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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: image-text-to-text |
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--- |
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# Model description |
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We are excited to announce the continuation and rebranding of our **BLIP series** into **XGen-MM**, to be better aligned with Salesforce's unified XGen initiative for large foundation models! This rebranding marks a significant step in our ongoing development of cutting-edge multimodal technologies. |
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`XGen-MM` is a series of the latest foundational Large Multimodal Models (LMMs) developed by Salesforce AI Research. This series advances upon the successful designs of the `BLIP` series, incorporating fundamental enhancements that ensure a more robust and superior foundation. These models have been trained at scale on high-quality image caption datasets and interleaved image-text data. |
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In the v1.5 (08/2024) release, we present a series of XGen-MM models including: |
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- [🤗 xGen-MM-base](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-base-r-v1.5): `xgen-mm-phi3-mini-base-r-v1.5` |
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- [🤗 xGen-MM-instruct](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1.5): `xgen-mm-phi3-mini-instruct-r-v1.5` |
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- [🤗 xGen-MM-instruct-interleave](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-multi-r-v1.5): `xgen-mm-phi3-mini-instruct-multi-r-v1.5` |
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- [🤗 xGen-MM-instruct-dpo](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-dpo-r-v1.5): `xgen-mm-phi3-mini-instruct-dpo-r-v1.5` |
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In addition to the models, we are also releasing a series of datasets for multi-modal pre-training, including: |
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- [🍃 MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens](https://arxiv.org/abs/2406.11271) |
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- [🤗 BLIP3-OCR-200M](https://huggingface.co/datasets/Salesforce/blip3-ocr-200m): a dataset with dense OCR annotations. |
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- [🤗 BLIP3-GROUNDING-50M](https://huggingface.co/datasets/Salesforce/blip3-grounding-50m): a dataset for enhancing the ability to ground semantic concepts in images. |
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- BLIP3-KALE-300M (stay tuned): a large-scale curated high-quality caption dataset. |
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For more details, check out our [tech report]() and project page (coming soon). |
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# Data |
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The base model is pre-trained on a mixture of data sources described above, with around 100 billion image-text tokens in total. |
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# Results |
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### Few-shot Evaluation on Base model (without instruction tuning) |
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| Model | Shot | VQAv2 | TextVQA | OKVQA | COCO | NoCaps | TextCaps | |
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|:--------------|:-----|:------|:--------|:------|:------|:-------|:---------| |
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| Flamingo-3B | 0 | 49.2 | 30.1 | 41.2 | 73.0 | - | - | |
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| | 4 | 53.2 | 32.7 | 43.3 | 85.0 | - | - | |
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| | 8 | 55.4 | 32.4 | 44.6 | 90.6 | - | - | |
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| MM1-3B | 0 | 46.2 | 29.4 | 26.1 | 73.5 | 55.6 | 63.3 | |
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| | 4 | 57.9 | 45.3 | 44.6 | **112.3** | 99.7 | 84.1 | |
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| | 8 | 63.6 | 44.6 | 48.4 | **114.6** | **104.7** | 88.8 | |
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| xGen-MM-base | 0 | 43.1 | 34.0 | 28.0 | 67.2 | 82.6 | 69.5 | |
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| | 4 | **66.3**| **54.2**| **48.9**| 107.6 | **100.8**| **89.9** | |
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| | 8 | **66.9**| **55.3**| **50.1**| 109.8| 104.6| **94.0**| |
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### Showcases on In-Context Learning |
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Below are some qualitative examples below of the mutli-modal in-context learning capacity of our base model. |
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<img src="icl_examples/art.png" alt="Art" width=500> |
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<img src="icl_examples/animal.png" alt="Animal" width=500> |
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<img src="icl_examples/street.png" alt="Street" width=500> |
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# How to use |
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Please check out our [inference notebook](demo.ipynb) for example code to use our model. We also provide example script for [batch inference](batch_inference.ipynb). |
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# Reproducibility: |
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The pretraining evaluation is implemented based on [OpenFlamingo: An open-source framework for training large multimodal models.](https://github.com/mlfoundations/open_flamingo). |
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Few-shot examples are randomly drawn so there will be some variance with different random seeds. |
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# Bias, Risks, Limitations, and Ethical Considerations |
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The main data sources are from the internet, including webpages, |
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image stock sites, and curated datasets released by the research community. We have excluded certain data, such as LAION, due to known CSAM concerns. |
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The model may be subject to bias from the original data source, as well as bias from LLMs and commercial APIs. |
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We strongly recommend users assess safety and fairness before applying to downstream applications. |
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# License |
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Our code and weights are released under the Creative Commons Attribution Non Commercial 4.0 [LICENSE](LICENSE.txt). Please fill out a form at [here](https://forms.gle/ffPc9oZC2ZGeJ1N68) to consult the commercial use of model weights. |
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# Code acknowledgement |
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Our training code is based on [OpenFlamingo: An open-source framework for training large multimodal models.](https://github.com/mlfoundations/open_flamingo), and part of our data preprocessing code is adapted from [LLaVA](https://github.com/haotian-liu/LLaVA). |
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Our evaluation code is based on [VLMEvalKit: Open-source evaluation toolkit of large vision-language models (LVLMs)](https://github.com/open-compass/VLMEvalKit). |
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We thank the authors for their open-source implementations. |
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# Citation |
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``` |
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@misc{xgen_mm_phi3_mini, |
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title={xgen-mm-phi3-mini-base Model Card}, |
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url={https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1}, |
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author={Salesforce AI Research}, |
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month={May}, |
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year={2024} |
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} |
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``` |
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# Troubleshoot |
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1. If you missed any packages, please consider the following |
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``` |
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pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cu121 |
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pip install open_clip_torch==2.24.0 |
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pip install einops |
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pip install einops-exts |
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pip install transformers==4.41.1 |
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``` |
