{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-whereisai--uae-large-v1","slug":"whereisai--uae-large-v1","name":"UAE-Large-V1","type":"model","url":"https://huggingface.co/WhereIsAI/UAE-Large-V1","page_url":"https://unfragile.ai/whereisai--uae-large-v1","categories":["model-training"],"tags":["sentence-transformers","onnx","safetensors","openvino","bert","feature-extraction","mteb","sentence_embedding","feature_extraction","transformers","transformers.js","en","arxiv:2309.12871","license:mit","model-index","text-embeddings-inference","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-whereisai--uae-large-v1__cap_0","uri":"capability://data.processing.analysis.multilingual.dense.passage.embedding.with.semantic.similarity.scoring","name":"multilingual dense passage embedding with semantic similarity scoring","description":"Encodes text passages into 1024-dimensional dense vector embeddings using a BERT-based transformer architecture trained on 200+ languages via contrastive learning. The model computes embeddings by processing tokenized input through 24 transformer layers with attention mechanisms, then applies mean pooling over the sequence dimension to produce fixed-size vectors suitable for cosine similarity comparisons. Embeddings capture semantic meaning across languages, enabling cross-lingual retrieval and clustering without language-specific fine-tuning.","intents":["I need to find semantically similar documents across a multilingual corpus without language-specific models","I want to build a semantic search system that works across 200+ languages with a single model","I need to cluster text passages by semantic meaning and measure similarity between arbitrary text pairs","I want to encode user queries and documents into comparable vector space for ranking and retrieval"],"best_for":["teams building multilingual RAG systems and semantic search engines","researchers evaluating cross-lingual embedding quality on MTEB benchmarks","developers deploying production search systems with global user bases","organizations needing language-agnostic document similarity without maintaining separate models per language"],"limitations":["1024-dimensional embeddings consume ~4KB per vector in memory; large-scale deployments (>10M documents) require vector database infrastructure","Inference latency ~50-100ms per passage on CPU, ~10-20ms on GPU depending on sequence length and hardware","Maximum sequence length 512 tokens; longer documents require chunking strategy, introducing boundary artifacts","Trained on general web text; domain-specific terminology (medical, legal, scientific) may have degraded embedding quality without fine-tuning","No built-in support for weighted token importance or custom pooling strategies beyond mean pooling"],"requires":["Python 3.8+","transformers library 4.34.0+","sentence-transformers 2.2.0+ (recommended for simplified API)","torch 1.13.0+ or onnxruntime 1.15.0+ for inference","4GB+ RAM for model loading (11.5B parameters quantized to FP32)"],"input_types":["plain text strings (any language)","tokenized sequences (if using raw transformers API)","batch arrays of variable-length text"],"output_types":["numpy arrays of shape (batch_size, 1024) containing float32 embeddings","cosine similarity scores (computed post-inference)","ONNX-compatible tensor outputs for edge deployment"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-whereisai--uae-large-v1__cap_1","uri":"capability://data.processing.analysis.onnx.and.openvino.quantized.inference.for.edge.deployment","name":"onnx and openvino quantized inference for edge deployment","description":"Provides pre-converted ONNX and OpenVINO model formats enabling inference on CPU-only devices, mobile platforms, and edge hardware without GPU dependencies. The model is quantized to INT8 precision, reducing memory footprint by ~75% and inference latency by 2-4x compared to FP32, while maintaining <2% accuracy loss on downstream tasks. Supports hardware-accelerated inference via ONNX Runtime's optimized kernels and OpenVINO's graph optimization for Intel CPUs.","intents":["I need to run embeddings on edge devices or mobile without cloud API calls","I want to reduce model size from 1.3GB to <350MB for on-device deployment","I need sub-50ms inference latency on CPU-only infrastructure for real-time search","I want to avoid GPU costs and cloud inference fees for embedding generation at scale"],"best_for":["edge computing teams deploying embeddings on IoT devices, mobile phones, or embedded systems","cost-conscious organizations processing millions of embeddings without GPU infrastructure","privacy-first applications requiring on-device inference without data transmission","developers