{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-sharegpt4omni--sharegpt4video","slug":"sharegpt4omni--sharegpt4video","name":"ShareGPT4Video","type":"repo","url":"https://sharegpt4video.github.io/","page_url":"https://unfragile.ai/sharegpt4omni--sharegpt4video","categories":["video-generation"],"tags":["chatgpt","gpt","gpt-4v","large-language-models","large-multimodal-models","large-video-language-models","large-vision-language-models","sora","text-to-video"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-sharegpt4omni--sharegpt4video__cap_0","uri":"capability://image.visual.video.to.natural.language.understanding.via.llava.based.multimodal.encoding","name":"video-to-natural-language understanding via llava-based multimodal encoding","description":"ShareGPT4Video-8B processes video inputs through a LLaVA framework architecture that encodes video frames into a shared vision-language embedding space, enabling the 8B parameter model to answer arbitrary questions about video content and generate detailed descriptions. The model samples frames from input videos (supporting variable durations and aspect ratios), encodes them through a vision encoder, and fuses the visual embeddings with language model tokens to enable conversational understanding without requiring external APIs.","intents":["I need to ask questions about video content and get detailed answers without sending data to external APIs","I want to build a video understanding system that runs locally with moderate compute requirements","I need to understand what's happening in a video at specific moments or across the entire duration"],"best_for":["Teams building privacy-sensitive video analysis pipelines","Developers deploying video understanding on resource-constrained infrastructure","Researchers fine-tuning video-language models on domain-specific data"],"limitations":["8B parameter model trades off accuracy vs. larger models like GPT-4V; performance degrades on complex reasoning tasks requiring world knowledge","Frame sampling strategy may miss fast-motion events or brief visual details depending on sampling interval configuration","Requires 8×A100 GPUs for training (5 hours); inference latency not specified but likely 1-5 seconds per video depending on length and hardware","No built-in support for audio understanding; video understanding is purely visual"],"requires":["Python 3.8+","PyTorch 1.13+","CUDA 11.8+ for GPU inference (CPU inference possible but slow)","Video file in common format (MP4, AVI, MOV, etc.)","Minimum 8GB VRAM for inference; 80GB+ for training"],"input_types":["video files (MP4, AVI, MOV, WebM)","natural language queries (text)","video duration and resolution (variable support)"],"output_types":["natural language text responses","detailed video descriptions","structured answers to specific questions about video content"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sharegpt4omni--sharegpt4video__cap_1","uri":"capability://image.visual.fast.frame.sampling.video.captioning.with.fixed.interval.extraction","name":"fast frame-sampling video captioning with fixed-interval extraction","description":"ShareCaptioner-Video implements a 'Fast Captioning' mode that samples a fixed number of frames uniformly across the video timeline, encodes each frame independently, and generates captions optimized for speed rather than comprehensiveness. This mode trades caption detail for inference speed by avoiding redundant processing of similar consecutive frames, making it suitable for batch processing large video collections.","intents":["I need to caption thousands of videos quickly for a content management system","I want to generate basic descriptive captions for video search indexing without high latency","I need to process video batches on limited compute and accept lower caption quality for speed"],"best_for":["Content platforms processing high-volume video uploads","Batch video processing pipelines with latency budgets under 1 second per video","Teams prioritizing throughput over caption comprehensiveness"],"limitations":["Fixed frame sampling may miss important visual transitions or events occurring between sampled frames","Generated captions are less detailed than Slide Captioning mode; may lack temporal context about event sequences","No adaptive sampling based on scene changes; uniform sampling can be inefficient for static-content videos"],"requires":["Python 3.8+","PyTorch 1.13+","Video file in standard format","Minimum 4GB VRAM for inference"],"input_types":["video files (MP4, AVI, MOV, WebM)","frame count parameter (configurable sampling density)"],"output_types":["text captions (single or per-frame)","caption metadata (frame indices, timestamps)"],"categories":["image-visual","text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sharegpt4omni--sharegpt4video__cap_10","uri":"capability://tool.use.integration.model.integration.with.external.video.generation.systems.sora.etc","name":"model integration with external video generation systems (sora, etc.)","description":"ShareGPT4Video is designed as a caption generation component that can feed high-quality video descriptions into text-to-video generation models like Sora. The system outputs structured captions that serve as semantic conditioning signals for video generation, improving the quality and coherence of generated videos by providing richer textual descriptions than user prompts alone.","intents":["I want to improve text-to-video generation quality by providing better semantic descriptions","I need to generate training data for video generation models with high-quality captions","I want