{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-qwen--qwen3-vl-embedding-2b","slug":"qwen--qwen3-vl-embedding-2b","name":"Qwen3-VL-Embedding-2B","type":"model","url":"https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B","page_url":"https://unfragile.ai/qwen--qwen3-vl-embedding-2b","categories":["rag-knowledge"],"tags":["sentence-transformers","safetensors","qwen3_vl","image-text-to-text","transformers","multimodal embedding","qwen","embedding","sentence-similarity","arxiv:2601.04720","base_model:Qwen/Qwen3-VL-2B-Instruct","base_model:finetune:Qwen/Qwen3-VL-2B-Instruct","license:apache-2.0","endpoints_compatible","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-qwen--qwen3-vl-embedding-2b__cap_0","uri":"capability://data.processing.analysis.multimodal.image.text.embedding.generation","name":"multimodal image-text embedding generation","description":"Generates unified dense vector embeddings (2B parameter model) that encode both images and text into a shared semantic space, enabling direct similarity comparisons between visual and textual content. Uses a vision-language transformer architecture fine-tuned from Qwen3-VL-2B-Instruct base model with contrastive learning objectives to align image and text representations in a single embedding space.","intents":["I need to find images semantically similar to a text query without separate image and text encoders","I want to embed both images and text descriptions into comparable vectors for cross-modal retrieval","I need to measure semantic similarity between an image and multiple text captions to find the best match","I want to build a unified search index that handles both visual and textual queries"],"best_for":["teams building multimodal RAG systems with mixed image-text corpora","developers implementing cross-modal search without maintaining separate vision and language models","researchers prototyping vision-language applications with resource constraints (2B parameters vs 7B+ alternatives)"],"limitations":["2B parameter model trades inference speed for accuracy compared to larger vision-language models (7B+)","Embedding dimension and pooling strategy are fixed post-training — no dynamic adaptation to downstream task requirements","No built-in support for batch processing optimization or GPU memory management — requires external orchestration","Fine-tuned specifically for sentence-similarity tasks; may not generalize optimally to other multimodal tasks like VQA or captioning"],"requires":["Python 3.8+","transformers library 4.36+","torch 2.0+ or compatible deep learning framework","GPU with 4GB+ VRAM for inference (CPU inference possible but significantly slower)","HuggingFace Hub access or local model weights (~4GB disk space)"],"input_types":["image (PIL Image, numpy array, or file path)","text (string, list of strings)","mixed batches of images and text"],"output_types":["dense vector embeddings (float32, typically 768-1024 dimensions)","similarity scores (cosine similarity between embedding pairs)","structured similarity matrices for batch comparisons"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-vl-embedding-2b__cap_1","uri":"capability://data.processing.analysis.semantic.similarity.scoring.between.multimodal.pairs","name":"semantic similarity scoring between multimodal pairs","description":"Computes cosine similarity or other distance metrics between embeddings of image-text pairs to quantify semantic alignment. Operates on pre-computed or on-the-fly embeddings, supporting batch similarity matrix computation for ranking or clustering tasks. Leverages the shared embedding space to directly compare cross-modal content without additional alignment layers.","intents":["I need to score how well a text caption describes an image on a 0-1 scale","I want to rank multiple text descriptions by relevance to a given image","I need to identify duplicate or near-duplicate images in a dataset using semantic similarity","I want to cluster images and text together based on semantic meaning"],"best_for":["content moderation teams filtering image-text mismatches","e-commerce platforms matching product images to descriptions","researchers evaluating image captioning or visual question answering systems"],"limitations":["Similarity scores are relative, not absolute — threshold selection requires task-specific calibration","Cosine similarity in high-dimensional spaces can suffer from curse of dimensionality; may require normalization or dimensionality reduction for very large-scale comparisons","No built-in confidence intervals or uncertainty quantification — scores are point estimates","Performance degrades on out-of-distribution content not represented in fine-tuning data"],"requires":["Pre-computed embeddings from the multimodal embedding generation capability","numpy or torch for similarity computation","Optional: faiss or annoy for efficient similarity search at scale (>100K embeddings)"],"input_types":["embedding vectors (float32 arrays)","image-text pairs (for on-the-fly embedding + similarity)","batch queries (multiple images or texts to compare against a corpus)"],"output_types":["similarity scores (float, typically 0-1 range)","ranked lists of