remote-vllm/run.yaml
View file

@ -1,6 +1,6 @@
version: '2'
image_name: remote-vllm
docker_image: llamastack/distribution-remote-vllm:test-0.0.52rc3
docker_image: null
conda_env: null
apis:
- agents

90
distributions/together/run.yaml
View file

@ -1,45 +1,87 @@
version: '2'
image_name: local
image_name: together
docker_image: null
conda_env: local
conda_env: null
apis:
- shields
- agents
- models
- memory
- memory_banks
- inference
- memory
- safety
- telemetry
providers:
inference:
- provider_id: together0
- provider_id: together
provider_type: remote::together
config:
url: https://api.together.xyz/v1
# api_key: <ENTER_YOUR_API_KEY>
safety:
- provider_id: meta0
provider_type: inline::llama-guard
config:
model: Llama-Guard-3-1B
excluded_categories: []
- provider_id: meta1
provider_type: inline::prompt-guard
config:
model: Prompt-Guard-86M
api_key: ${env.TOGETHER_API_KEY}
memory:
- provider_id: meta0
provider_type: remote::weaviate
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
agents:
- provider_id: meta0
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
namespace: null
type: sqlite
db_path: ~/.llama/runtime/kvstore.db
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/agents_store.db
telemetry:
- provider_id: meta0
- provider_id: meta-reference
provider_type: inline::meta-reference
config: {}
metadata_store:
namespace: null
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/registry.db
models:
- metadata: {}
model_id: meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo
provider_id: null
provider_model_id: null
- metadata: {}
model_id: meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo
provider_id: null
provider_model_id: null
- metadata: {}
model_id: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo
provider_id: null
provider_model_id: null
- metadata: {}
model_id: meta-llama/Llama-3.2-3B-Instruct-Turbo
provider_id: null
provider_model_id: null
- metadata: {}
model_id: meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo
provider_id: null
provider_model_id: null
- metadata: {}
model_id: meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo
provider_id: null
provider_model_id: null
- metadata: {}
model_id: meta-llama/Meta-Llama-Guard-3-8B
provider_id: null
provider_model_id: null
- metadata: {}
model_id: meta-llama/Llama-Guard-3-11B-Vision-Turbo
provider_id: null
provider_model_id: null
shields:
- params: null
shield_id: meta-llama/Llama-Guard-3-1B
provider_id: null
provider_shield_id: null
memory_banks: []
datasets: []
scoring_fns: []
eval_tasks: []

102
docs/source/getting_started/distributions/self_hosted_distro/fireworks.md
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@ -2,63 +2,67 @@
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` |
| **API** | **Inference** | **Agents** | **Memory** | **Safety** | **Telemetry** |
|----------------- |--------------- |---------------- |-------------------------------------------------- |---------------- |---------------- |
| **Provider(s)** | remote::fireworks | meta-reference | meta-reference | meta-reference | meta-reference |
### Step 0. Prerequisite
- Make sure you have access to a fireworks API Key. You can get one by visiting [fireworks.ai](https://fireworks.ai/)
### Environment Variables
### Step 1. Start the Distribution (Single Node CPU)
The following environment variables can be configured:
#### (Option 1) Start Distribution Via Docker
> [!NOTE]
> This assumes you have an hosted endpoint at Fireworks with API Key.
- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `FIREWORKS_API_KEY`: Fireworks.AI API Key (default: ``)
```
$ cd distributions/fireworks && docker compose up
### Models
The following models are available by default:
- `fireworks/llama-v3p1-8b-instruct`
- `fireworks/llama-v3p1-70b-instruct`
- `fireworks/llama-v3p1-405b-instruct`
- `fireworks/llama-v3p2-1b-instruct`
- `fireworks/llama-v3p2-3b-instruct`
- `fireworks/llama-v3p2-11b-vision-instruct`
- `fireworks/llama-v3p2-90b-vision-instruct`
- `fireworks/llama-guard-3-8b`
- `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 \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env FIREWORKS_API_KEY=$FIREWORKS_API_KEY
```
Make sure in you `run.yaml` file, you inference provider is pointing to the correct Fireworks URL server endpoint. E.g.
```
inference:
- provider_id: fireworks
provider_type: remote::fireworks
config:
url: https://api.fireworks.ai/inference
api_key: <optional api key>
```
#### (Option 2) Start Distribution Via Conda
### Via Conda
```bash
llama stack build --template fireworks --image-type conda
# -- modify run.yaml to a valid Fireworks server endpoint
llama stack run ./run.yaml
```
### (Optional) Model Serving
Use `llama-stack-client models list` to check the available models served by Fireworks.
```
$ llama-stack-client models list
+------------------------------+------------------------------+---------------+------------+
| identifier | llama_model | provider_id | metadata |
+==============================+==============================+===============+============+
| Llama3.1-8B-Instruct | Llama3.1-8B-Instruct | fireworks0 | {} |
+------------------------------+------------------------------+---------------+------------+
| Llama3.1-70B-Instruct | Llama3.1-70B-Instruct | fireworks0 | {} |
+------------------------------+------------------------------+---------------+------------+
| Llama3.1-405B-Instruct | Llama3.1-405B-Instruct | fireworks0 | {} |
+------------------------------+------------------------------+---------------+------------+
| Llama3.2-1B-Instruct | Llama3.2-1B-Instruct | fireworks0 | {} |
+------------------------------+------------------------------+---------------+------------+
| Llama3.2-3B-Instruct | Llama3.2-3B-Instruct | fireworks0 | {} |
+------------------------------+------------------------------+---------------+------------+
| Llama3.2-11B-Vision-Instruct | Llama3.2-11B-Vision-Instruct | fireworks0 | {} |
+------------------------------+------------------------------+---------------+------------+
| Llama3.2-90B-Vision-Instruct | Llama3.2-90B-Vision-Instruct | fireworks0 | {} |
+------------------------------+------------------------------+---------------+------------+
llama stack run ./run.yaml \
--port 5001 \
--env FIREWORKS_API_KEY=$FIREWORKS_API_KEY
```