to create a pipeline that generates videos from existing videos with improved descriptions"],"best_for":["Teams building video generation systems that require semantic conditioning","Content creators generating training data for video models","Researchers studying the impact of caption quality on video generation"],"limitations":["Integration with specific video generation systems (Sora) not documented; requires custom implementation","No standardized interface for caption output; each video generation system may require different caption formats","Caption quality directly impacts generation quality; no guidance on optimal caption length or detail level","No feedback loop from video generation to caption refinement; captions are generated independently"],"requires":["Python 3.8+","PyTorch 1.13+","Video generation system API or local model","Integration code to format captions for target system","Minimum 8GB VRAM"],"input_types":["video files (MP4, AVI, MOV, WebM)","optional target video generation system specification"],"output_types":["captions formatted for video generation system","structured metadata (video ID, caption, confidence scores)","optional JSON for downstream processing"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sharegpt4omni--sharegpt4video__cap_11","uri":"capability://tool.use.integration.hugging.face.model.hub.integration.with.automatic.weight.download","name":"hugging face model hub integration with automatic weight download","description":"ShareGPT4Video integrates with Hugging Face's model hub, automatically downloading pre-trained weights (Lin-Chen/sharegpt4video-8b) on first use without manual configuration. The integration handles model caching, version management, and device-specific loading, enabling users to start using the model with a single command without managing weights manually.","intents":["I want to use the pre-trained model without manually downloading and managing weights","I need to ensure I'm using the latest model version without manual updates","I want to integrate the model into my application with minimal setup code"],"best_for":["Developers building applications that require minimal setup overhead","Teams deploying models in containerized environments with automatic weight management","Researchers experimenting with pre-trained models without infrastructure setup"],"limitations":["Initial download requires internet connectivity and sufficient disk space (~16GB for 8B model weights)","No built-in version pinning; automatic updates may introduce breaking changes","Hugging Face API rate limits may cause download failures for large-scale deployments","No local mirror or offline mode; requires external connectivity for first-time setup"],"requires":["Python 3.8+","PyTorch 1.13+","Internet connectivity for initial weight download","Disk space for model weights (~16GB)","Hugging Face transformers library 4.30+"],"input_types":["model identifier (string, defaults to Lin-Chen/sharegpt4video-8b)","optional device specification (cuda, cpu, mps)"],"output_types":["loaded model ready for inference","model configuration and metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sharegpt4omni--sharegpt4video__cap_2","uri":"capability://image.visual.slide.window.video.captioning.with.temporal.context.preservation","name":"slide-window video captioning with temporal context preservation","description":"ShareCaptioner-Video's 'Slide Captioning' mode processes videos using a sliding window of frames with fixed sampling intervals, enabling the model to capture temporal context and event sequences within each window. This approach generates higher-quality, more contextually-aware captions by processing frame groups rather than individual frames, at the cost of increased computational overhead compared to Fast Captioning.","intents":["I need detailed, temporally-aware captions that describe event sequences and transitions in videos","I want to improve text-to-video generation by providing richer semantic descriptions of video content","I need captions that capture the narrative flow and temporal relationships in video content"],"best_for":["Video generation systems (e.g., Sora) requiring high-quality semantic descriptions","Content creation platforms where caption quality directly impacts user experience","Research teams studying video-language alignment and temporal understanding"],"limitations":["Slide window processing increases inference latency by 2-4× compared to Fast Captioning due to overlapping frame processing","Window size and stride are fixed hyperparameters; no adaptive adjustment for video content characteristics","Memory overhead scales with window size; large windows may exceed VRAM on resource-constrained devices","Temporal context is limited to window size; events spanning longer durations may not be fully captured"],"requires":["Python 3.8+","PyTorch 1.13+","Video file in standard format","Minimum 8GB VRAM for inference (16GB+ recommended for large windows)","Configurable window size and stride parameters"],"input_types":["video files (MP4, AVI, MOV, WebM)","window size parameter (frames per window)","stride parameter (frame interval between windows)"],"output_types":["detailed text captions with temporal context","per-window caption metadata","structured caption sequences preserving temporal order"],"categories":["image-visual","text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sharegpt4omni--sharegpt4video__cap_3","uri":"capability://text.generation.language.prompt.guided.video.re.captioning.with.custom.instruction.injection","name":"prompt-guided video re-captioning with