{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"llava-1-6","slug":"llava-1-6","name":"LLaVA 1.6","type":"model","url":"https://llava-vl.github.io","page_url":"https://unfragile.ai/llava-1-6","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"llava-1-6__cap_0","uri":"capability://image.visual.visual.question.answering.with.instruction.tuning","name":"visual-question-answering-with-instruction-tuning","description":"Answers natural language questions about images by combining a frozen CLIP ViT-L/14 vision encoder with a Vicuna language model connected via a learned projection matrix. The model is trained end-to-end using a 158K instruction-tuning dataset (LLaVA-Instruct-150K) generated by GPT-4, enabling it to understand visual content and generate contextually relevant text responses to arbitrary image-based queries without task-specific fine-tuning.","intents":["I need to ask questions about image content and get detailed, contextually accurate answers","I want to build a chatbot that understands both visual and textual context in a single turn","I need to extract information from images using natural language queries rather than structured APIs"],"best_for":["researchers building multimodal AI systems","developers creating vision-language applications without large labeled datasets","teams prototyping visual understanding features with limited computational budgets"],"limitations":["Frozen CLIP vision encoder limits visual understanding to CLIP's pre-trained capabilities — cannot adapt to domain-specific visual features","Achieves 85.1% relative performance vs GPT-4 on synthetic benchmarks, indicating gaps in complex multimodal reasoning","Context window size unknown; likely limited by underlying Vicuna model","Single-image input only; no multi-image reasoning or temporal understanding"],"requires":["Image input (JPEG, PNG, or other standard formats)","Text query/instruction in natural language","GPU with sufficient VRAM for model inference (exact requirements unknown)","Python environment with PyTorch or compatible inference framework"],"input_types":["image (JPEG, PNG, WebP, or standard vision formats)","text (natural language question or instruction)"],"output_types":["text (natural language response)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-1-6__cap_1","uri":"capability://text.generation.language.multimodal.instruction.following.chat","name":"multimodal-instruction-following-chat","description":"Engages in multi-turn conversations that combine visual and textual context, interpreting user instructions that reference image content and generating coherent, contextually-aware responses. The model processes image embeddings through a projection layer into the language model's token space, allowing the Vicuna LLM to reason over both visual and linguistic information in a unified sequence.","intents":["I want to have a natural conversation about images without switching between separate vision and language tools","I need an AI assistant that can follow complex instructions that reference both visual and textual context","I want to build a conversational interface that understands images as naturally as text"],"best_for":["application developers building conversational AI with visual understanding","teams creating accessibility tools that describe images in natural dialogue","researchers studying multimodal reasoning and instruction-following"],"limitations":["No explicit multi-image reasoning — each image is processed independently","Conversation history management and context window constraints unknown","Performance degrades on images with small or dense text (CLIP encoder limitation)","No real-time streaming of responses documented"],"requires":["Image input (standard formats)","Text instruction/question in natural language","Sufficient GPU VRAM for model inference","Framework supporting multimodal input batching"],"input_types":["image (JPEG, PNG, WebP)","text (natural language instruction or question)"],"output_types":["text (natural language response)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-1-6__cap_10","uri":"capability://automation.workflow.two.stage.instruction.tuning.training.pipeline","name":"two-stage-instruction-tuning-training-pipeline","description":"Implements a two-stage training process for instruction tuning that optimizes the projection matrix and language model parameters while keeping the CLIP vision encoder frozen. The training pipeline processes image-text instruction pairs and learns to generate appropriate responses, with stages designed to progressively improve multimodal reasoning (specific stage details not fully documented).","intents":["I want to understand how to efficiently train multimodal models in stages","I need to train a vision-language model with limited compute resources","I want to implement a reproducible training pipeline for multimodal instruction-tuning"],"best_for":["researchers studying training strategies for vision-language models","teams implementing custom multimodal training pipelines","developers optimizing training efficiency for multimodal