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2025-03-23 15:48:14 -07:00

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Ollama Distribution

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self

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

API	Provider(s)
agents	`inline::meta-reference`
datasetio	`remote::huggingface`, `inline::localfs`
inference	`remote::ollama`
safety	`inline::llama-guard`
telemetry	`inline::meta-reference`
tool_runtime	`remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha`
vector_io	`inline::faiss`, `remote::chromadb`, `remote::pgvector`

You should use this distribution if you have a regular desktop machine without very powerful GPUs. Of course, if you have powerful GPUs, you can still continue using this distribution since Ollama supports GPU acceleration.

Environment Variables

The following environment variables can be configured:

LLAMA_STACK_PORT: Port for the Llama Stack distribution server (default: 8321)
OLLAMA_URL: URL of the Ollama server (default: http://127.0.0.1:11434)
INFERENCE_MODEL: Inference model loaded into the Ollama server (default: meta-llama/Llama-3.2-3B-Instruct)
SAFETY_MODEL: Safety model loaded into the Ollama server (default: meta-llama/Llama-Guard-3-1B)

Setting up Ollama server

Please check the Ollama Documentation 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:

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.

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.

export LLAMA_STACK_PORT=8321
docker run \
  -it \
  --pull always \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  -v ~/.llama:/root/.llama \
  llamastack/distribution-ollama \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=$INFERENCE_MODEL \
  --env OLLAMA_URL=http://host.docker.internal:11434

If you are using Llama Stack Safety / Shield APIs, use:

# You need a local checkout of llama-stack to run this, get it using
# git clone https://github.com/meta-llama/llama-stack.git
cd /path/to/llama-stack

docker run \
  -it \
  --pull always \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  -v ~/.llama:/root/.llama \
  -v ./llama_stack/templates/ollama/run-with-safety.yaml:/root/my-run.yaml \
  llamastack/distribution-ollama \
  --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 uv pip install llama-stack and have the Llama Stack CLI available.

export LLAMA_STACK_PORT=8321

llama stack build --template ollama --image-type conda
llama stack run ./run.yaml \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=$INFERENCE_MODEL \
  --env OLLAMA_URL=http://localhost:11434

If you are using Llama Stack Safety / Shield APIs, use:

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

Please check the [model_entries](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/ollama/models.py) for the supported Ollama models.

To serve a new model with ollama

ollama run <model_name>

To make sure that the model is being served correctly, run ollama ps to get a list of models being served by ollama.

$ ollama ps
NAME                         ID              SIZE      PROCESSOR    UNTIL
llama3.2:3b-instruct-fp16    195a8c01d91e    8.6 GB    100% GPU     9 minutes from now

To verify that the model served by ollama is correctly connected to Llama Stack server

$ llama-stack-client models list

Available Models

┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ model_type   ┃ identifier                           ┃ provider_resource_id         ┃ metadata  ┃ provider_id ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ llm          │ meta-llama/Llama-3.2-3B-Instruct     │ llama3.2:3b-instruct-fp16    │           │ ollama      │
└──────────────┴──────────────────────────────────────┴──────────────────────────────┴───────────┴─────────────┘

Total models: 1

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