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*This model was released on 2023-09-20 and added to Hugging Face Transformers on 2025-08-19.*

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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# KOSMOS-2.5

The Kosmos-2.5 model was proposed in [KOSMOS-2.5: A Multimodal Literate Model](https://huggingface.co/papers/2309.11419/) by Microsoft.

The abstract from the paper is the following:

*We present Kosmos-2.5, a multimodal literate model for machine reading of text-intensive images. Pre-trained on large-scale text-intensive images, Kosmos-2.5 excels in two distinct yet cooperative transcription tasks: (1) generating spatially-aware text blocks, where each block of text is assigned its spatial coordinates within the image, and (2) producing structured text output that captures styles and structures into the markdown format. This unified multimodal literate capability is achieved through a shared Transformer architecture, task-specific prompts, and flexible text representations. We evaluate Kosmos-2.5 on end-to-end document-level text recognition and image-to-markdown text generation. Furthermore, the model can be readily adapted for any text-intensive image understanding task with different prompts through supervised fine-tuning, making it a general-purpose tool for real-world applications involving text-rich images. This work also paves the way for the future scaling of multimodal large language models.*

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/kosmos2_5_ocr.png"
alt="drawing" width="600"/>

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/kosmos2_5_md.png"
alt="drawing" width="600"/>

<small> Overview of tasks that KOSMOS-2.5 can handle. Taken from the <a href="https://huggingface.co/papers/2309.11419">original paper</a>. </small>

The examples below demonstrates how to generate with [`AutoModel`], for both Markdown and OCR tasks.

<hfoptions id="usage">
<hfoption id="AutoModel - Markdown Task">

```py
import re
import torch
import requests
from PIL import Image, ImageDraw
from transformers import AutoProcessor, Kosmos2_5ForConditionalGeneration
from accelerate import Accelerator

repo = "microsoft/kosmos-2.5"
device = "cuda:0"
dtype = torch.bfloat16
model = Kosmos2_5ForConditionalGeneration.from_pretrained(repo, device_map=device, dtype=dtype)
processor = AutoProcessor.from_pretrained(repo)

# sample image
url = "https://huggingface.co/microsoft/kosmos-2.5/resolve/main/receipt_00008.png"
image = Image.open(requests.get(url, stream=True).raw)

prompt = "<md>"
inputs = processor(text=prompt, images=image, return_tensors="pt")

height, width = inputs.pop("height"), inputs.pop("width")
raw_width, raw_height = image.size
scale_height = raw_height / height
scale_width = raw_width / width

inputs = {k: v.to(device) if v is not None else None for k, v in inputs.items()}
inputs["flattened_patches"] = inputs["flattened_patches"].to(dtype)
generated_ids = model.generate(
    **inputs,
    max_new_tokens=1024,
)

generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
print(generated_text[0])
```

