{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"llava-instruct-150k","slug":"llava-instruct-150k","name":"LLaVA-Instruct 150K","type":"dataset","url":"https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K","page_url":"https://unfragile.ai/llava-instruct-150k","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"llava-instruct-150k__cap_0","uri":"capability://data.processing.analysis.multi.turn.visual.conversation.dataset.generation","name":"multi-turn visual conversation dataset generation","description":"Generates 58K multi-turn dialogue examples where GPT-4V analyzes images and engages in extended conversations about visual content. The dataset captures sequential question-answer pairs with context preservation across turns, enabling models to maintain coherent visual reasoning across multiple exchanges. This approach uses GPT-4V's vision capabilities to ground conversations in actual image content rather than synthetic descriptions.","intents":["Train multimodal models that can sustain coherent multi-turn conversations about images","Create instruction-following datasets where visual context persists across dialogue turns","Build models capable of answering follow-up questions that reference previously discussed image regions"],"best_for":["Teams training vision-language models for conversational AI applications","Researchers building multimodal chatbots requiring context persistence","Organizations developing visual question-answering systems with dialogue capabilities"],"limitations":["Generated via GPT-4V API calls, introducing potential biases from GPT-4V's visual understanding","Conversation quality depends on GPT-4V's ability to maintain coherence across turns","Limited to image types GPT-4V can process; no video or 3D content","Dataset frozen at generation time; no dynamic conversation adaptation"],"requires":["Access to original image corpus used for generation","Understanding of GPT-4V's visual reasoning patterns to interpret dataset biases","Vision encoder + language model architecture for fine-tuning"],"input_types":["images (JPEG, PNG, WebP)","conversation turn indices","image metadata"],"output_types":["multi-turn dialogue JSON with image references","structured conversation trees with visual grounding"],"categories":["data-processing-analysis","multimodal-dataset-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-instruct-150k__cap_1","uri":"capability://data.processing.analysis.detailed.image.description.dataset.generation","name":"detailed image description dataset generation","description":"Generates 23K comprehensive image descriptions using GPT-4V that go beyond simple captions to include spatial relationships, object attributes, scene context, and visual details. Each description is structured to capture fine-grained visual information that enables models to understand complex visual scenes. The generation leverages GPT-4V's ability to produce detailed natural language descriptions grounded in actual image content.","intents":["Train models to generate rich, detailed descriptions of visual scenes beyond basic captions","Create datasets for image understanding tasks requiring fine-grained visual analysis","Build models capable of describing spatial relationships and object interactions in images"],"best_for":["Teams training vision-language models for image captioning and description tasks","Researchers building models for accessibility applications (alt-text generation)","Organizations developing detailed image understanding systems"],"limitations":["Description length and detail level determined by GPT-4V's generation parameters at dataset creation time","May contain GPT-4V hallucinations or misinterpretations of complex visual scenes","Descriptions are English-only; no multilingual variants","Static dataset; cannot adapt description style or detail level per downstream task"],"requires":["Original image corpus with sufficient resolution for detailed analysis","Vision encoder capable of processing images at GPT-4V's input resolution","Language model fine-tuning infrastructure"],"input_types":["images (JPEG, PNG, WebP)","image identifiers"],"output_types":["detailed text descriptions (100-500 tokens per image)","structured description JSON with attribute annotations"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-instruct-150k__cap_2","uri":"capability://data.processing.analysis.complex.visual.reasoning.task.dataset.generation","name":"complex visual reasoning task dataset generation","description":"Generates 77K instruction-following examples that require multi-step visual reasoning, including counting, spatial reasoning, attribute comparison, and scene understanding. Each example pairs an image with a complex question and detailed answer generated by GPT-4V. The dataset is structured to train models on reasoning patterns that go beyond simple visual recognition, incorporating logical inference over visual elements.","intents":["Train models to perform multi-step reasoning over visual content","Create datasets for visual question-answering requiring complex inference","Build models capable of counting, comparing, and reasoning about spatial relationships in images"],"best_for":["Teams training visual reasoning models for VQA and complex scene understanding","Researchers building models for educational applications requiring visual analysis","Organizations developing AI systems for visual inspection and quality control"],"limitations":["Reasoning complexity limited by GPT-4V's inference capabilities; may miss subtle visual relationships","Question diversity depends on GPT-4V's prompt engineering at generation time","No explicit reasoning chain annotations; model must infer reasoning steps from Q&A pairs","Potential for GPT-4V to generate plausible but incorrect answers for ambiguous visual scenarios"],"requires":["Original image corpus with sufficient visual complexity for reasoning tasks","Vision encoder + language model architecture capable of multi-step inference","Fine-tuning infrastructure supporting instruction-following training"],"input_types":["images (JPEG, PNG, WebP)","question-answer