> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/roboflow/roboflow-hi/deploy/supported-models/florence-2.md).

# Florence 2

हम समर्थन करते हैं [Microsoft का Florence 2](https://huggingface.co/microsoft/Florence-2-base), एक multimodal vision-language model, हमारे [Serverless Hosted API](/roboflow/roboflow-hi/deploy/serverless-hosted-api-v2.md). Florence 2 captioning, object detection, segmentation, और OCR को task prompts के माध्यम से सपोर्ट करता है (जैसे `<CAPTION>`, `<OD>`, `<OCR>`, `<REFERRING_EXPRESSION_SEGMENTATION>`).

## डिफ़ॉल्ट aliases

alias को `model_id` अपने request में उपयोग करें, और runtime इसे संबंधित pretrained weights में resolve कर देता है.

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

## कोड उदाहरण

Florence 2 साझा `/infer/lmm` endpoint. इसे सीधे call करें 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>"
  }'
```

SDK के माध्यम से वही call. इसे install करें और LMM inference endpoint को task prompt के साथ call करें. अपना [Roboflow API Key](https://app.roboflow.com/settings/api) के माध्यम से `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>': 'एक आदमी अपनी पीठ पर एक कुत्ता लिए हुए।'}
```

{% hint style="info" %}
सेट करें `api_url` को अपने deployment target से मिलाने के लिए:

* `https://serverless.roboflow.com` Serverless Hosted API के लिए।
* `http://localhost:9001` एक स्थानीय [Inference](https://inference.roboflow.com/) सर्वर।
* आपका [Dedicated Deployment](/roboflow/roboflow-hi/deploy/dedicated-deployments.md) एक private endpoint के लिए URL।
  {% endhint %}

स्विच करें `<CAPTION>` किसी भी समर्थित task prompt के लिए (उदाहरण के लिए `<DETAILED_CAPTION>`, `<OD>`, `<OCR>`, `<OPEN_VOCABULARY_DETECTION>`, `<REFERRING_EXPRESSION_SEGMENTATION>`) captioning, detection, OCR, और segmentation tasks के बीच स्विच करने के लिए.

self-hosted deployment और task prompts की पूरी सूची के लिए, देखें [Inference documentation](https://inference.roboflow.com/).


---

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