---
id: lmstudio
title: LM Studio
sidebar_label: LM Studio
---

`deepeval` supports running evaluations using local LLMs that expose OpenAI-compatible APIs. One such provider is **LM Studio**, a user-friendly desktop app for running models locally.

### Command Line

To start using LM Studio with `deepeval`, follow these steps:

1. Make sure LM Studio is running. The typical base URL for LM Studio is: `http://localhost:1234/v1/`.
2. Run the following command in your terminal to connect `deepeval` to LM Studio:

```bash
deepeval set-local-model \
    --model=<model_name> \
    --base-url="http://localhost:1234/v1/"
```

:::tip
If your local endpoint doesn't require authentication enter any placeholder string when prompted to enter an api key.
:::

:::tip[Persisting settings]
You can persist CLI settings with the optional `--save` flag.
See [Flags and Configs -> Persisting CLI settings](/docs/evaluation-flags-and-configs#persisting-cli-settings-with---save).
:::

### Python

Alternatively, you can define `LocalModel` directly in Python code:

<Tabs items={["Python", "ENV"]}>
<Tab value="Python">

```python
from deepeval.models import LocalModel
from deepeval.metrics import AnswerRelevancyMetric

model = LocalModel(
    model="<model_name>",
    base_url="http://localhost:1234/v1/",
    api_key="lm-studio",  # any placeholder works if your server has no auth
    temperature=0
)

answer_relevancy = AnswerRelevancyMetric(model=model)
```

</Tab>
<Tab value="ENV">

To use a local model directly in `deepeval`, set `USE_LOCAL_MODEL=1` in your `env` and simply pass the name of your desired model in your metric initialization:

```python
from deepeval.metrics import AnswerRelevancyMetric

answer_relevancy = AnswerRelevancyMetric(
    model="<model_name>",
)
```

You should also set the other necessary vars like `LOCAL_MODEL_BASE_URL` and `LOCAL_MODEL_API_KEY` to be able to use your local model as shown above.

</Tab>
</Tabs>

There are **ZERO** mandatory and **SIX** optional parameters when creating a `LocalModel`:

- [Optional] `model`: A string specifying the local model to use. Defaults to `LOCAL_MODEL_NAME` if not passed; raises an error at runtime if unset.
- [Optional] `api_key`: A string specifying the API key for your local server. Defaults to `LOCAL_MODEL_API_KEY` if not passed; raises an error at runtime if unset. Local servers without authentication accept any placeholder string.
- [Optional] `base_url`: A string specifying the base URL of your local server. Defaults to `LOCAL_MODEL_BASE_URL` if not passed.
- [Optional] `temperature`: A float specifying the model temperature. Defaults to `TEMPERATURE` if not passed; falls back to `0.0` if unset.
- [Optional] `format`: A string specifying the structured-output response format. Defaults to `LOCAL_MODEL_FORMAT` if not passed; falls back to `"json"` if unset.
- [Optional] `generation_kwargs`: A dictionary of additional generation parameters forwarded to the local server's `chat.completions.create(...)` call.

:::tip
Any `**kwargs` you would like to use for your model can be passed directly to `LocalModel(...)`; these are forwarded to the underlying OpenAI client constructor.
:::

### Reverting to OpenAI

To switch back to using OpenAI’s hosted models, run:

```bash
deepeval unset-local-model
```

:::info
For more help on enabling LM Studio’s server or configuring models, check out the [LM Studio docs](https://lmstudio.ai/).
:::