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feat: consolidate most distros into "starter" (#2516)
# What does this PR do? * Removes a bunch of distros * Removed distros were added into the "starter" distribution * Doc for "starter" has been added * Partially reverts https://github.com/meta-llama/llama-stack/pull/2482 since inference providers are disabled by default and can be turned on manually via env variable. * Disables safety in starter distro Closes: https://github.com/meta-llama/llama-stack/issues/2502. ~Needs: https://github.com/meta-llama/llama-stack/pull/2482 for Ollama to work properly in the CI.~ TODO: - [ ] We can only update `install.sh` when we get a new release. - [x] Update providers documentation - [ ] Update notebooks to reference starter instead of ollama Signed-off-by: Sébastien Han <seb@redhat.com>
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Sébastien Han
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---
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orphan: true
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---
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<!-- This file was auto-generated by distro_codegen.py, please edit source -->
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# Ollama Distribution
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```{toctree}
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:maxdepth: 2
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:hidden:
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self
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```
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The `llamastack/distribution-ollama` distribution consists of the following provider configurations.
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| API | Provider(s) |
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|-----|-------------|
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| agents | `inline::meta-reference` |
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| datasetio | `remote::huggingface`, `inline::localfs` |
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| eval | `inline::meta-reference` |
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| files | `inline::localfs` |
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| inference | `remote::ollama` |
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| post_training | `inline::huggingface` |
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| safety | `inline::llama-guard` |
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| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
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| telemetry | `inline::meta-reference` |
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| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
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| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
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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.
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### Environment Variables
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The following environment variables can be configured:
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- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
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- `OLLAMA_URL`: URL of the Ollama server (default: `http://127.0.0.1:11434`)
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- `INFERENCE_MODEL`: Inference model loaded into the Ollama server (default: `meta-llama/Llama-3.2-3B-Instruct`)
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- `SAFETY_MODEL`: Safety model loaded into the Ollama server (default: `meta-llama/Llama-Guard-3-1B`)
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## Setting up Ollama server
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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.
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In order to load models, you can run:
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```bash
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export INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct"
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# ollama names this model differently, and we must use the ollama name when loading the model
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export OLLAMA_INFERENCE_MODEL="llama3.2:3b-instruct-fp16"
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ollama run $OLLAMA_INFERENCE_MODEL --keepalive 60m
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```
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If you are using Llama Stack Safety / Shield APIs, you will also need to pull and run the safety model.
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```bash
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export SAFETY_MODEL="meta-llama/Llama-Guard-3-1B"
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# ollama names this model differently, and we must use the ollama name when loading the model
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export OLLAMA_SAFETY_MODEL="llama-guard3:1b"
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ollama run $OLLAMA_SAFETY_MODEL --keepalive 60m
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```
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## Running Llama Stack
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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.
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### Via Docker
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This method allows you to get started quickly without having to build the distribution code.
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```bash
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export LLAMA_STACK_PORT=8321
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docker run \
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-it \
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--pull always \
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-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
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-v ~/.llama:/root/.llama \
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llamastack/distribution-ollama \
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--port $LLAMA_STACK_PORT \
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--env INFERENCE_MODEL=$INFERENCE_MODEL \
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--env OLLAMA_URL=http://host.docker.internal:11434
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```
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If you are using Llama Stack Safety / Shield APIs, use:
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```bash
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# You need a local checkout of llama-stack to run this, get it using
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# git clone https://github.com/meta-llama/llama-stack.git
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cd /path/to/llama-stack
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docker run \
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-it \
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--pull always \
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-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
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-v ~/.llama:/root/.llama \
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-v ./llama_stack/templates/ollama/run-with-safety.yaml:/root/my-run.yaml \
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llamastack/distribution-ollama \
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--config /root/my-run.yaml \
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--port $LLAMA_STACK_PORT \
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--env INFERENCE_MODEL=$INFERENCE_MODEL \
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--env SAFETY_MODEL=$SAFETY_MODEL \
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--env OLLAMA_URL=http://host.docker.internal:11434
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```
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### Via Conda
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Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
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```bash
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export LLAMA_STACK_PORT=8321
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llama stack build --template ollama --image-type conda
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llama stack run ./run.yaml \
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--port $LLAMA_STACK_PORT \
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--env INFERENCE_MODEL=$INFERENCE_MODEL \
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--env OLLAMA_URL=http://localhost:11434
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```
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If you are using Llama Stack Safety / Shield APIs, use:
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```bash
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llama stack run ./run-with-safety.yaml \
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--port $LLAMA_STACK_PORT \
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--env INFERENCE_MODEL=$INFERENCE_MODEL \
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--env SAFETY_MODEL=$SAFETY_MODEL \
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--env OLLAMA_URL=http://localhost:11434
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```
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### (Optional) Update Model Serving Configuration
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```{note}
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Please check the [model_entries](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/ollama/models.py) for the supported Ollama models.
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```
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To serve a new model with `ollama`
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```bash
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ollama run <model_name>
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```
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To make sure that the model is being served correctly, run `ollama ps` to get a list of models being served by ollama.
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```
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$ ollama ps
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NAME ID SIZE PROCESSOR UNTIL
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llama3.2:3b-instruct-fp16 195a8c01d91e 8.6 GB 100% GPU 9 minutes from now
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```
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To verify that the model served by ollama is correctly connected to Llama Stack server
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```bash
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$ llama-stack-client models list
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Available Models
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┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━┓
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┃ model_type ┃ identifier ┃ provider_resource_id ┃ metadata ┃ provider_id ┃
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┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━┩
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│ llm │ meta-llama/Llama-3.2-3B-Instruct │ llama3.2:3b-instruct-fp16 │ │ ollama │
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└──────────────┴──────────────────────────────────────┴──────────────────────────────┴───────────┴─────────────┘
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Total models: 1
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```
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