pairs","reasoning task type indicators"],"output_types":["structured instruction-following examples (image + question + answer)","reasoning task classifications"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-instruct-150k__cap_3","uri":"capability://data.processing.analysis.vision.encoder.language.model.alignment.via.instruction.tuning","name":"vision encoder + language model alignment via instruction tuning","description":"Provides a dataset specifically designed to align pre-trained vision encoders with language models through instruction-following examples. The dataset demonstrates that a frozen vision encoder (e.g., CLIP) can be effectively aligned with a language model using only instruction-tuning data, without requiring end-to-end vision-language pre-training. This approach uses GPT-4V-generated examples to create a bridge between independent vision and language components.","intents":["Align existing vision encoders with language models without full retraining","Create multimodal models by combining off-the-shelf vision and language components","Demonstrate that instruction tuning alone can produce strong multimodal capabilities"],"best_for":["Teams building multimodal models with limited compute budgets","Researchers exploring modular vision-language architectures","Organizations wanting to leverage existing vision encoders with new language models"],"limitations":["Alignment quality depends on vision encoder's pre-training; weak encoders cannot be salvaged by instruction tuning","Dataset assumes vision encoder is frozen; fine-tuning encoder requires different training approach","No explicit vision-language alignment loss; relies on instruction-following to drive alignment","Instruction tuning alone may not achieve alignment quality of end-to-end vision-language pre-training"],"requires":["Pre-trained vision encoder (e.g., CLIP, ViT) with known capabilities","Language model (7B+ parameters recommended) with instruction-following capability","Projection layer or adapter to map vision encoder outputs to language model input space"],"input_types":["images","instruction-following examples","vision encoder embeddings"],"output_types":["aligned multimodal model","vision-to-language projection weights"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-instruct-150k__cap_4","uri":"capability://data.processing.analysis.gpt.4v.feedback.based.dataset.quality.control","name":"gpt-4v feedback-based dataset quality control","description":"Leverages GPT-4V's multimodal understanding to generate consistent, high-quality instruction-following examples with implicit quality control. Each example is generated by GPT-4V analyzing the actual image, ensuring descriptions and answers are grounded in visual content rather than hallucinated. This approach uses GPT-4V as both a data generator and implicit quality filter, producing dataset examples where text is verifiable against image content.","intents":["Generate instruction-following datasets with implicit quality guarantees through vision grounding","Ensure dataset examples are factually grounded in image content rather than hallucinated","Create datasets where text-image alignment is verified by GPT-4V's multimodal understanding"],"best_for":["Teams requiring high-quality instruction-tuning datasets with minimal manual curation","Researchers building datasets where text-image alignment is critical","Organizations prioritizing dataset quality over annotation cost"],"limitations":["Quality ceiling determined by GPT-4V's visual understanding; systematic biases in GPT-4V propagate to dataset","No explicit quality metrics; quality is implicit in GPT-4V's generation process","Cannot detect hallucinations where GPT-4V confidently generates plausible but incorrect information","No human verification layer; dataset quality not validated by human annotators"],"requires":["Access to GPT-4V API or equivalent multimodal model for generation","Original image corpus with sufficient diversity and quality","Computational budget for large-scale GPT-4V API calls"],"input_types":["images","generation prompts for GPT-4V"],"output_types":["instruction-following examples with implicit quality grounding","image-text pairs with verified alignment"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-instruct-150k__cap_5","uri":"capability://data.processing.analysis.instruction.following.dataset.with.diverse.task.types","name":"instruction-following dataset with diverse task types","description":"Provides a unified dataset combining three distinct task types (conversations, descriptions, reasoning) into a single instruction-following corpus. The dataset is structured to train models on diverse visual understanding tasks simultaneously, with 150K total examples spanning different reasoning patterns and interaction modalities. This multi-task structure enables models to learn generalizable visual understanding capabilities rather than task-specific patterns.","intents":["Train multimodal models on diverse visual understanding tasks in a single dataset","Create instruction-following models that generalize across conversation, description, and reasoning tasks","Build models capable of handling varied visual understanding requests without task-specific fine-tuning"],"best_for":["Teams training general-purpose multimodal models for diverse applications","Researchers exploring multi-task learning for vision-language understanding","Organizations building flexible visual AI systems handling varied user requests"],"limitations":["Task diversity may reduce specialization; models may underperform on specific tasks vs task-specific training","No explicit task weighting; all task types contribute equally to training signal","Dataset imbalance (77K reasoning vs 23K descriptions) may bias model toward reasoning tasks","No explicit task conditioning; model must infer task type from instruction context"],"requires":["Language model architecture supporting instruction-following across diverse tasks","Vision encoder capable of processing images for varied reasoning patterns","Training infrastructure supporting multi-task learning with