custom instruction injection","description":"ShareCaptioner-Video supports 'Prompt Re-Captioning' mode where users provide custom prompts or instructions to guide caption generation, enabling fine-grained control over caption style, detail level, and focus areas. This capability injects user prompts into the model's input context, allowing domain-specific or task-specific caption customization without model retraining.","intents":["I need captions focused on specific aspects of videos (e.g., technical details, narrative elements, visual composition)","I want to generate captions in a particular style or format for downstream applications","I need to adapt captions for different audiences or use cases without retraining the model"],"best_for":["Content creators customizing captions for specific platforms or audiences","Domain-specific video analysis (medical, technical, educational) requiring specialized caption focus","Teams building caption generation pipelines with variable output requirements"],"limitations":["Prompt engineering required; caption quality depends on prompt clarity and specificity","Model may not fully respect complex or contradictory instructions; behavior is probabilistic","No validation that generated captions actually follow the provided prompt; requires post-processing verification","Inference latency increases slightly due to longer input context from prompt injection"],"requires":["Python 3.8+","PyTorch 1.13+","Video file in standard format","Custom prompt text (natural language instructions)","Minimum 4GB VRAM for inference"],"input_types":["video files (MP4, AVI, MOV, WebM)","natural language prompts (text instructions)","optional captioning mode selection (fast or slide)"],"output_types":["customized text captions","captions styled according to prompt specifications"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sharegpt4omni--sharegpt4video__cap_4","uri":"capability://automation.workflow.batch.video.captioning.with.parallel.processing.and.result.aggregation","name":"batch video captioning with parallel processing and result aggregation","description":"ShareCaptioner-Video implements batch inference capabilities that process multiple videos in parallel, managing GPU memory allocation and result aggregation to maximize throughput. The system queues videos, distributes them across available compute resources, and collects captions with metadata (video ID, timestamps, caption text) for downstream consumption.","intents":["I need to caption hundreds or thousands of videos efficiently without manual per-video processing","I want to maximize GPU utilization by processing multiple videos concurrently","I need structured output with video-to-caption mappings for database ingestion"],"best_for":["Content platforms with continuous video upload streams","Batch processing pipelines running on scheduled intervals","Teams with multi-GPU infrastructure seeking to maximize hardware utilization"],"limitations":["Batch size is limited by available VRAM; exceeding limits causes out-of-memory errors or requires dynamic batching with performance overhead","No built-in retry logic for failed videos; requires external error handling and re-queuing","Result ordering may not match input order depending on processing speed variations; requires explicit video ID tracking","No progress monitoring or checkpointing; interrupting batch processing loses all intermediate results"],"requires":["Python 3.8+","PyTorch 1.13+","Video files in standard formats","Batch configuration (batch size, queue size)","Minimum 8GB VRAM; scales with batch size (16GB+ for large batches)"],"input_types":["list of video file paths","batch size parameter","optional captioning mode selection"],"output_types":["structured caption results (video ID, caption text, metadata)","optional JSON or CSV export format","caption statistics (processing time, success rate)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sharegpt4omni--sharegpt4video__cap_5","uri":"capability://tool.use.integration.command.line.interface.for.single.video.understanding.and.captioning","name":"command-line interface for single-video understanding and captioning","description":"ShareGPT4Video provides a CLI entry point (run.py) that accepts video file paths and natural language queries, executing the full pipeline from video loading through model inference to text output. The CLI supports model selection, device configuration, and output formatting, enabling developers to integrate video understanding into shell scripts and automation workflows without writing Python code.","intents":["I want to quickly test video understanding on a single video from the command line","I need to integrate video analysis into shell scripts or CI/CD pipelines","I want to experiment with different queries on the same video without writing code"],"best_for":["Developers prototyping video understanding features","DevOps engineers integrating video processing into automation workflows","Researchers experimenting with model behavior on specific videos"],"limitations":["CLI interface limited to single-video processing; batch operations require Python scripting","No streaming output; entire inference must complete before results are displayed","Limited error reporting; failures may not provide actionable debugging information","No progress indication for long-running inferences; user has no visibility into processing status"],"requires":["Python 3.8+","PyTorch 1.13+","CUDA 11.8+ for GPU inference","Video file accessible from command line","Model weights downloaded (auto-downloads from Hugging Face on first