matches with scores","similarity matrices (NxM for N queries vs M corpus items)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-vl-embedding-2b__cap_2","uri":"capability://search.retrieval.image.to.text.retrieval.via.embedding.search","name":"image-to-text retrieval via embedding search","description":"Retrieves the most semantically relevant text descriptions or captions for a given image by embedding the image, then searching a pre-indexed corpus of text embeddings using approximate nearest neighbor (ANN) search or exhaustive similarity computation. Supports both dense vector search (faiss, annoy) and sparse indexing strategies for efficient retrieval at scale.","intents":["I have an image and need to find the best matching text description from a large database","I want to retrieve relevant product descriptions for images in an e-commerce catalog","I need to find similar images in a corpus by querying with a reference image's semantic content"],"best_for":["e-commerce platforms matching product images to descriptions","content discovery systems finding relevant articles for images","multimodal search engines supporting image-based queries"],"limitations":["Retrieval quality depends on corpus quality and diversity — garbage in, garbage out","ANN search introduces recall-precision tradeoffs; exact nearest neighbor search requires O(n) comparisons for n corpus items","No re-ranking or diversity mechanisms built-in — top-k results may be semantically redundant","Requires pre-indexing of corpus embeddings; dynamic corpus updates require re-indexing overhead"],"requires":["Pre-computed embeddings for all text items in the corpus","faiss, annoy, or similar ANN library for efficient search (optional for small corpora <10K items)","Image embedding capability from the multimodal embedding generation","Sufficient memory to hold corpus embeddings in RAM or on GPU"],"input_types":["image (PIL Image, numpy array, or file path)","indexed text corpus (pre-embedded and indexed)"],"output_types":["ranked list of text descriptions with similarity scores","top-k matches (configurable k)","structured results with metadata (e.g., document IDs, URLs)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-vl-embedding-2b__cap_3","uri":"capability://search.retrieval.text.to.image.retrieval.via.embedding.search","name":"text-to-image retrieval via embedding search","description":"Retrieves the most semantically relevant images for a given text query by embedding the text, then searching a pre-indexed corpus of image embeddings using approximate nearest neighbor search or exhaustive similarity computation. Mirrors the image-to-text capability but inverts the query-corpus relationship for text-driven image discovery.","intents":["I have a text description and need to find matching images from a large dataset","I want to search an image database using natural language queries","I need to find images that visually match a text-based product description"],"best_for":["visual search engines supporting natural language queries","content curation systems finding images for articles or descriptions","accessibility tools converting text queries to visual results"],"limitations":["Text-to-image retrieval is typically harder than image-to-text due to semantic gap between language and vision","Requires pre-indexing of all images in the corpus; dynamic image addition requires re-indexing","Query formulation matters significantly — verbose descriptions may perform better than short queries","No built-in handling of ambiguous or multi-faceted text queries"],"requires":["Pre-computed embeddings for all images in the corpus","faiss, annoy, or similar ANN library for efficient search","Text embedding capability from the multimodal embedding generation","Sufficient memory for corpus embeddings"],"input_types":["text query (string or list of strings)","indexed image corpus (pre-embedded and indexed)"],"output_types":["ranked list of images with similarity scores","top-k matches with metadata (image IDs, URLs, file paths)","similarity matrices for batch text queries"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-vl-embedding-2b__cap_4","uri":"capability://data.processing.analysis.batch.multimodal.embedding.computation.with.batching.optimization","name":"batch multimodal embedding computation with batching optimization","description":"Processes multiple images and texts in batches to generate embeddings efficiently, leveraging GPU parallelization and memory pooling to reduce per-sample overhead. Supports mixed batches (images and text together) and implements dynamic batching strategies to maximize throughput while respecting memory constraints. Uses transformer attention mechanisms with vision patch tokenization for images and subword tokenization for text.","intents":["I need to embed a large dataset of images and captions efficiently for indexing","I want to process thousands of image-text pairs in parallel to build a search index","I need to generate embeddings for a corpus while managing GPU memory constraints"],"best_for":["data engineers building large-scale multimodal search indices","ML teams pre-computing embeddings for production retrieval