systems"],"limitations":["Two-stage process details not documented — unclear what each stage optimizes or how they differ","No published ablation studies comparing one-stage vs two-stage training","Training time estimate (1 day on 8 A100s) is for LLaVA-1.5; LLaVA 1.6 training time unknown","No guidance on hyperparameter selection, learning rate schedules, or convergence criteria","Reproducibility limited by undocumented training details"],"requires":["8× A100 GPUs (or equivalent high-memory GPU cluster)","158K instruction-tuning dataset (or custom equivalent)","PyTorch training framework","Distributed training setup (likely using torch.distributed or similar)"],"input_types":["image-text instruction pairs (JSON or HuggingFace format)"],"output_types":["trained model weights","training metrics and checkpoints"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-1-6__cap_11","uri":"capability://code.generation.editing.open.source.model.weights.and.code.distribution","name":"open-source-model-weights-and-code-distribution","description":"Provides publicly-available model weights, training code, and inference code through official GitHub repository and HuggingFace Model Hub, enabling researchers and developers to reproduce results, fine-tune models, and deploy systems without proprietary dependencies. The open-source release includes the trained LLaVA 1.6 model, training scripts, and evaluation benchmarks.","intents":["I want to use a vision-language model without API dependencies or licensing restrictions","I need to reproduce published results and verify model performance","I want to fine-tune or customize a vision-language model for my domain"],"best_for":["academic researchers requiring reproducibility and transparency","open-source advocates building fully-open systems","teams with on-premise deployment requirements"],"limitations":["Model weights may be large (likely 7-13B parameters); requires significant storage and download bandwidth","Training code requires 8 A100 GPUs for reproduction — not accessible to most individual researchers","License for GPT-4-generated training data unclear for commercial use","No official support or SLA — community-driven maintenance","Inference optimization (quantization, distillation) not documented"],"requires":["GitHub account or HuggingFace account for access","Python 3.8+ with PyTorch","GPU with sufficient VRAM for model inference (8-16 GB estimated)","Internet connection for model download (likely 10-30 GB)"],"input_types":["model weights (safetensors or PyTorch format)","training code (Python scripts)","inference code (Python scripts)"],"output_types":["trained model (for fine-tuning)","inference outputs (text responses)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-1-6__cap_2","uri":"capability://text.generation.language.detailed.image.description.generation","name":"detailed-image-description-generation","description":"Generates comprehensive, multi-sentence descriptions of image content by processing visual features through the CLIP encoder and using the Vicuna language model to produce detailed, structured narratives. The model is trained on 23K detailed description samples from the LLaVA-Instruct-150K dataset, enabling it to produce descriptions that go beyond simple captions to include spatial relationships, object attributes, and contextual information.","intents":["I need to automatically generate detailed alt-text for images in accessibility applications","I want to create rich image descriptions for content management systems without manual annotation","I need to extract structured information about image composition and content for cataloging"],"best_for":["accessibility teams building alt-text generation systems","content platforms requiring automated image description at scale","digital asset management systems needing rich metadata extraction"],"limitations":["Descriptions are generated text, not structured metadata — no semantic tagging or object bounding boxes","Quality depends on CLIP's visual understanding; may miss fine-grained details or domain-specific visual concepts","No control over description length or style (e.g., formal vs casual tone)","Hallucination possible — model may describe objects not present in image"],"requires":["Image input (standard formats)","GPU for inference","Optional: instruction prompt to guide description style"],"input_types":["image (JPEG, PNG, WebP)"],"output_types":["text (natural language description)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-1-6__cap_3","uri":"capability://planning.reasoning.visual.reasoning.over.complex.scenes","name":"visual-reasoning-over-complex-scenes","description":"Performs multi-step logical reasoning over image content to answer questions requiring inference, comparison, or synthesis of visual information. The model is trained on 77K complex reasoning samples from LLaVA-Instruct-150K, enabling it to decompose visual scenes, identify relationships between objects, and generate explanations for its reasoning rather than just factual answers.","intents":["I need to ask questions about images that