run)"],"input_types":["video file path (--video argument)","natural language query (--query argument)","optional model path (--model-path, defaults to Lin-Chen/sharegpt4video-8b)"],"output_types":["text response printed to stdout","optional JSON output format"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sharegpt4omni--sharegpt4video__cap_6","uri":"capability://tool.use.integration.web.interface.for.interactive.video.understanding.and.captioning","name":"web interface for interactive video understanding and captioning","description":"ShareGPT4Video includes web UI applications (app.py in root and captioner directories) built on a web framework that provide interactive interfaces for video upload, query input, and result display. The web interface manages file uploads, queues inference requests, and streams results back to the browser, enabling non-technical users to interact with the models without command-line knowledge.","intents":["I want to provide a user-friendly interface for video understanding without building a custom UI","I need to share video analysis capabilities with non-technical team members","I want to demo video understanding capabilities to stakeholders or customers"],"best_for":["Teams building internal tools for video analysis","Researchers demonstrating model capabilities","Content creators needing accessible video captioning tools"],"limitations":["Web UI framework and performance characteristics not specified in available documentation; likely uses Flask or Gradio with inherent latency","No built-in authentication or access control; requires external reverse proxy for production deployment","File upload size limits depend on web server configuration; large videos may timeout or fail","Single-instance deployment; no horizontal scaling or load balancing built-in","No persistent storage of results; captions are lost if browser session ends"],"requires":["Python 3.8+","PyTorch 1.13+","Web framework dependencies (Flask/Gradio/etc.)","CUDA 11.8+ for GPU inference","Port availability (typically 7860 for Gradio or 5000 for Flask)","Minimum 8GB VRAM"],"input_types":["video file upload (multipart form)","natural language query (text input)","optional captioning mode selection"],"output_types":["HTML-rendered text responses","downloadable caption files (optional)","video preview with captions overlay (optional)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sharegpt4omni--sharegpt4video__cap_7","uri":"capability://memory.knowledge.multi.modal.embedding.fusion.for.vision.language.alignment","name":"multi-modal embedding fusion for vision-language alignment","description":"ShareGPT4Video-8B uses the LLaVA framework's vision-language fusion architecture, which encodes video frames through a vision encoder and projects them into the language model's embedding space, enabling seamless integration of visual information with text generation. This fusion happens at the token level, allowing the language model to attend to visual features while generating text responses.","intents":["I need to understand how visual information is integrated with language generation in multimodal models","I want to fine-tune the model on domain-specific video-language pairs","I need to extract or analyze the learned vision-language embeddings for downstream tasks"],"best_for":["Researchers studying vision-language alignment and multimodal learning","Teams fine-tuning the model on specialized video-language datasets","Developers building custom applications that require access to intermediate embeddings"],"limitations":["Embedding fusion is fixed at model architecture level; cannot be modified without retraining","Vision encoder is frozen during inference; cannot adapt to domain-specific visual features without full model retraining","Embedding dimensionality and projection strategy not specified; limits ability to integrate with external systems","No API for extracting intermediate embeddings; requires model source code modification"],"requires":["Python 3.8+","PyTorch 1.13+","Understanding of LLaVA architecture and vision-language fusion","Access to model source code for embedding extraction","Minimum 8GB VRAM for inference"],"input_types":["video frames (tensor format)","text tokens (from language model tokenizer)"],"output_types":["fused embeddings (vision + language tokens)","attention weights (if extracted)","generated text tokens"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sharegpt4omni--sharegpt4video__cap_8","uri":"capability://automation.workflow.dataset.driven.model.training.with.gpt.4.vision.generated.captions","name":"dataset-driven model training with gpt-4 vision-generated captions","description":"ShareGPT4Video provides training infrastructure to fine-tune models on the included dataset of 40K GPT-4 Vision-generated captions and 400K implicit video split captions. The training pipeline handles data loading, caption-video alignment, loss computation, and model checkpointing, enabling researchers to adapt the model to new domains or improve performance on specific video understanding tasks.","intents":["I want to fine-tune the model on domain-specific videos (medical, technical, etc.)","I need to improve model performance on specific video understanding tasks","I want to understand how caption quality impacts model performance"],"best_for":["Research teams studying video-language model training","Organizations with proprietary video datasets seeking to improve model performance","Teams building specialized video understanding systems for specific domains"],"limitations":["Training requires 8×A100 GPUs for 5 hours; prohibitively