121
docs/source/getting_started/distributions/self_hosted_distro/ollama.md
View file

@ -11,90 +11,97 @@ The `llamastack/distribution-ollama` distribution consists of the following prov
| 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.### 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.
The following models are configured by default:
- `${env.INFERENCE_MODEL}`
- `${env.SAFETY_MODEL}`
## Setting up Ollama server
## Using Docker Compose
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.
You can use `docker compose` to start a Ollama server and connect with Llama Stack server in a single command.
In order to load models, you can run:
```bash
$ cd distributions/ollama; docker compose up
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
```
You will see outputs similar to following ---
If you are using Llama Stack Safety / Shield APIs, you will also need to pull and run the safety model.
```bash
[ollama] | [GIN] 2024/10/18 - 21:19:41 | 200 | 226.841µs | ::1 | GET "/api/ps"
[ollama] | [GIN] 2024/10/18 - 21:19:42 | 200 | 60.908µs | ::1 | GET "/api/ps"
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)
[llamastack] | Resolved 12 providers
[llamastack] | inner-inference => ollama0
[llamastack] | models => __routing_table__
[llamastack] | inference => __autorouted__
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
```
To kill the server
## 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
docker compose down
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 \
--gpus=all \
llamastack/distribution-ollama \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env OLLAMA_URL=http://host.docker.internal:11434
```
## Starting Ollama and Llama Stack separately
If you are using Llama Stack Safety / Shield APIs, use:
If you wish to separately spin up a Ollama server, and connect with Llama Stack, you should use the following commands.
#### Start Ollama server
- Please check the [Ollama Documentation](https://github.com/ollama/ollama) for more details.
**Via Docker**
```bash
docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
-v ./run-with-safety.yaml:/root/my-run.yaml \
--gpus=all \
llamastack/distribution-ollama \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env OLLAMA_URL=http://host.docker.internal:11434
```
**Via CLI**
```bash
ollama run <model_id>
```
### Via Conda
#### Start Llama Stack server pointing to Ollama server
**Via Conda**
Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
```bash
llama stack build --template ollama --image-type conda
llama stack run run.yaml
llama stack run ./run.yaml \
--port 5001 \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env OLLAMA_URL=http://127.0.0.1:11434
```
**Via Docker**
```
docker run --network host -it -p 5000:5000 -v ~/.llama:/root/.llama -v ./gpu/run.yaml:/root/llamastack-run-ollama.yaml --gpus=all llamastack/distribution-ollama --yaml_config /root/llamastack-run-ollama.yaml
```
Make sure in your `run.yaml` file, your inference provider is pointing to the correct Ollama endpoint. E.g.
```yaml
inference:
- provider_id: ollama0
provider_type: remote::ollama
config:
url: http://127.0.0.1:14343
```
### (Optional) Update Model Serving Configuration
#### Downloading model via Ollama
You can use ollama for managing model downloads.
If you are using Llama Stack Safety / Shield APIs, use:
```bash
ollama pull llama3.1:8b-instruct-fp16
ollama pull llama3.1:70b-instruct-fp16
llama stack run ./run-with-safety.yaml \
--port 5001 \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env OLLAMA_URL=http://127.0.0.1:11434
```
### (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.

139
docs/source/getting_started/distributions/self_hosted_distro/remote-vllm.md
View file

@ -12,77 +12,106 @@ The `llamastack/distribution-remote-vllm` distribution consists of the following
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:
- `${env.INFERENCE_MODEL}`
- `${env.SAFETY_MODEL}`
## Using Docker Compose
You can use `docker compose` to start a vLLM container and Llama Stack server container together.
```bash
$ cd distributions/remote-vllm; docker compose up
```
## Setting up vLLM server
You will see outputs similar to following ---
```
<TO BE FILLED>
```
To kill the server
```bash
docker compose down
```
## Starting vLLM and Llama Stack separately
You can also decide to start a vLLM server and connect with Llama Stack manually. There are two ways to start a vLLM server and connect with Llama Stack.
#### Start 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
docker run --runtime nvidia --gpus all \
export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export CUDA_VISIBLE_DEVICES=0
docker run \
--runtime nvidia \
--gpus $CUDA_VISIBLE_DEVICES \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HUGGING_FACE_HUB_TOKEN=<secret>" \
-p 8000:8000 \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
-p $INFERENCE_PORT:$INFERENCE_PORT \
--ipc=host \
vllm/vllm-openai:latest \
--model meta-llama/Llama-3.2-3B-Instruct
--model $INFERENCE_MODEL \
--port $INFERENCE_PORT
```
Please check the [vLLM Documentation](https://docs.vllm.ai/en/v0.5.5/serving/deploying_with_docker.html) for more details.
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:
#### Start Llama Stack server pointing to your vLLM server
We have provided a template `run.yaml` file in the `distributions/remote-vllm` directory. Please make sure to modify the `inference.provider_id` to point to your vLLM server endpoint. As an example, if your vLLM server is running on `http://127.0.0.1:8000`, your `run.yaml` file should look like the following:
```yaml
inference:
- provider_id: vllm0
provider_type: remote::vllm
config:
url: http://127.0.0.1:8000
```
**Via Conda**
If you are using Conda, you can build and run the Llama Stack server with the following commands:
```bash
cd distributions/remote-vllm
llama stack build --template remote-vllm --image-type conda
llama stack run run.yaml
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 \
--model $SAFETY_MODEL \
--port $SAFETY_PORT
```
**Via Docker**
## 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.
You can use the Llama Stack Docker image to start the server with the following command:
```bash
docker run --network host -it -p 5000:5000 \
-v ~/.llama:/root/.llama \
-v ./gpu/run.yaml:/root/llamastack-run-remote-vllm.yaml \
--gpus=all \
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/llamastack-run-remote-vllm.yaml
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_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-remote-vllm \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env VLLM_SAFETY_URL=http://host.docker.internal:$SAFETY_PORT
```
### Via Conda
Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
```bash
llama stack build --template remote-vllm --image-type conda
llama stack run ./run.yaml \
--port 5001 \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_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 VLLM_URL=http://127.0.0.1:$INFERENCE_PORT \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env VLLM_SAFETY_URL=http://127.0.0.1:$SAFETY_PORT
```