require reasoning (e.g., 'Why is this happening?' or 'What will happen next?')","I want to extract causal or logical relationships from visual content","I need an AI that can explain its visual understanding, not just classify objects"],"best_for":["educational platforms requiring visual reasoning assessment","scientific image analysis tools needing interpretable reasoning","quality assurance systems analyzing complex visual scenarios"],"limitations":["Reasoning quality capped at Vicuna's language model capabilities — complex multi-step logic may fail","No explicit reasoning chain visualization or step-by-step explanation output","Performance on Science QA (92.53%) suggests domain-specific reasoning still requires fine-tuning","Frozen CLIP encoder cannot learn domain-specific visual patterns needed for specialized reasoning"],"requires":["Image input (standard formats)","Text question requiring reasoning (not simple factual queries)","GPU for inference"],"input_types":["image (JPEG, PNG, WebP)","text (reasoning-based question)"],"output_types":["text (reasoning explanation and answer)"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-1-6__cap_4","uri":"capability://image.visual.science.domain.visual.understanding","name":"science-domain-visual-understanding","description":"Achieves state-of-the-art performance on Science QA benchmark (92.53% accuracy) by combining visual understanding with scientific knowledge reasoning. The model processes scientific diagrams, charts, and experimental images through CLIP encoding and generates answers grounded in both visual content and scientific reasoning, demonstrating domain-specific capability without explicit science-domain fine-tuning.","intents":["I need to automatically answer science questions that include diagrams or experimental images","I want to build an educational tool that understands scientific visualizations","I need to extract insights from scientific images without domain-specific model training"],"best_for":["educational technology platforms for STEM learning","scientific research tools requiring visual diagram understanding","automated grading systems for science exams with visual content"],"limitations":["Performance is 92.53% on Science QA but relative performance vs GPT-4 is 85.1% on synthetic benchmarks, indicating gaps in complex scientific reasoning","No explicit domain adaptation — performance emerges from general instruction-tuning rather than science-specific fine-tuning","Limited to 2D diagrams and charts; may struggle with 3D scientific visualizations or microscopy images","Requires clear, well-formatted scientific images; performance on low-quality or hand-drawn diagrams unknown"],"requires":["Science-related image (diagram, chart, experimental photo)","Science question in natural language","GPU for inference"],"input_types":["image (JPEG, PNG, WebP — scientific diagrams, charts, photos)","text (science question)"],"output_types":["text (answer with reasoning)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-1-6__cap_5","uri":"capability://code.generation.editing.end.to.end.multimodal.model.training","name":"end-to-end-multimodal-model-training","description":"Enables training of vision-language models by combining a frozen CLIP ViT-L/14 vision encoder with a Vicuna language model through a learned projection matrix, using a two-stage instruction-tuning process. The training pipeline accepts image-text instruction pairs and optimizes the projection layer and language model parameters while keeping vision encoder weights fixed, completing full training in approximately 1 day on 8 A100 GPUs.","intents":["I want to train a custom vision-language model without building from scratch","I need to adapt a multimodal model to my specific domain or dataset","I want to understand how to efficiently train vision-language models with limited compute"],"best_for":["researchers experimenting with multimodal architectures","teams with domain-specific image-text data wanting to build custom models","developers prototyping vision-language applications with limited GPU budgets"],"limitations":["Frozen CLIP encoder cannot be fine-tuned — limits adaptation to CLIP's visual understanding capabilities","Two-stage training process details not fully documented — unclear what stages optimize","Requires 8 A100 GPUs for 1-day training; no guidance on single-GPU or distributed training strategies","Training data must be in instruction-following format; no support for raw image-caption pairs without reformatting","No built-in data augmentation or curriculum learning strategies documented"],"requires":["8× A100 GPUs (or equivalent high-memory GPU cluster)","Image-text instruction-following dataset (minimum ~150K samples recommended based on LLaVA-Instruct-150K)","PyTorch or compatible deep learning framework","Python 3.8+","CUDA 11.8+ for GPU support"],"input_types":["image (JPEG, PNG, WebP)","text (instruction-following format: question, answer, or conversation)"],"output_types":["model weights (trained vision-language model)","training logs and