data_summary_card.md

Data Summary for microsoft_llava-med-v1.5-mistral-7b

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-v1.5-Mistral-7B

1.2.2 Model release date: 14-May-2024

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: Med builds upon PMC-15 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: Biomedical figures and images extracted from PubMed Central research articles, including microscopy, radiography, histology, chest X-rays, CT and MRI scans, gross pathology images, and diverse figure-caption pairs

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: Not applicable

1.3.1.J Other training data content: Not applicable

1.3.2 Latest date of data acquisition/collection for model training: 01-May-2024

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-2024

1.3.5 Rationale or purpose of data selection: The dataset leverages PMC-15M, a broad-coverage biomedical figure-caption corpus, to align biomedical visual concepts and enable open-ended instruction-following. This supports research use by improving biomedical VQA and visual chat capabilities through curriculum learning using diverse, automatically generated instruction data

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? Not applicable

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