28
docs/source/getting_started/distributions/self_hosted_distro/tgi.md
View file

@ -29,13 +29,13 @@ The following environment variables can be configured:
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 TGI_INFERENCE_PORT=8080
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 $TGI_INFERENCE_PORT:$TGI_INFERENCE_PORT \
-p $INFERENCE_PORT:$INFERENCE_PORT \
--gpus $CUDA_VISIBLE_DEVICES \
ghcr.io/huggingface/text-generation-inference:2.3.1 \
--dtype bfloat16 \
@ -43,29 +43,29 @@ docker run --rm -it \
--sharded false \
--cuda-memory-fraction 0.7 \
--model-id $INFERENCE_MODEL \
--port $TGI_INFERENCE_PORT
--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 TGI_SAFETY_PORT=8081
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 $TGI_SAFETY_PORT:$TGI_SAFETY_PORT \
-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 $TGI_SAFETY_PORT
--port $SAFETY_PORT
```
## Running Llama Stack with TGI as the inference provider
## 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.
@ -76,7 +76,6 @@ This method allows you to get started quickly without having to build the distri
```bash
LLAMA_STACK_PORT=5001
docker run \
--network host \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
@ -84,14 +83,13 @@ docker run \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env TGI_URL=http://host.docker.internal:$TGI_INFERENCE_PORT
--env TGI_URL=http://host.docker.internal:$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 \
@ -99,9 +97,9 @@ docker run \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env TGI_URL=http://host.docker.internal:$TGI_INFERENCE_PORT \
--env TGI_URL=http://host.docker.internal:$INFERENCE_PORT \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env TGI_SAFETY_URL=http://host.docker.internal:$TGI_SAFETY_PORT
--env TGI_SAFETY_URL=http://host.docker.internal:$SAFETY_PORT
```
### Via Conda
@ -113,7 +111,7 @@ llama stack build --template tgi --image-type conda
llama stack run ./run.yaml
--port 5001
--env INFERENCE_MODEL=$INFERENCE_MODEL
--env TGI_URL=http://127.0.0.1:$TGI_INFERENCE_PORT
--env TGI_URL=http://127.0.0.1:$INFERENCE_PORT
```
If you are using Llama Stack Safety / Shield APIs, use:
@ -122,7 +120,7 @@ If you are using Llama Stack Safety / Shield APIs, use:
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 TGI_URL=http://127.0.0.1:$INFERENCE_PORT
--env SAFETY_MODEL=$SAFETY_MODEL
--env TGI_SAFETY_URL=http://127.0.0.1:$TGI_SAFETY_PORT
--env TGI_SAFETY_URL=http://127.0.0.1:$SAFETY_PORT
```

103
docs/source/getting_started/distributions/self_hosted_distro/together.md
View file

@ -1,62 +1,67 @@
# Together Distribution
### Connect to a Llama Stack Together Endpoint
- You may connect to a hosted endpoint `https://llama-stack.together.ai`, serving a Llama Stack distribution
# Fireworks Distribution
The `llamastack/distribution-together` distribution consists of the following provider configurations.
| **API** | **Inference** | **Agents** | **Memory** | **Safety** | **Telemetry** |
|----------------- |--------------- |---------------- |-------------------------------------------------- |---------------- |---------------- |
| **Provider(s)** | remote::together | meta-reference | meta-reference, remote::weaviate | meta-reference | meta-reference |
| 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` |
### Docker: Start the Distribution (Single Node CPU)
### Environment Variables
> [!NOTE]
> This assumes you have an hosted endpoint at Together with API Key.
The following environment variables can be configured:
```
$ cd distributions/together && docker compose up
- `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/Meta-Llama-3.1-8B-Instruct-Turbo`
- `meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo`
- `meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo`
- `meta-llama/Llama-3.2-3B-Instruct-Turbo`
- `meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo`
- `meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo`
- `meta-llama/Meta-Llama-Guard-3-8B`
- `meta-llama/Llama-Guard-3-11B-Vision-Turbo`
### 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 \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env TOGETHER_API_KEY=$TOGETHER_API_KEY
```
Make sure in your `run.yaml` file, your inference provider is pointing to the correct Together URL server endpoint. E.g.
```
inference:
- provider_id: together
provider_type: remote::together
config:
url: https://api.together.xyz/v1
api_key: <optional api key>
```
### Conda llama stack run (Single Node CPU)
### Via Conda
```bash
llama stack build --template together --image-type conda
# -- modify run.yaml to a valid Together server endpoint
llama stack run ./run.yaml
```
### (Optional) Update Model Serving Configuration
Use `llama-stack-client models list` to check the available models served by together.