mixed task types"],"input_types":["images","diverse instruction types (conversation, description, reasoning)","task type indicators (implicit in instruction format)"],"output_types":["diverse response types (dialogue, descriptions, reasoning answers)","structured instruction-following examples"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-instruct-150k__cap_6","uri":"capability://data.processing.analysis.large.scale.visual.instruction.tuning.corpus","name":"large-scale visual instruction tuning corpus","description":"Provides 150K instruction-following examples at scale, enabling training of multimodal models with sufficient data diversity and volume to learn robust visual understanding. The dataset size and diversity allow models to learn generalizable patterns rather than memorizing specific examples. This scale is achieved through systematic GPT-4V-based generation rather than manual annotation, making large-scale dataset creation feasible.","intents":["Train multimodal models with sufficient data volume to achieve strong generalization","Create instruction-following datasets at scale without proportional increase in annotation costs","Enable research on scaling laws for visual instruction tuning"],"best_for":["Teams training production-grade multimodal models requiring large training corpora","Researchers studying scaling effects in vision-language instruction tuning","Organizations building models where data volume is critical for performance"],"limitations":["150K examples may be insufficient for training very large models (100B+ parameters)","Dataset diversity limited by GPT-4V's generation patterns; may not cover all visual understanding scenarios","No explicit diversity metrics; dataset composition determined by generation process rather than explicit sampling","Computational cost of GPT-4V API calls makes dataset regeneration expensive"],"requires":["Original image corpus with 150K+ images","Computational budget for large-scale GPT-4V API calls","Storage and processing infrastructure for 150K instruction-following examples"],"input_types":["images (150K+)","generation prompts"],"output_types":["150K instruction-following examples","structured dataset with metadata"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-instruct-150k__cap_7","uri":"capability://data.processing.analysis.instruction.response.pair.formatting.for.supervised.fine.tuning","name":"instruction-response pair formatting for supervised fine-tuning","description":"Structures all 150K examples as instruction-response pairs in a format compatible with supervised fine-tuning (SFT) pipelines. Each example pairs a visual instruction (question, task, or directive) with a corresponding response grounded in image content. The format supports standard SFT loss computation where models learn to predict responses given instructions and images. This standardization enables direct integration with existing fine-tuning frameworks and training recipes.","intents":["Fine-tune vision-language models using standard SFT pipelines and loss functions","Create instruction datasets in formats compatible with popular training frameworks (HuggingFace, LLaMA-Factory)","Enable reproducible training by providing standardized instruction-response formats"],"best_for":["Teams using standard SFT training frameworks (HuggingFace Trainer, DeepSpeed)","Researchers reproducing LLaVA training or building on its methodology","Organizations fine-tuning open-source vision-language models"],"limitations":["SFT format assumes single correct response per instruction; doesn't capture response diversity or uncertainty","No explicit support for preference learning or RLHF-style training","Instruction-response pairs may not capture complex multi-step reasoning that requires intermediate supervision"],"requires":["Training framework supporting instruction-response pair formatting (PyTorch, HuggingFace Transformers)","Data loader that handles image-text pair batching","Standard SFT loss implementation (cross-entropy on response tokens)"],"input_types":["images","instruction text","response text"],"output_types":["fine-tuned model weights","instruction-following metrics (BLEU, ROUGE, exact match)","loss curves and training logs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-instruct-150k__headline","uri":"capability://model.training.visual.instruction.tuning.dataset","name":"visual instruction tuning dataset","description":"A comprehensive dataset of 150,000 image-text instruction-following examples designed for training multimodal models, enhancing their ability to understand and generate responses based on visual inputs.","intents":["best visual instruction tuning dataset","visual instruction dataset for multimodal training","high-quality image-text datasets for AI","datasets for training vision-language models","top datasets for visual reasoning tasks"],"best_for":["training multimodal models","enhancing visual reasoning capabilities"],"limitations":[],"requires":[],"input_types":["images","text instructions"],"output_types":["model training data"],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"low","permissions":["Access to original image corpus used for generation","Understanding of GPT-4V's visual reasoning patterns to interpret dataset biases","Vision encoder + language model architecture for fine-tuning","Original image corpus with sufficient resolution for detailed analysis","Vision encoder capable of processing images at GPT-4V's input resolution","Language model fine-tuning infrastructure","Original image corpus with sufficient visual complexity for reasoning tasks","Vision encoder + language model architecture capable of multi-step inference","Fine-tuning infrastructure supporting instruction-following training","Pre-trained vision encoder (e.g., CLIP, ViT) with known capabilities"],"failure_modes":["Generated via GPT-4V API calls, introducing potential biases from GPT-4V's visual understanding","Conversation quality depends on GPT-4V's ability to maintain coherence across turns","Limited to image types GPT-4V can process; 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