expensive for most organizations without access to large compute clusters","No distributed training support documented; scaling to larger datasets or longer training may require custom implementation","Dataset composition (40K GPT-4 captions + 400K split captions) is fixed; no guidance on optimal data mixing ratios","No curriculum learning or hard example mining strategies documented; training may be inefficient for large custom datasets","Hyperparameter tuning guidance not provided; requires experimentation to adapt to new domains"],"requires":["Python 3.8+","PyTorch 1.13+","CUDA 11.8+","8×A100 GPUs (80GB VRAM each)","Video dataset with aligned captions","Training script and configuration files from repository"],"input_types":["video files (MP4, AVI, MOV, WebM)","caption text files (one per video or batch format)","training configuration (YAML or JSON)"],"output_types":["fine-tuned model weights (PyTorch checkpoints)","training logs (loss curves, metrics)","evaluation results on validation set"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sharegpt4omni--sharegpt4video__cap_9","uri":"capability://data.processing.analysis.evaluation.metrics.and.benchmarking.for.video.understanding.quality","name":"evaluation metrics and benchmarking for video understanding quality","description":"ShareGPT4Video includes evaluation infrastructure to measure video understanding quality using standard metrics (BLEU, METEOR, CIDEr, SPICE for captioning; accuracy/F1 for QA tasks). The evaluation pipeline compares model outputs against reference captions or answers, aggregates metrics across test sets, and generates performance reports for model comparison and ablation studies.","intents":["I need to measure how well my fine-tuned model performs on video understanding tasks","I want to compare different model variants or training approaches objectively","I need to validate that model improvements on one dataset don't degrade performance on others"],"best_for":["Researchers conducting model ablation studies and performance analysis","Teams validating model improvements before production deployment","Organizations benchmarking video understanding systems against competitors"],"limitations":["Evaluation metrics are designed for caption quality; no metrics specified for video QA or other understanding tasks","Metrics like BLEU and METEOR are reference-based; require ground-truth captions which may not exist for all videos","No human evaluation framework; automated metrics may not correlate with human perception of caption quality","Evaluation infrastructure not fully documented; requires source code inspection to understand metric computation"],"requires":["Python 3.8+","PyTorch 1.13+","Reference captions or answers for test videos","Evaluation script from repository","Model outputs in compatible format"],"input_types":["model-generated captions (text)","reference captions (text)","optional metadata (video IDs, timestamps)"],"output_types":["metric scores (BLEU, METEOR, CIDEr, SPICE)","per-video metric breakdowns","aggregate statistics and comparisons","evaluation report (JSON or CSV)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.13+","CUDA 11.8+ for GPU inference (CPU inference possible but slow)","Video file in common format (MP4, AVI, MOV, etc.)","Minimum 8GB VRAM for inference; 80GB+ for training","Video file in standard format","Minimum 4GB VRAM for inference","Video generation system API or local model","Integration code to format captions for target system","Minimum 8GB VRAM"],"failure_modes":["8B parameter model trades off accuracy vs. larger models like GPT-4V; performance degrades on complex reasoning tasks requiring world knowledge","Frame sampling strategy may miss fast-motion events or brief visual details depending on sampling interval configuration","Requires 8×A100 GPUs for training (5 hours); inference latency not specified but likely 1-5 seconds per video depending on length and hardware","No built-in support for audio understanding; video understanding is purely visual","Fixed frame sampling may miss important visual transitions or events occurring between sampled frames","Generated captions are less detailed than Slide Captioning mode; may lack temporal context about event sequences","No adaptive sampling based on scene changes; uniform sampling can be inefficient for static-content videos","Integration with specific video generation systems (Sora) not documented; requires custom implementation","No standardized interface for caption output; each video generation system may require different caption formats","Caption quality directly impacts generation quality; no guidance on optimal caption length or detail level","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4033571119781707,"quality":0.49,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"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":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:59:47.981Z","last_commit":"2024-10-09T20:36:13Z"},"community":{"stars":1093,"forks":45,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=sharegpt4omni--sharegpt4video","compare_url":"https://unfragile.ai/compare?artifact=sharegpt4omni--sharegpt4video"}},"signature":"DkK70xbRX7hY68YvMMpttkoYNjrFTn5rJNsR+QvlzWlf4nNYPHs6xXBAuCOaJH0RFaR5nBKaTInBrG+NUhtTAA==","signedAt":"2026-06-20T08:36:41.580Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sharegpt4omni--sharegpt4video","artifact":"https://unfragile.ai/sharegpt4omni--sharegpt4video","verify":"https://unfragile.ai/api/v1/verify?slug=sharegpt4omni--sharegpt4video","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"}}