building offline-first applications with local semantic search capabilities"],"limitations":["INT8 quantization introduces ~1-2% accuracy degradation on MTEB benchmarks; not suitable for applications requiring maximum precision","ONNX Runtime CPU inference is 3-5x slower than GPU inference; batch processing required for throughput >100 embeddings/sec","OpenVINO optimization is Intel CPU-specific; ARM-based edge devices (Raspberry Pi, mobile) may not benefit from graph optimizations","Requires separate model conversion pipeline; updates to base model necessitate re-quantization and re-export","Limited debugging visibility into quantization artifacts; difficult to diagnose embedding quality issues post-quantization"],"requires":["onnxruntime 1.15.0+ or openvino 2023.0+","Python 3.8+ (for conversion utilities)","2GB+ RAM for inference (quantized model)","CPU with AVX2 support recommended for ONNX Runtime optimizations"],"input_types":["plain text strings","ONNX-compatible tensor inputs (int64 token IDs, attention masks)"],"output_types":["ONNX tensor outputs (float32 embeddings)","OpenVINO IR format outputs compatible with Intel inference engines"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-whereisai--uae-large-v1__cap_10","uri":"capability://automation.workflow.text.embeddings.inference.server.compatibility.for.high.throughput.serving","name":"text-embeddings-inference server compatibility for high-throughput serving","description":"Compatible with Hugging Face's text-embeddings-inference (TEI) server, a Rust-based inference engine optimized for embedding workloads with batching, caching, and dynamic quantization. Enables deployment of the model on TEI servers for 10-100x throughput improvement compared to Python-based inference, with automatic request batching and response caching for repeated queries. Supports distributed inference across multiple GPUs with load balancing.","intents":["I need to serve embeddings at high throughput (>1000 requests/sec) without building custom infrastructure","I want to reduce inference latency through request batching and response caching","I need to distribute inference across multiple GPUs for production-scale serving","I want to deploy embeddings with minimal operational overhead"],"best_for":["teams building production search and RAG systems with high query volume","organizations serving embeddings to thousands of concurrent users","developers optimizing inference cost and latency for embedding services","teams deploying embeddings on Kubernetes or container orchestration platforms"],"limitations":["TEI server requires Rust runtime and CUDA/ROCm for GPU support; not available for CPU-only deployment","Dynamic quantization adds ~1-2% accuracy loss; not suitable for maximum-precision applications","Response caching is query-specific; no semantic caching (similar queries not deduplicated)","Distributed inference requires external load balancing; no built-in multi-GPU orchestration","TEI is newer and less mature than Python inference frameworks; limited debugging tools and community support"],"requires":["text-embeddings-inference server (Docker image or binary)","CUDA 11.8+ or ROCm 5.7+ for GPU support","Docker or Kubernetes for containerized deployment","8GB+ VRAM for GPU inference"],"input_types":["HTTP POST requests with JSON payloads","text strings in request body","batch requests with multiple texts"],"output_types":["JSON responses with embeddings","HTTP status codes and error messages"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-whereisai--uae-large-v1__cap_2","uri":"capability://data.processing.analysis.batch.embedding.generation.with.variable.length.sequence.handling","name":"batch embedding generation with variable-length sequence handling","description":"Processes multiple text passages simultaneously through a batching pipeline that dynamically pads sequences to the longest item in the batch, reducing computational waste compared to fixed-size padding. Implements attention masking to ensure padding tokens don't contribute to embeddings, and uses efficient tensor operations to parallelize transformer computations across batch dimensions. Supports batches of 1-512 items with automatic memory management to prevent OOM errors on constrained hardware.","intents":["I need to embed 1M documents efficiently without processing them one-at-a-time","I want to minimize padding overhead when embedding documents of highly variable length","I need to process embeddings in batches while staying within memory constraints","I want to parallelize embedding generation across multiple CPU cores or GPUs"],"best_for":["data engineers building ETL pipelines for corpus-scale embedding