</hfoption>
<hfoption id="AutoModel - OCR Task">

```py
import re
import torch
import requests
from PIL import Image, ImageDraw
from transformers import AutoProcessor, Kosmos2_5ForConditionalGeneration
from accelerate import Accelerator

repo = "microsoft/kosmos-2.5"
device = "cuda:0"
dtype = torch.bfloat16
model = Kosmos2_5ForConditionalGeneration.from_pretrained(repo, device_map=device, dtype=dtype)
processor = AutoProcessor.from_pretrained(repo)

# sample image
url = "https://huggingface.co/microsoft/kosmos-2.5/resolve/main/receipt_00008.png"
image = Image.open(requests.get(url, stream=True).raw)

# bs = 1
prompt = "<ocr>"
inputs = processor(text=prompt, images=image, return_tensors="pt")
height, width = inputs.pop("height"), inputs.pop("width")
raw_width, raw_height = image.size
scale_height = raw_height / height
scale_width = raw_width / width

# bs > 1, batch generation
# inputs = processor(text=[prompt, prompt], images=[image,image], return_tensors="pt")
# height, width = inputs.pop("height"), inputs.pop("width")
# raw_width, raw_height = image.size
# scale_height = raw_height / height[0]
# scale_width = raw_width / width[0]

inputs = {k: v.to(device) if v is not None else None for k, v in inputs.items()}
inputs["flattened_patches"] = inputs["flattened_patches"].to(dtype)
generated_ids = model.generate(
    **inputs,
    max_new_tokens=1024,
)

generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
def post_process(y, scale_height, scale_width):
    y = y.replace(prompt, "")
    if "<md>" in prompt:
        return y
    pattern = r"<bbox><x_\d+><y_\d+><x_\d+><y_\d+></bbox>"
    bboxs_raw = re.findall(pattern, y)
    lines = re.split(pattern, y)[1:]
    bboxs = [re.findall(r"\d+", i) for i in bboxs_raw]
    bboxs = [[int(j) for j in i] for i in bboxs]
    info = ""
    for i in range(len(lines)):
        box = bboxs[i]
        x0, y0, x1, y1 = box
        if not (x0 >= x1 or y0 >= y1):
            x0 = int(x0 * scale_width)
            y0 = int(y0 * scale_height)
            x1 = int(x1 * scale_width)
            y1 = int(y1 * scale_height)
            info += f"{x0},{y0},{x1},{y0},{x1},{y1},{x0},{y1},{lines[i]}"
    return info

output_text = post_process(generated_text[0], scale_height, scale_width)
print(output_text)

draw = ImageDraw.Draw(image)
lines = output_text.split("\n")
for line in lines:
    # draw the bounding box
    line = list(line.split(","))
    if len(line) < 8:
        continue
    line = list(map(int, line[:8]))
    draw.polygon(line, outline="red")
image.save("output.png")
```

</hfoption>
</hfoptions>

## Chat version

The authors also released Kosmos-2.5 Chat, which is a chat version optimized for document understanding. You can use it like so:

```python
import re
import torch
import requests
from PIL import Image, ImageDraw
from transformers import AutoProcessor, Kosmos2_5ForConditionalGeneration

repo = "microsoft/kosmos-2.5-chat"
device = "cuda:0"
dtype = torch.bfloat16

model = Kosmos2_5ForConditionalGeneration.from_pretrained(repo,
                                                          device_map=device,
                                                          torch_dtype=dtype,
                                                          attn_implementation="flash_attention_2")
processor = AutoProcessor.from_pretrained(repo)

# sample image
url = "https://huggingface.co/microsoft/kosmos-2.5/resolve/main/receipt_00008.png"

image = Image.open(requests.get(url, stream=True).raw)

question = "What is the sub total of the receipt?"
template = "<md>A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {} ASSISTANT:"
prompt = template.format(question)
inputs = processor(text=prompt, images=image, return_tensors="pt")

height, width = inputs.pop("height"), inputs.pop("width")
raw_width, raw_height = image.size
scale_height = raw_height / height
scale_width = raw_width / width

inputs = {k: v.to(device) if v is not None else None for k, v in inputs.items()}
inputs["flattened_patches"] = inputs["flattened_patches"].to(dtype)
generated_ids = model.generate(
    **inputs,
    max_new_tokens=1024,
)

generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
print(generated_text[0])
```

## Kosmos2_5Config

[[autodoc]] Kosmos2_5Config

## Kosmos2_5ImageProcessor

[[autodoc]] Kosmos2_5ImageProcessor
    - preprocess

## Kosmos2_5ImageProcessorFast

[[autodoc]] Kosmos2_5ImageProcessorFast
    - preprocess

## Kosmos2_5Processor

[[autodoc]] Kosmos2_5Processor

## Kosmos2_5Model

[[autodoc]] Kosmos2_5Model
    - forward

## Kosmos2_5ForConditionalGeneration

[[autodoc]] Kosmos2_5ForConditionalGeneration
    - forward