systems","researchers processing large vision-language datasets"],"limitations":["Batch size is constrained by GPU memory; typical batch sizes 8-64 depending on GPU (A100 vs RTX 4090)","Mixed image-text batches require padding to common sequence lengths, adding computational overhead","No built-in distributed batching across multiple GPUs — requires external orchestration (torch.nn.DataParallel or torch.distributed)","Embedding computation is deterministic but sensitive to floating-point precision; fp16 vs fp32 can produce slightly different results"],"requires":["GPU with 4GB+ VRAM (8GB+ recommended for batch size >16)","torch with CUDA support or compatible accelerator (Metal for Apple Silicon)","transformers library with vision support","PIL or similar image loading library"],"input_types":["batch of images (list of PIL Images or numpy arrays)","batch of text strings","mixed batches of images and text"],"output_types":["batch of embedding vectors (shape: [batch_size, embedding_dim])","optional: attention weights or intermediate layer activations for interpretability"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-vl-embedding-2b__cap_5","uri":"capability://data.processing.analysis.fine.tuning.and.domain.adaptation.for.specialized.similarity.tasks","name":"fine-tuning and domain adaptation for specialized similarity tasks","description":"Enables further fine-tuning of the pre-trained 2B model on domain-specific image-text pairs using contrastive loss functions (e.g., InfoNCE, triplet loss) to adapt embeddings for specialized similarity tasks. Supports parameter-efficient fine-tuning approaches (LoRA, adapter layers) to reduce computational cost while maintaining performance. Leverages the Qwen3-VL-2B-Instruct base architecture with frozen vision encoder and trainable text/alignment layers.","intents":["I want to adapt the model to my domain (medical images, fashion, real estate) without training from scratch","I need to improve similarity matching for a specific use case with limited labeled data","I want to use parameter-efficient fine-tuning to reduce training cost and memory usage"],"best_for":["teams with domain-specific image-text datasets (medical, legal, e-commerce)","researchers exploring transfer learning for multimodal tasks","practitioners with limited compute budgets seeking to adapt pre-trained models"],"limitations":["Fine-tuning requires labeled image-text pairs; quality and quantity of training data directly impact performance","Catastrophic forgetting risk — aggressive fine-tuning can degrade performance on general-domain tasks","No built-in curriculum learning or hard negative mining — requires manual implementation for optimal convergence","LoRA/adapter fine-tuning adds inference latency (~5-10%) due to additional forward passes through adapter modules","Hyperparameter tuning (learning rate, batch size, contrastive temperature) is task-dependent and requires experimentation"],"requires":["Python 3.8+","torch 2.0+ with autograd support","transformers library with fine-tuning utilities","GPU with 8GB+ VRAM for full fine-tuning, 4GB+ for LoRA","Labeled dataset of image-text pairs (minimum 1K pairs recommended)"],"input_types":["image-text pair dataset (CSV, JSON, or custom DataLoader)","optional: hard negative examples for contrastive learning","hyperparameter configuration (learning rate, batch size, loss function)"],"output_types":["fine-tuned model weights (safetensors or PyTorch format)","training logs with loss curves and validation metrics","optional: LoRA adapters for parameter-efficient storage"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-vl-embedding-2b__cap_6","uri":"capability://text.generation.language.sentence.level.semantic.similarity.evaluation","name":"sentence-level semantic similarity evaluation","description":"Evaluates semantic similarity between pairs of sentences (text-only) by embedding them and computing cosine similarity, supporting both direct similarity scoring and ranking of candidate sentences by relevance to a query. Operates on the text encoding component of the multimodal model, which is fine-tuned specifically for sentence-similarity tasks. Useful for NLU tasks like paraphrase detection, semantic textual similarity (STS), and query-document matching.","intents":["I need to detect if two sentences are paraphrases or semantically equivalent","I want to rank candidate answers by relevance to a user query","I need to measure semantic similarity between search queries and indexed documents"],"best_for":["NLP teams building semantic search or question-answering systems","content moderation systems detecting duplicate or similar text","researchers evaluating semantic textual similarity benchmarks"],"limitations":["Text-only similarity may miss visual context important for multimodal understanding","Similarity scores are language-dependent; cross-lingual similarity requires separate evaluation","No built-in handling of long documents — model is optimized for sentence-length inputs (typically <512 tokens)","Similarity is symmetric (sim(A,B) = sim(B,A)) — asymmetric tasks (query-document ranking) may require task-specific fine-tuning"],"requires":["Text input (strings