```
$ llama-stack-client models list
+------------------------------+------------------------------+---------------+------------+
| identifier | llama_model | provider_id | metadata |
+==============================+==============================+===============+============+
| Llama3.1-8B-Instruct | Llama3.1-8B-Instruct | together0 | {} |
+------------------------------+------------------------------+---------------+------------+
| Llama3.1-70B-Instruct | Llama3.1-70B-Instruct | together0 | {} |
+------------------------------+------------------------------+---------------+------------+
| Llama3.1-405B-Instruct | Llama3.1-405B-Instruct | together0 | {} |
+------------------------------+------------------------------+---------------+------------+
| Llama3.2-3B-Instruct | Llama3.2-3B-Instruct | together0 | {} |
+------------------------------+------------------------------+---------------+------------+
| Llama3.2-11B-Vision-Instruct | Llama3.2-11B-Vision-Instruct | together0 | {} |
+------------------------------+------------------------------+---------------+------------+
| Llama3.2-90B-Vision-Instruct | Llama3.2-90B-Vision-Instruct | together0 | {} |
+------------------------------+------------------------------+---------------+------------+
llama stack run ./run.yaml \
--port 5001 \
--env TOGETHER_API_KEY=$TOGETHER_API_KEY
```

9
llama_stack/providers/remote/inference/fireworks/config.py
View file

@ -4,7 +4,7 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Optional
from typing import Any, Dict, Optional
from llama_models.schema_utils import json_schema_type
from pydantic import BaseModel, Field
@ -20,3 +20,10 @@ class FireworksImplConfig(BaseModel):
default=None,
description="The Fireworks.ai API Key",
)
@classmethod
def sample_run_config(cls) -> Dict[str, Any]:
return {
"url": "https://api.fireworks.ai/inference",
"api_key": "${env.FIREWORKS_API_KEY}",
}

4
llama_stack/providers/remote/inference/fireworks/fireworks.py
View file

@ -35,7 +35,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .config import FireworksImplConfig
model_aliases = [
MODEL_ALIASES = [
build_model_alias(
"fireworks/llama-v3p1-8b-instruct",
CoreModelId.llama3_1_8b_instruct.value,
@ -79,7 +79,7 @@ class FireworksInferenceAdapter(
ModelRegistryHelper, Inference, NeedsRequestProviderData
):
def __init__(self, config: FireworksImplConfig) -> None:
ModelRegistryHelper.__init__(self, model_aliases)
ModelRegistryHelper.__init__(self, MODEL_ALIASES)
self.config = config
self.formatter = ChatFormat(Tokenizer.get_instance())

9
llama_stack/providers/remote/inference/together/config.py
View file

@ -4,7 +4,7 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Optional
from typing import Any, Dict, Optional
from llama_models.schema_utils import json_schema_type
from pydantic import BaseModel, Field
@ -20,3 +20,10 @@ class TogetherImplConfig(BaseModel):
default=None,
description="The Together AI API Key",
)
@classmethod
def sample_run_config(cls) -> Dict[str, Any]:
return {
"url": "https://api.together.xyz/v1",
"api_key": "${env.TOGETHER_API_KEY}",
}

4
llama_stack/providers/remote/inference/together/together.py
View file

@ -38,7 +38,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .config import TogetherImplConfig
model_aliases = [
MODEL_ALIASES = [
build_model_alias(
"meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
CoreModelId.llama3_1_8b_instruct.value,
@ -78,7 +78,7 @@ class TogetherInferenceAdapter(
ModelRegistryHelper, Inference, NeedsRequestProviderData
):
def __init__(self, config: TogetherImplConfig) -> None:
ModelRegistryHelper.__init__(self, model_aliases)
ModelRegistryHelper.__init__(self, MODEL_ALIASES)
self.config = config
self.formatter = ChatFormat(Tokenizer.get_instance())

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

@ -0,0 +1,7 @@
# 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

20
llama_stack/templates/fireworks/build.yaml
View file

@ -1,11 +1,19 @@
version: '2'
name: fireworks
distribution_spec:
description: Use Fireworks.ai for running LLM inference
description: Use Fireworks.AI for running LLM inference
docker_image: null
providers:
inference: remote::fireworks
inference:
- remote::fireworks
memory:
- inline::faiss
- remote::weaviate
safety: inline::llama-guard
agents: inline::meta-reference
telemetry: inline::meta-reference
- remote::chromadb
- remote::pgvector
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
image_type: conda

60
llama_stack/templates/fireworks/doc_template.md Normal file
View file

@ -0,0 +1,60 @@
# Fireworks 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 }}`
{% 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=5001
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-{{ name }} \
/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
```

