# LM Studio Distribution The `llamastack/distribution-lmstudio` distribution consists of the following provider configurations. | API | Provider(s) | |-----|-------------| | agents | `inline::meta-reference` | | datasetio | `remote::huggingface`, `inline::localfs` | | eval | `inline::meta-reference` | | inference | `remote::lmstudio` | | safety | `inline::llama-guard` | | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | | tool_runtime | `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime` | | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | ### Environment Variables The following environment variables can be configured: - `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `5001`) ### Models The following models are available by default: - `meta-llama-3-8b-instruct ` - `meta-llama-3-70b-instruct ` - `meta-llama-3.1-8b-instruct ` - `meta-llama-3.1-70b-instruct ` - `llama-3.2-1b-instruct ` - `llama-3.2-3b-instruct ` - `llama-3.3-70b-instruct ` - `nomic-embed-text-v1.5 ` - `all-minilm-l6-v2 ` ## Set up LM Studio Download LM Studio from [https://lmstudio.ai/download](https://lmstudio.ai/download). Start the server by opening LM Studio and navigating to the `Developer` Tab, or, run the CLI command `lms server start`. ## Running Llama Stack with LM Studio 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-lmstudio \ --yaml-config /root/my-run.yaml \ --port $LLAMA_STACK_PORT ``` ### Via Conda ```bash llama stack build --template lmstudio --image-type conda llama stack run ./run.yaml \ --port 5001 ```