generation","teams processing heterogeneous text collections (short queries + long documents)","developers optimizing inference throughput for production embedding services","researchers benchmarking embedding quality on large-scale datasets"],"limitations":["Dynamic padding adds ~5-10ms overhead per batch for sequence length computation; fixed-size batching is faster for homogeneous data","Memory usage scales linearly with batch size and max sequence length; batch_size=512 with 512-token sequences requires ~8GB VRAM","Attention masking computation adds ~2-3% latency overhead compared to unmasked inference","No built-in distributed batching across multiple GPUs; requires external orchestration (Ray, Spark) for multi-device parallelization","Batch size tuning is hardware-dependent; no automatic batch size selection based on available memory"],"requires":["transformers 4.34.0+","torch 1.13.0+ or onnxruntime 1.15.0+","sufficient GPU VRAM (8GB+ recommended) or CPU RAM (16GB+ for large batches)","sentence-transformers 2.2.0+ for simplified batch API"],"input_types":["list of text strings with variable lengths (1-512 tokens)","pre-tokenized sequences (token IDs + attention masks)"],"output_types":["numpy array of shape (batch_size, 1024) with float32 embeddings","PyTorch tensors for downstream model integration"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-whereisai--uae-large-v1__cap_3","uri":"capability://search.retrieval.semantic.similarity.ranking.and.retrieval.with.cosine.distance.computation","name":"semantic similarity ranking and retrieval with cosine distance computation","description":"Computes pairwise cosine similarity between query embeddings and document embeddings using optimized linear algebra operations (BLAS/LAPACK), enabling fast nearest-neighbor retrieval. Implements efficient similarity scoring via dot product normalization, supporting both dense vector search and approximate nearest-neighbor indexing for large-scale retrieval (>1M documents). Returns ranked results sorted by similarity score with optional threshold filtering.","intents":["I need to find the top-K most similar documents to a query from a corpus of millions","I want to rank search results by semantic relevance without BM25 keyword matching","I need to filter documents by similarity threshold (e.g., only return >0.7 similarity matches)","I want to build a recommendation system that finds similar items based on semantic embeddings"],"best_for":["search engineers building semantic search and retrieval-augmented generation (RAG) systems","product teams implementing recommendation engines based on semantic similarity","researchers evaluating embedding quality on retrieval benchmarks","developers building question-answering systems with document ranking"],"limitations":["Brute-force cosine similarity is O(n*d) where n=corpus size and d=embedding dimension; impractical for >10M documents without approximate indexing (FAISS, Annoy)","Cosine similarity assumes normalized embeddings; non-normalized vectors produce incorrect scores","No built-in support for weighted similarity (e.g., boosting recent documents or specific fields)","Similarity scores are relative, not calibrated to absolute relevance; threshold selection requires manual tuning per domain","Requires external vector database (Pinecone, Weaviate, Milvus) for production-scale retrieval; no built-in persistence"],"requires":["numpy 1.21.0+ or scipy 1.7.0+ for similarity computation","sentence-transformers 2.2.0+ for simplified API","vector database (FAISS, Annoy, Pinecone, Weaviate) for >100K documents","pre-computed embeddings stored in memory or vector store"],"input_types":["query embedding (1024-dim float32 vector)","document embeddings (N x 1024 matrix)","optional similarity threshold (float 0-1)"],"output_types":["ranked list of (document_id, similarity_score) tuples","top-K results with scores","filtered results above threshold"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-whereisai--uae-large-v1__cap_4","uri":"capability://search.retrieval.cross.lingual.semantic.matching.without.language.specific.models","name":"cross-lingual semantic matching without language-specific models","description":"Enables semantic matching between text in different languages by projecting all languages into a shared embedding space learned during multilingual contrastive training. The model learns language-agnostic representations where semantically equivalent phrases in different languages have similar embeddings, without requiring language identification or separate language-specific models. Supports direct similarity computation between queries in one language and documents in another.","intents":["I need to find English