60
llama_stack/templates/fireworks/fireworks.py Normal file
View file

@ -0,0 +1,60 @@
# 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
from llama_stack.providers.remote.inference.fireworks import FireworksImplConfig
from llama_stack.providers.remote.inference.fireworks.fireworks import MODEL_ALIASES
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::fireworks"],
"memory": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
}
inference_provider = Provider(
provider_id="fireworks",
provider_type="remote::fireworks",
config=FireworksImplConfig.sample_run_config(),
)
default_models = [ModelInput(model_id=m.provider_model_id) for m in MODEL_ALIASES]
return DistributionTemplate(
name="fireworks",
distro_type="self_hosted",
description="Use Fireworks.AI for running LLM inference",
docker_image=None,
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
default_models=default_models,
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider],
},
default_models=default_models,
default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
),
},
run_config_env_vars={
"LLAMASTACK_PORT": (
"5001",
"Port for the Llama Stack distribution server",
),
"FIREWORKS_API_KEY": (
"",
"Fireworks.AI API Key",
),
},
)

127
llama_stack/templates/ollama/doc_template.md
View file

@ -6,103 +6,106 @@ The `llamastack/distribution-{{ name }}` distribution consists of the following
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 docker_compose_env_vars %}
{%- if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in docker_compose_env_vars.items() %}
{% 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 configured by default:
{% for model in default_models %}
- `{{ model.model_id }}`
{% endfor %}
{% endif %}
## Setting up Ollama server
## Using Docker Compose
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.
You can use `docker compose` to start a Ollama server and connect with Llama Stack server in a single command.
In order to load models, you can run:
```bash
$ cd distributions/{{ name }}; docker compose up
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
```
You will see outputs similar to following ---
If you are using Llama Stack Safety / Shield APIs, you will also need to pull and run the safety model.
```bash
[ollama] | [GIN] 2024/10/18 - 21:19:41 | 200 | 226.841µs | ::1 | GET "/api/ps"
[ollama] | [GIN] 2024/10/18 - 21:19:42 | 200 | 60.908µs | ::1 | GET "/api/ps"
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)
[llamastack] | Resolved 12 providers
[llamastack] | inner-inference => ollama0
[llamastack] | models => __routing_table__
[llamastack] | inference => __autorouted__
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
```
To kill the server
## 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
docker compose down
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 \
--gpus=all \
llamastack/distribution-{{ name }} \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env OLLAMA_URL=http://host.docker.internal:11434
```
## Starting Ollama and Llama Stack separately
If you are using Llama Stack Safety / Shield APIs, use:
If you wish to separately spin up a Ollama server, and connect with Llama Stack, you should use the following commands.
#### Start Ollama server
- Please check the [Ollama Documentation](https://github.com/ollama/ollama) for more details.
**Via Docker**
```bash
docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
-v ./run-with-safety.yaml:/root/my-run.yaml \
--gpus=all \
llamastack/distribution-{{ name }} \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env OLLAMA_URL=http://host.docker.internal:11434
```
**Via CLI**
```bash
ollama run <model_id>
```
### Via Conda
#### Start Llama Stack server pointing to Ollama server
**Via Conda**
Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
```bash
llama stack build --template ollama --image-type conda
llama stack run run.yaml
llama stack run ./run.yaml \
--port 5001 \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env OLLAMA_URL=http://127.0.0.1:11434
```
**Via Docker**
```
docker run --network host -it -p 5000:5000 -v ~/.llama:/root/.llama -v ./gpu/run.yaml:/root/llamastack-run-ollama.yaml --gpus=all llamastack/distribution-ollama --yaml_config /root/llamastack-run-ollama.yaml