metrics"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-1-6__cap_6","uri":"capability://data.processing.analysis.synthetic.instruction.data.generation.and.curation","name":"synthetic-instruction-data-generation-and-curation","description":"Provides a publicly-released 158K instruction-tuning dataset (LLaVA-Instruct-150K) generated by GPT-4 from COCO image-text pairs, organized into three categories: conversation (58K samples), detailed description (23K samples), and complex reasoning (77K samples). This dataset enables training of vision-language models without manual annotation, and is available on HuggingFace Dataset hub for reproducible research and model development.","intents":["I need a large, high-quality instruction-tuning dataset for vision-language model training without manual annotation costs","I want to understand how to generate synthetic multimodal instruction data using language models","I need to benchmark my model against a standard instruction-tuning dataset"],"best_for":["researchers training vision-language models with limited annotation budgets","teams building multimodal datasets for specific domains","developers studying synthetic data generation for AI training"],"limitations":["Data is GPT-4-generated, not human-annotated — may contain hallucinations or biases from GPT-4","Based on COCO dataset; limited to general object recognition domains — may not transfer well to specialized domains (medical, scientific, industrial)","No explicit quality filtering or human validation documented","License implications of GPT-4-generated data for commercial use unknown","Conversation samples (58K) may have limited diversity in dialogue patterns"],"requires":["HuggingFace account for dataset access","Python with datasets library (pip install datasets)","Storage for 158K image-text pairs (~20-30 GB estimated)","Understanding of instruction-tuning format for model training"],"input_types":["COCO image-text pairs (base data)"],"output_types":["instruction-tuning dataset (JSON or HuggingFace format)","three subcategories: conversation, detailed description, complex reasoning"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-1-6__cap_7","uri":"capability://image.visual.clip.vision.encoder.integration","name":"clip-vision-encoder-integration","description":"Integrates a frozen CLIP ViT-L/14 vision encoder as the visual feature extractor, converting images into embeddings that are projected into the language model's token space via a learned projection matrix. The frozen encoder ensures stable visual feature extraction while the projection layer learns to align visual and linguistic representations during training.","intents":["I need a pre-trained, reliable vision encoder that doesn't require fine-tuning","I want to leverage CLIP's broad visual understanding without training a vision model from scratch","I need to understand how to integrate pre-trained vision encoders with language models"],"best_for":["teams building multimodal systems with limited vision-specific expertise","researchers studying vision-language alignment","developers prioritizing training speed over visual adaptation"],"limitations":["Frozen encoder cannot adapt to domain-specific visual features — limits performance on specialized images (medical, scientific, industrial)","CLIP ViT-L/14 has known limitations with small text, dense objects, and fine-grained visual details","No fine-tuning capability means visual understanding is capped at CLIP's pre-training","Projection matrix adds ~50-100ms latency per inference (estimated)"],"requires":["CLIP model weights (ViT-L/14 variant)","PyTorch or compatible framework","GPU with sufficient VRAM for CLIP encoder (~4-8 GB estimated)"],"input_types":["image (JPEG, PNG, WebP)"],"output_types":["image embeddings (projected into language model token space)"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-1-6__cap_8","uri":"capability://text.generation.language.vicuna.language.model.backbone.integration","name":"vicuna-language-model-backbone-integration","description":"Integrates Vicuna (an open-source language model) as the text generation backbone, receiving projected visual embeddings as additional tokens in the input sequence. The language model generates text responses by attending to both visual embeddings and text tokens, enabling unified multimodal reasoning within a single transformer architecture.","intents":["I want to use an open-source language model for vision-language tasks without proprietary APIs","I need to understand how to integrate visual embeddings into language model token sequences","I want to build multimodal systems with full control over the language model component"],"best_for":["open-source advocates building fully-open multimodal systems","teams requiring full model control and customization","researchers studying language model behavior in multimodal settings"],"limitations":["Vicuna is smaller than GPT-4, explaining 85.1% relative performance gap on synthetic benchmarks","Vicuna's context window is limited (likely 2K tokens); constrains image description length and conversation history","No explicit documentation