documents matching a query in Spanish, Arabic, or Chinese without translation","I want to build a multilingual search engine with a single model instead of maintaining separate models per language","I need to cluster documents across multiple languages by semantic meaning","I want to match user-generated content in any language against a multilingual knowledge base"],"best_for":["global teams building multilingual search and recommendation systems","organizations serving users across 200+ languages without language-specific infrastructure","researchers studying cross-lingual semantic understanding and transfer learning","developers building international customer support systems with semantic matching"],"limitations":["Cross-lingual performance degrades for low-resource languages (e.g., Amharic, Icelandic) due to limited training data; high-resource languages (English, Spanish, Chinese) perform best","No explicit language identification; ambiguous text (e.g., code-mixed queries) may produce suboptimal embeddings","Semantic equivalence is approximate; idioms, cultural references, and domain-specific terminology may not align across languages","Requires careful query formulation; grammatically incorrect or heavily accented text may degrade matching quality","No language-specific fine-tuning available; domain adaptation requires retraining or external fine-tuning"],"requires":["transformers 4.34.0+","sentence-transformers 2.2.0+","text in any of 200+ supported languages","no language identification or preprocessing required"],"input_types":["text strings in any supported language","mixed-language text (code-switching)","transliterated text (e.g., Hinglish)"],"output_types":["embeddings in shared 1024-dim space","cross-lingual similarity scores","ranked multilingual results"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-whereisai--uae-large-v1__cap_5","uri":"capability://data.processing.analysis.mteb.benchmark.compatible.evaluation.and.fine.tuning","name":"mteb benchmark-compatible evaluation and fine-tuning","description":"Integrates with the Massive Text Embedding Benchmark (MTEB) evaluation framework, enabling standardized assessment across 56 datasets covering retrieval, clustering, semantic similarity, and reranking tasks. Provides pre-computed benchmark scores and supports fine-tuning on custom datasets using the same evaluation protocol, allowing researchers to measure improvements against established baselines. Compatible with sentence-transformers' fine-tuning API for domain-specific adaptation.","intents":["I want to evaluate my embedding model on standardized benchmarks to compare against published results","I need to fine-tune the model on domain-specific data and measure improvement using MTEB metrics","I want to understand which tasks (retrieval, clustering, reranking) my embeddings perform best on","I need to validate that my custom embeddings meet quality thresholds before production deployment"],"best_for":["researchers publishing embedding models and comparing against baselines","teams fine-tuning embeddings for domain-specific applications (legal, medical, scientific)","organizations validating embedding quality before production deployment","developers benchmarking embedding performance across different hardware and inference engines"],"limitations":["MTEB evaluation is time-consuming (2-4 hours for full benchmark on GPU); not suitable for rapid iteration","Benchmark scores may not correlate with downstream task performance; high MTEB scores don't guarantee production quality","Fine-tuning requires labeled data (similarity pairs, relevance judgments); no unsupervised fine-tuning support","MTEB covers general-domain tasks; domain-specific evaluation (biomedical, legal) requires custom benchmarks","Benchmark results are snapshot-in-time; model updates or training data changes require re-evaluation"],"requires":["mteb 0.0.50+ library","sentence-transformers 2.2.0+","labeled training data for fine-tuning (optional)","GPU recommended for evaluation speed (4-8 hours on CPU)"],"input_types":["pre-trained model checkpoint","optional labeled fine-tuning dataset (queries, documents, relevance labels)","MTEB task specifications"],"output_types":["MTEB benchmark scores (nDCG, NDCG@10, MAP, MRR, etc.)","per-task performance breakdown","fine-tuned model checkpoint"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-whereisai--uae-large-v1__cap_6","uri":"capability://safety.moderation.safetensors.format.support.for.secure.model.loading.and.distribution","name":"safetensors format support for secure model loading and distribution","description":"Provides model weights in safetensors format, a secure serialization standard that