```
Make sure in your `run.yaml` file, your inference provider is pointing to the correct Ollama endpoint. E.g.
```yaml
inference:
- provider_id: ollama0
provider_type: remote::ollama
config:
url: http://127.0.0.1:14343
```
### (Optional) Update Model Serving Configuration
#### Downloading model via Ollama
You can use ollama for managing model downloads.
If you are using Llama Stack Safety / Shield APIs, use:
```bash
ollama pull llama3.1:8b-instruct-fp16
ollama pull llama3.1:70b-instruct-fp16
llama stack run ./run-with-safety.yaml \
--port 5001 \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env OLLAMA_URL=http://127.0.0.1:11434
```
### (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.

12
llama_stack/templates/ollama/ollama.py
View file

@ -68,17 +68,17 @@ def get_distribution_template() -> DistributionTemplate:
"5001",
"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 TGI server",
),
"OLLAMA_URL": (
"http://host.docker.internal:11434",
"URL of the Ollama server",
"Inference model loaded into the Ollama server",
),
"SAFETY_MODEL": (
"meta-llama/Llama-Guard-3-1B",
"Name of the safety (Llama-Guard) model to use",
"Safety model loaded into the Ollama server",
),
},
)

2
llama_stack/templates/remote-vllm/build.yaml
View file

@ -2,7 +2,7 @@ version: '2'
name: remote-vllm
distribution_spec:
description: Use (an external) vLLM server for running LLM inference
docker_image: llamastack/distribution-remote-vllm:test-0.0.52rc3
docker_image: null
providers:
inference:
- remote::vllm

148
llama_stack/templates/remote-vllm/doc_template.md
View file

@ -6,90 +6,114 @@ The `llamastack/distribution-{{ name }}` distribution consists of the following
You can use this distribution if you have GPUs and want to run an independent vLLM server container for running inference.
{%- if docker_compose_env_vars %}
{% if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in docker_compose_env_vars.items() %}
{% 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 configured by default:
{% for model in default_models %}
- `{{ model.model_id }}`
{% endfor %}
{% endif %}
## Setting up vLLM server
## Using Docker Compose
You can use `docker compose` to start a vLLM container and Llama Stack server container together.
```bash
$ cd distributions/{{ name }}; docker compose up
```
You will see outputs similar to following ---
```
<TO BE FILLED>
```
To kill the server
```bash
docker compose down
```
## Starting vLLM and Llama Stack separately
You can also decide to start a vLLM server and connect with Llama Stack manually. There are two ways to start a vLLM server and connect with Llama Stack.
#### Start 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
docker run --runtime nvidia --gpus all \
export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export CUDA_VISIBLE_DEVICES=0
docker run \
--runtime nvidia \
--gpus $CUDA_VISIBLE_DEVICES \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HUGGING_FACE_HUB_TOKEN=<secret>" \
-p 8000:8000 \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
-p $INFERENCE_PORT:$INFERENCE_PORT \
--ipc=host \
vllm/vllm-openai:latest \
--model meta-llama/Llama-3.2-3B-Instruct
--model $INFERENCE_MODEL \
--port $INFERENCE_PORT
```
Please check the [vLLM Documentation](https://docs.vllm.ai/en/v0.5.5/serving/deploying_with_docker.html) for more details.
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 \
--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
LLAMA_STACK_PORT=5001
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-{{ name }} \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_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-{{ name }} \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env VLLM_SAFETY_URL=http://host.docker.internal:$SAFETY_PORT
```
### Via Conda
Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
#### Start Llama Stack server pointing to your vLLM server
We have provided a template `run.yaml` file in the `distributions/remote-vllm` directory. Please make sure to modify the `inference.provider_id` to point to your vLLM server endpoint. As an example, if your vLLM server is running on `http://127.0.0.1:8000`, your `run.yaml` file should look like the following:
