Create README.md

README.md

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+---
+library_name: peft
+license: other
+base_model: Qwen/Qwen2-VL-2B
+tags:
+- llama-factory
+- lora
+model-index:
+- name: qwen2_2b_lora_expert_generalv2-102400
+  results: []
+task_categories:
+- visual-question-answering
+language:
+- en
+pretty_name: Domain Expert Datasets
+size_categories:
+- 100K<n<1M
+---
+
+<h2 align="center">
+Linear Model Merging Unlocks Simple and Scalable Multimodal Data Mixture Optimization
+
+<br>
+
+[![arXiv](https://img.shields.io/badge/arXiv-2602.04937-b31b1b.svg)](https://www.arxiv.org/pdf/2602.04937)
+[![🤗 Model (HuggingFace)](https://img.shields.io/badge/Models-HuggingFace-FFD21E.svg?logo=huggingface&logoColor=yellow)](https://huggingface.co/collections/daviBera/mllms-merging-4-dmo)
+[![🤗 Dataset (HuggingFace)](https://img.shields.io/badge/Datasets-HuggingFace-FFD21E.svg?logo=huggingface&logoColor=yellow)](https://huggingface.co/datasets/daviBera/experts_datasets-102400)
+[![github](https://img.shields.io/badge/github-repo-blue?logo=github)](https://github.com/BerasiDavide/mLLMs_merging_4_DMO)
+</h2>
+
+This are the domain-specific datasets from the paper: "Linear Model Merging Unlocks Simple and Scalable Multimodal Data Mixture Optimization
+" ([link](https://www.arxiv.org/pdf/2602.04937)).
+
+Each dataset contains 102400 VQA samples from a specific domain: General VQA, OCR, Counting & Visual Perception, Chart Understanding.
+
+You can find many models trained on mixtures of these datasets in [this Huggingface Collection](https://huggingface.co/collections/daviBera/mllms-merging-4-dmo).
+
+### Composition
+<img src="https://cdn-uploads.huggingface.co/production/uploads/6597e62f929cd840d808b8c9/c7X6YXUDUcXIRFsnjHv-w.png" width="800">