data_summary_card.md

 

 Data Summary for microsoft_llava-med-7b-delta 

 

 

## 1. General information 

**1.0.1 Version of the Summary:** 1.0 

 

**1.0.2 Last update:** 24-Nov-2025  

 

 ## 1.1 Model Developer Identification 

**1.1.1 Model Developer name and contact details:** Microsoft Corporation at One Microsoft Way, Redmond, WA 98052. Tel: 425-882-8080. 

 

 ## 1.2 Model Identification 

**1.2.1 Versioned model name(s):** LLaVA-Med-7b-delta 

 

**1.2.2 Model release date:** 09-Nov-2023 

 

 ## 1.3 Overall training data size and characteristics 

 ### 1.3.1 Size of dataset and characteristics 

**1.3.1.A Text training data size:** Less than 1 billion tokens 

 

**1.3.1.B Text training data content:** LLaVA-Med builds upon PMC-15M dataset, which is a large-scale parallel image-text dataset for biomedical vision-language processing. It contains 15 million figure-caption pairs extracted from biomedical research articles in PubMed Central. 

 

**1.3.1.C Image training data size:** Less than 1 billion tokens 

 

 

**1.3.1.D Image training data content:** Figure images from biomedical research articles in PubMed Central paired with captions across modalities including microscopy, radiography, histology, CT, MRI, chest X-ray, pathology figures 

 

**1.3.1.E Audio training data size:** Not applicable 

 

**1.3.1.F Audio training data content:** Not applicable 

 

**1.3.1.G Video training data size:** Not applicable 

 

**1.3.1.H Video training data content:** Not applicable 

 

**1.3.1.I Other training data size:** The model also uses instruction-following conversational data generated by GPT-4 from captions and inline mentions; sizes include versions of 10K and 60K samples 

 

**1.3.1.J Other training data content:** GPT-4 generated multi-round biomedical instruction-following conversations derived from figure captions and sentences mentioning figures (inline mentions) from PubMed Central articles 

 

**1.3.2 Latest date of data acquisition/collection for model training:** 01-May-2023 

**1.3.3 Is data collection ongoing to update the model with new data collection after deployment?** No 

 

**1.3.4 Date the training dataset was first used to train the model:** 01-May-2023 

 

**1.3.5 Rationale or purpose of data selection:** Datasets were selected to adapt a general-domain multimodal model to biomedical vision-language tasks by aligning biomedical concepts and enabling instruction-following. PMC-15M figure-caption pairs provide broad coverage of biomedical images and terminology, while GPT-4 generated conversations from captions and inline mentions create diverse, open-ended instruction data to support visual chat and VQA performance 

 

 ## 2. List of data sources 

 ### 2.1 Publicly available datasets 

**2.1.1 Have you used publicly available datasets to train the model?** Yes 

 

 ## 2.2 Private non-publicly available datasets obtained from third parties 

### 2.2.1 Datasets commercially licensed by rights holders or their representatives 

**2.2.1.A Have you concluded transactional commercial licensing agreement(s) with rights holder(s) or with their representatives?** This information cannot be provided due to unavailability of the underlying data (e.g., loss, corruption, or other access limitations) 

 ### 2.2.2 Private datasets obtained from other third-parties 

** 2.2.2.A Have you obtained private datasets from third parties that are not licensed as described in Section 2.2.1, such as data obtained from providers of private databases, or data intermediaries?** No 

 

## 2.3 Personal Information 

**2.3.1 Was personal data used to train the model?** Microsoft follows all relevant laws and regulations pertaining to personal information. 

 

 ## 2.4 Synthetic data 

**2.4.1 Was any synthetic AI-generated data used to train the model?** Yes 

 

 ## 3. Data processing aspects 

## 3.1 Respect of reservation of rights from text and data mining exception or limitation 

**3.1.1 Does this dataset include any data protected by copyright, trademark, or patent?** Microsoft follows all required regulations and laws for processing data protected by copyright, trademark, or patent. 

 

 ### 3.2 Other information 

**3.2.1 Does the dataset include information about consumer groups without revealing individual consumer identities?** Microsoft follows all required regulations and laws for protecting consumer identities. 

 

**3.2.2 Was the dataset cleaned or modified before model training?** Yes