llama-stack/docs/source/getting_started/distributions/self_hosted_distro/ollama.md

Ollama Distribution

The llamastack/distribution-ollama distribution consists of the following provider configurations.

API	Inference	Agents	Memory	Safety	Telemetry
Provider(s)	remote::ollama	meta-reference	remote::pgvector, remote::chromadb	meta-reference	meta-reference

Using Docker Compose

You can use docker compose to start a Ollama server and connect with Llama Stack server in a single command.

Docker: Start the Distribution (Single Node regular Desktop machine)

Note

This will start an ollama server with CPU only, please see Ollama Documentations for serving models on CPU only.

$ cd distributions/ollama; docker compose up

Docker: Start a Distribution (Single Node with nvidia GPUs)

Note

This assumes you have access to GPU to start a Ollama server with access to your GPU.

$ cd distributions/ollama-gpu; docker compose up

You will see outputs similar to following ---

[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__

To kill the server

docker compose down

Starting Ollama and Llama Stack separately

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 for more details.

Via Docker

docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama

Via CLI

ollama run <model_id>

Start Llama Stack server pointing to Ollama server

Via Conda

llama stack build --template ollama --image-type conda
llama stack run ./gpu/run.yaml

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.

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.

ollama pull llama3.1:8b-instruct-fp16
ollama pull llama3.1:70b-instruct-fp16

Note

Please check the OLLAMA_SUPPORTED_MODELS for the supported Ollama models.

To serve a new model with ollama

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

$ 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'} |
+----------------------+----------------------+---------------+-----------------------------------------------+