Llava Instruction Tuning Data for Vision Language Models

Presenting a reformatted version of theblackcat102/llava-instruct-mix, prepared for Vision Supervised Fine-Tuning (VSFT) with the TRL SFT Trainer. It is designed for instruction tuning of multimodal vision-language models. The dataset's author is HuggingFaceH4, and it was last updated in April 2024.

Use Cases

• Fine-tune a vision-language model for instruction following using the reformatted instruction tuning data.
• Apply Vision Supervised Fine-Tuning (VSFT) with the TRL SFT Trainer on the multimodal instruction data.
• Benchmark instruction-following performance in multimodal models using the provided English instruction tuning format.

Strengths

• Dataset is specifically formatted for use with the TRL library's SFT Trainer, a standard tool for fine-tuning.
• Last update was in April 2024, indicating recent maintenance.

Limitations

• The dataset size, row count, and specific column structure are unknown, making it difficult to assess scale and structure.
• The original data source and its collection methodology are not detailed, limiting understanding of data provenance.

Provenance

Source

Reformatted from theblackcat102/llava-instruct-mix dataset.

Collection Method

Reformatted for Vision Supervised Fine-Tuning (VSFT) with TRL's SFT Trainer.

Freshness

Last updated on 2024-04-11.