# Florence 2

We support [Microsoft's Florence 2](https://huggingface.co/microsoft/Florence-2-base), a multimodal vision-language model, via our [Serverless Hosted API](/deploy/serverless-hosted-api-v2.md). Florence 2 supports captioning, object detection, segmentation, and OCR through task prompts (such as `<CAPTION>`, `<OD>`, `<OCR>`, `<REFERRING_EXPRESSION_SEGMENTATION>`).

## Default aliases

Use the alias as the `model_id` in your request and the runtime resolves it to the corresponding pretrained weights.

| Alias              |
| ------------------ |
| `florence-2-base`  |
| `florence-2-large` |

## Code sample

Florence 2 runs through the shared `/infer/lmm` endpoint. Call it directly with `curl`:

```bash
curl --location 'https://serverless.roboflow.com/infer/lmm' \
  --header 'Content-Type: application/json' \
  --data '{
    "api_key": "YOUR_API_KEY",
    "image": {"type": "url", "value": "https://media.roboflow.com/notebooks/examples/dog.jpeg"},
    "model_id": "florence-2-base",
    "prompt": "<CAPTION>"
  }'
```

The same call through the SDK. Install it and call the LMM inference endpoint with a task prompt. Pass your [Roboflow API Key](https://app.roboflow.com/settings/api) via the `API_KEY` environment variable.

```bash
pip install inference-sdk
```

```python
import os
import urllib.request
from inference_sdk import InferenceHTTPClient

image_url = "https://media.roboflow.com/notebooks/examples/dog.jpeg"
image_path = "dog.jpeg"
urllib.request.urlretrieve(image_url, image_path)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.getenv("API_KEY"),
)

result = client.infer_lmm(
    inference_input=image_path,
    model_id="florence-2-base",
    prompt="<CAPTION>",
)

print(result["response"])  # {'<CAPTION>': 'A man carrying a dog on his back.'}
```

{% hint style="info" %}
Set `api_url` to match your deployment target:

* `https://serverless.roboflow.com` for the Serverless Hosted API.
* `http://localhost:9001` for a local [Inference](https://inference.roboflow.com/) server.
* Your [Dedicated Deployment](/deploy/dedicated-deployments.md) URL for a private endpoint.
  {% endhint %}

Swap `<CAPTION>` for any supported task prompt (for example `<DETAILED_CAPTION>`, `<OD>`, `<OCR>`, `<OPEN_VOCABULARY_DETECTION>`, `<REFERRING_EXPRESSION_SEGMENTATION>`) to switch between captioning, detection, OCR, and segmentation tasks.

For self-hosted deployment and the full list of task prompts, see the [Inference documentation](https://inference.roboflow.com/).


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.roboflow.com/deploy/supported-models/florence-2.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.