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108 lines
3.3 KiB
Markdown
108 lines
3.3 KiB
Markdown
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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*This model was published in HF papers on 2024-05-31 and contributed to Hugging Face Transformers on 2025-08-18.*
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# Ovis2
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## Overview
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The [Ovis2](https://github.com/AIDC-AI/Ovis) is an updated version of the [Ovis](https://huggingface.co/papers/2405.20797) model developed by the AIDC-AI team at Alibaba International Digital Commerce Group.
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Ovis2 is the latest advancement in multi-modal large language models (MLLMs), succeeding Ovis1.6. It retains the architectural design of the Ovis series, which focuses on aligning visual and textual embeddings, and introduces major improvements in data curation and training methods.
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<img src="https://cdn-uploads.huggingface.co/production/uploads/637aebed7ce76c3b834cea37/XB-vgzDL6FshrSNGyZvzc.png" width="600">
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<small> Ovis2 architecture.</small>
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This model was contributed by [thisisiron](https://huggingface.co/thisisiron).
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## Usage example
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```python
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import requests
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import torch
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from PIL import Image
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from transformers import AutoModelForImageTextToText, AutoProcessor
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model = AutoModelForImageTextToText.from_pretrained(
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"thisisiron/Ovis2-2B-hf",
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).eval().to(model.device, device_map="auto")
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processor = AutoProcessor.from_pretrained("thisisiron/Ovis2-2B-hf")
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{"type": "text", "text": "Describe the image."},
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],
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},
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]
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url = "http://images.cocodataset.org/val2014/COCO_val2014_000000537955.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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messages = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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print(messages)
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inputs = processor(
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images=[image],
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text=messages,
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return_tensors="pt",
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)
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inputs = inputs.to(model.device)
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inputs['pixel_values'] = inputs['pixel_values'].to(torch.bfloat16)
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with torch.inference_mode():
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output_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False)
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generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
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output_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
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print(output_text)
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```
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## Ovis2Config
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[[autodoc]] Ovis2Config
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## Ovis2VisionConfig
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[[autodoc]] Ovis2VisionConfig
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## Ovis2Model
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[[autodoc]] Ovis2Model
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## Ovis2ForConditionalGeneration
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[[autodoc]] Ovis2ForConditionalGeneration
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- forward
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- get_image_features
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## Ovis2ImageProcessor
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[[autodoc]] Ovis2ImageProcessor
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- preprocess
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## Ovis2ImageProcessorPil
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[[autodoc]] Ovis2ImageProcessorPil
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- preprocess
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## Ovis2Processor
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[[autodoc]] Ovis2Processor
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- __call__ |