```yaml
inference:
- provider_id: vllm0
provider_type: remote::vllm
config:
url: http://127.0.0.1:8000
```
**Via Conda**
If you are using Conda, you can build and run the Llama Stack server with the following commands:
```bash
cd distributions/remote-vllm
llama stack build --template remote-vllm --image-type conda
llama stack run run.yaml
llama stack run ./run.yaml \
--port 5001 \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_URL=http://127.0.0.1:$INFERENCE_PORT
```
**Via Docker**
If you are using Llama Stack Safety / Shield APIs, use:
You can use the Llama Stack Docker image to start the server with the following command:
```bash
docker run --network host -it -p 5000:5000 \
-v ~/.llama:/root/.llama \
-v ./gpu/run.yaml:/root/llamastack-run-remote-vllm.yaml \
--gpus=all \
llamastack/distribution-remote-vllm \
--yaml_config /root/llamastack-run-remote-vllm.yaml
llama stack run ./run-with-safety.yaml \
--port 5001 \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_URL=http://127.0.0.1:$INFERENCE_PORT \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env VLLM_SAFETY_URL=http://127.0.0.1:$SAFETY_PORT
```

1
llama_stack/templates/remote-vllm/vllm.py
View file

@ -41,7 +41,6 @@ def get_distribution_template() -> DistributionTemplate:
name="remote-vllm",
distro_type="self_hosted",
description="Use (an external) vLLM server for running LLM inference",
docker_image="llamastack/distribution-remote-vllm:test-0.0.52rc3",
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
default_models=[inference_model, safety_model],

28
llama_stack/templates/tgi/doc_template.md
View file

@ -22,13 +22,13 @@ The following environment variables can be configured:
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 TGI_INFERENCE_PORT=8080
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 $TGI_INFERENCE_PORT:$TGI_INFERENCE_PORT \
-p $INFERENCE_PORT:$INFERENCE_PORT \
--gpus $CUDA_VISIBLE_DEVICES \
ghcr.io/huggingface/text-generation-inference:2.3.1 \
--dtype bfloat16 \
@ -36,29 +36,29 @@ docker run --rm -it \
--sharded false \
--cuda-memory-fraction 0.7 \
--model-id $INFERENCE_MODEL \
--port $TGI_INFERENCE_PORT
--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 TGI_SAFETY_PORT=8081
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 $TGI_SAFETY_PORT:$TGI_SAFETY_PORT \
-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 $TGI_SAFETY_PORT
--port $SAFETY_PORT
```
## Running Llama Stack with TGI as the inference provider
## 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.
@ -69,7 +69,6 @@ This method allows you to get started quickly without having to build the distri
```bash
LLAMA_STACK_PORT=5001
docker run \
--network host \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
@ -77,14 +76,13 @@ docker run \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env TGI_URL=http://host.docker.internal:$TGI_INFERENCE_PORT
--env TGI_URL=http://host.docker.internal:$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 \
@ -92,9 +90,9 @@ docker run \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env TGI_URL=http://host.docker.internal:$TGI_INFERENCE_PORT \
--env TGI_URL=http://host.docker.internal:$INFERENCE_PORT \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env TGI_SAFETY_URL=http://host.docker.internal:$TGI_SAFETY_PORT
--env TGI_SAFETY_URL=http://host.docker.internal:$SAFETY_PORT
```
### Via Conda
@ -106,7 +104,7 @@ llama stack build --template {{ name }} --image-type conda
llama stack run ./run.yaml
--port 5001
--env INFERENCE_MODEL=$INFERENCE_MODEL
--env TGI_URL=http://127.0.0.1:$TGI_INFERENCE_PORT
--env TGI_URL=http://127.0.0.1:$INFERENCE_PORT
```
If you are using Llama Stack Safety / Shield APIs, use:
@ -115,7 +113,7 @@ If you are using Llama Stack Safety / Shield APIs, use:
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 TGI_URL=http://127.0.0.1:$INFERENCE_PORT
--env SAFETY_MODEL=$SAFETY_MODEL
--env TGI_SAFETY_URL=http://127.0.0.1:$TGI_SAFETY_PORT
--env TGI_SAFETY_URL=http://127.0.0.1:$SAFETY_PORT
```

