AMALIA-VL-SFT: Vision and Language Training Mix for Instruction Tuning

AMALIA-VL-SFT is a vision+language training dataset mix compiled for the AMALIA project. The dataset is provided by author 'amalia-llm' and was last updated on 2026-06-30. It is composed of multiple source datasets, each contributing a single train split, excluding those derived from the core LLM training mix.

Use Cases

• Supervised fine-tuning of vision-language models based on the described training mix.
• Instruction tuning for multimodal AI systems based on the combined vision+language data.
• Benchmarking model performance on aggregated vision-language tasks based on the multiple source datasets.
• Training models for multimodal reasoning and generation based on the described SFT objective.

Strengths

• Dataset is part of a named project (AMALIA), suggesting a defined research context.
• Last update timestamp is precisely recorded as 2026-06-30 21:31:46.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

huggingface

Collection Method

Aggregated from multiple source datasets, as described in the training mix.

Freshness

Last updated 2026-06-30 21:31:46; freshness should be verified.