or list of strings)","transformers library with tokenization support","Optional: numpy or torch for similarity computation"],"input_types":["sentence pairs (tuple of two strings)","query and candidate sentences (for ranking)","batch of sentences (for pairwise similarity matrix)"],"output_types":["similarity scores (float, typically 0-1)","ranked lists of sentences with scores","pairwise similarity matrices"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-vl-embedding-2b__cap_7","uri":"capability://text.generation.language.cross.lingual.semantic.similarity.implicit.via.multilingual.training","name":"cross-lingual semantic similarity (implicit via multilingual training)","description":"Supports semantic similarity computation across languages through implicit multilingual alignment learned during pre-training on Qwen3-VL-2B-Instruct, which is trained on multilingual data. Enables querying in one language and retrieving results in another without explicit translation, though performance varies by language pair and language representation in training data.","intents":["I need to find images matching text queries in different languages","I want to build a multilingual search system without separate language-specific models","I need to measure semantic similarity between text in different languages"],"best_for":["global platforms supporting multiple languages","multilingual content discovery systems","researchers studying cross-lingual transfer in vision-language models"],"limitations":["Cross-lingual performance is not explicitly optimized — relies on implicit alignment from base model pre-training","Performance varies significantly by language pair; high-resource languages (English, Chinese) perform better than low-resource languages","No explicit language identification or handling — model assumes input language is consistent with training distribution","Mixing languages in a single query may produce unpredictable results","No built-in translation or language-specific fine-tuning — requires external tools for optimal cross-lingual performance"],"requires":["Text input in languages supported by Qwen3-VL-2B-Instruct (primarily Chinese, English, and other high-resource languages)","Optional: language detection library to validate input language"],"input_types":["text in supported languages","image-text pairs with text in different languages"],"output_types":["similarity scores between cross-lingual text pairs","ranked results in target language"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","transformers library 4.36+","torch 2.0+ or compatible deep learning framework","GPU with 4GB+ VRAM for inference (CPU inference possible but significantly slower)","HuggingFace Hub access or local model weights (~4GB disk space)","Pre-computed embeddings from the multimodal embedding generation capability","numpy or torch for similarity computation","Optional: faiss or annoy for efficient similarity search at scale (>100K embeddings)","Pre-computed embeddings for all text items in the corpus","faiss, annoy, or similar ANN library for efficient search (optional for small corpora <10K items)"],"failure_modes":["2B parameter model trades inference speed for accuracy compared to larger vision-language models (7B+)","Embedding dimension and pooling strategy are fixed post-training — no dynamic adaptation to downstream task requirements","No built-in support for batch processing optimization or GPU memory management — requires external orchestration","Fine-tuned specifically for sentence-similarity tasks; may not generalize optimally to other multimodal tasks like VQA or captioning","Similarity scores are relative, not absolute — threshold selection requires task-specific calibration","Cosine similarity in high-dimensional spaces can suffer from curse of dimensionality; may require normalization or dimensionality reduction for very large-scale comparisons","No built-in confidence intervals or uncertainty quantification — scores are point estimates","Performance degrades on out-of-distribution content not represented in fine-tuning data","Retrieval quality depends on corpus quality and diversity — garbage in, garbage out","ANN search introduces recall-precision tradeoffs; exact nearest neighbor search requires O(n) comparisons for n corpus items","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7920509185589869,"quality":0.26,"ecosystem":0.5000000000000001,"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":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:56.943Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":2278525,"model_likes":394}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=qwen--qwen3-vl-embedding-2b","compare_url":"https://unfragile.ai/compare?artifact=qwen--qwen3-vl-embedding-2b"}},"signature":"QpCSkgx/T/h63AN4bz2JWsXsHsQopcIc2p57Cd2kjJK9JoF0hv6zbg4f2bf7Fmeu5XKBiBTe9JxNhdlOiN3PCw==","signedAt":"2026-06-20T21:41:22.201Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen--qwen3-vl-embedding-2b","artifact":"https://unfragile.ai/qwen--qwen3-vl-embedding-2b","verify":"https://unfragile.ai/api/v1/verify?slug=qwen--qwen3-vl-embedding-2b","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"}}