7
llama_stack/templates/together/__init__.py Normal file
View file

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

20
llama_stack/templates/together/build.yaml
View file

@ -1,11 +1,19 @@
version: '2'
name: together
distribution_spec:
description: Use Together.ai for running LLM inference
description: Use Together.AI for running LLM inference
docker_image: null
providers:
inference: remote::together
inference:
- remote::together
memory:
- inline::faiss
- remote::weaviate
safety: inline::llama-guard
agents: inline::meta-reference
telemetry: inline::meta-reference
- remote::chromadb
- remote::pgvector
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
image_type: conda

60
llama_stack/templates/together/doc_template.md Normal file
View file

@ -0,0 +1,60 @@
# Fireworks 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 }}`
{% endfor %}
{% endif %}
### 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-{{ name }} \
/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
```

60
llama_stack/templates/together/together.py Normal file
View file

@ -0,0 +1,60 @@
# 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
from llama_stack.providers.remote.inference.together import TogetherImplConfig
from llama_stack.providers.remote.inference.together.together import MODEL_ALIASES
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::together"],
"memory": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
}
inference_provider = Provider(
provider_id="together",
provider_type="remote::together",
config=TogetherImplConfig.sample_run_config(),
)
default_models = [ModelInput(model_id=m.provider_model_id) for m in MODEL_ALIASES]
return DistributionTemplate(
name="together",
distro_type="self_hosted",
description="Use Together.AI for running LLM inference",
docker_image=None,
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
default_models=default_models,
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider],
},
default_models=default_models,
default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-1B")],
),
},
run_config_env_vars={
"LLAMASTACK_PORT": (
"5001",
"Port for the Llama Stack distribution server",
),
"TOGETHER_API_KEY": (
"",
"Together.AI API Key",
),
},
)