on Vicuna version used (7B, 13B, 33B parameters unknown)","Vicuna's training data and alignment properties may differ from commercial LLMs, affecting response quality and safety"],"requires":["Vicuna model weights (version unspecified)","PyTorch or compatible framework","GPU with sufficient VRAM for language model inference (8-16 GB estimated for 7B-13B variants)"],"input_types":["visual embeddings (from CLIP encoder)","text tokens (instructions or questions)"],"output_types":["text (generated response)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-1-6__cap_9","uri":"capability://memory.knowledge.projection.matrix.vision.language.alignment","name":"projection-matrix-vision-language-alignment","description":"Learns a projection matrix that maps CLIP visual embeddings (dimensionality ~768 for ViT-L/14) into Vicuna's token embedding space, enabling visual information to be processed as additional tokens in the language model's sequence. This learned alignment layer is trained end-to-end during instruction tuning, allowing the language model to seamlessly integrate visual and textual information.","intents":["I need to align visual embeddings with language model token spaces","I want to understand how to connect pre-trained vision and language models","I need a lightweight fusion mechanism that doesn't add significant latency"],"best_for":["researchers studying vision-language alignment mechanisms","teams building efficient multimodal systems with minimal architectural complexity","developers integrating pre-trained models without custom fusion layers"],"limitations":["Simple linear projection may lose information during dimensionality reduction","No learned cross-attention or complex fusion — limits fine-grained vision-language interaction","Projection matrix parameters are small (~1-5M) compared to full model, limiting expressiveness","No documented ablation studies on projection matrix design choices"],"requires":["CLIP embedding dimension (768 for ViT-L/14)","Vicuna token embedding dimension (unknown, likely 4096 or similar)","PyTorch or compatible framework"],"input_types":["CLIP visual embeddings (768-dimensional vectors)"],"output_types":["projected embeddings (Vicuna token embedding space dimensionality)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llava-1-6__headline","uri":"capability://image.visual.multimodal.language.and.vision.assistant","name":"multimodal language and vision assistant","description":"LLaVA 1.6 is a powerful multimodal model that combines visual and language understanding, excelling in visual question answering and instruction-following tasks, making it ideal for developers seeking advanced AI solutions for integrating visual and textual data.","intents":["best multimodal AI model","multimodal model for visual question answering","AI assistant for language and vision tasks","top models for instruction-following with images","best AI for visual and language integration"],"best_for":["developers","researchers"],"limitations":[],"requires":[],"input_types":["language-image pairs"],"output_types":["text responses"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Image input (JPEG, PNG, or other standard formats)","Text query/instruction in natural language","GPU with sufficient VRAM for model inference (exact requirements unknown)","Python environment with PyTorch or compatible inference framework","Image input (standard formats)","Text instruction/question in natural language","Sufficient GPU VRAM for model inference","Framework supporting multimodal input batching","8× A100 GPUs (or equivalent high-memory GPU cluster)","158K instruction-tuning dataset (or custom equivalent)"],"failure_modes":["Frozen CLIP vision encoder limits visual understanding to CLIP's pre-trained capabilities — cannot adapt to domain-specific visual features","Achieves 85.1% relative performance vs GPT-4 on synthetic benchmarks, indicating gaps in complex multimodal reasoning","Context window size unknown; likely limited by underlying Vicuna model","Single-image input only; no multi-image reasoning or temporal understanding","No explicit multi-image reasoning — each image is processed independently","Conversation history management and context window constraints unknown","Performance degrades on images with small or dense text (CLIP encoder limitation)","No real-time streaming of responses documented","Two-stage process details not documented — unclear what each stage optimizes or how they differ","No published ablation studies comparing one-stage vs two-stage training","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.327Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=llava-1-6","compare_url":"https://unfragile.ai/compare?artifact=llava-1-6"}},"signature":"2tkGUOwKtfN859gFxLLUXLRCejAloQ1pE7BKZqJChH04Cb7qA3pUixp38L0dmRBaFb5glvKnkuBnDZS4lDJICA==","signedAt":"2026-06-22T04:14:55.827Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llava-1-6","artifact":"https://unfragile.ai/llava-1-6","verify":"https://unfragile.ai/api/v1/verify?slug=llava-1-6","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}