prevents arbitrary code execution during model loading (unlike pickle-based PyTorch formats). Enables fast, memory-mapped loading of model weights without deserializing untrusted Python objects, reducing security risks in multi-tenant environments. Compatible with transformers library's native safetensors support for transparent format handling.","intents":["I need to load models from untrusted sources without risking code injection attacks","I want to distribute model weights securely without pickle vulnerabilities","I need fast model loading with memory-mapped access for large models","I want to ensure model integrity through cryptographic verification"],"best_for":["security-conscious teams deploying models in multi-tenant or cloud environments","organizations distributing models to external partners or customers","developers building model serving infrastructure (Hugging Face Inference API, Together AI)","researchers sharing models on public repositories (Hugging Face Hub)"],"limitations":["Safetensors format is newer; some older tools and frameworks don't support it yet (requires transformers 4.30.0+)","No performance advantage over PyTorch format for inference; security benefit is primary value","Requires explicit format specification during loading; automatic format detection may fail with mixed repositories","Safetensors doesn't prevent model poisoning (adversarial weights); only prevents code execution during loading","Conversion from PyTorch to safetensors adds one-time overhead; no automatic conversion on first load"],"requires":["transformers 4.30.0+","safetensors 0.3.0+ library","Python 3.8+"],"input_types":["safetensors model files (.safetensors extension)","Hugging Face Hub model identifiers with safetensors format"],"output_types":["loaded model weights as PyTorch tensors","memory-mapped weight access for efficient loading"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-whereisai--uae-large-v1__cap_7","uri":"capability://automation.workflow.hugging.face.hub.integration.with.model.versioning.and.auto.download","name":"hugging face hub integration with model versioning and auto-download","description":"Integrates seamlessly with Hugging Face Hub for automatic model discovery, versioning, and download. Supports model caching, revision pinning (specific commits or tags), and automatic fallback to cached versions if Hub is unavailable. Enables one-line model loading with automatic dependency resolution and format detection (PyTorch, safetensors, ONNX).","intents":["I want to load the model with a single line of code without manual download","I need to pin specific model versions for reproducibility across environments","I want to cache models locally to avoid repeated downloads","I need to handle Hub unavailability gracefully by falling back to cached versions"],"best_for":["developers building quick prototypes and demos with minimal setup","teams deploying models in CI/CD pipelines with version pinning requirements","researchers ensuring reproducibility across different machines and time periods","organizations with limited bandwidth or offline environments requiring model caching"],"limitations":["First download requires internet connectivity and ~1.3GB bandwidth; no built-in compression or delta updates","Cache location is user-dependent; multi-user systems may have cache conflicts or permission issues","Hub API rate limits apply; bulk downloading many model versions may hit rate limits","No built-in model verification; relies on Hub's integrity checks (SHA256 hashes)","Revision pinning requires commit SHAs or tags; branch names are not stable across time"],"requires":["transformers 4.34.0+","huggingface-hub 0.16.0+","internet connectivity for initial download","~1.3GB disk space for model cache"],"input_types":["model identifier string (e.g., 'WhereIsAI/UAE-Large-V1')","optional revision specification (commit SHA, tag, or branch)"],"output_types":["loaded model ready for inference","local cache path for offline access"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-whereisai--uae-large-v1__cap_8","uri":"capability://data.processing.analysis.transformers.js.browser.compatible.inference","name":"transformers.js browser-compatible inference","description":"Provides WebAssembly-compiled model weights and JavaScript bindings enabling inference directly in web browsers without server-side computation. Uses ONNX.js runtime for efficient tensor operations in JavaScript, supporting both CPU inference and WebGPU acceleration on compatible browsers. Enables client-side embedding generation for privacy-preserving applications without data transmission to servers.","intents":["I want to run embeddings in the browser without sending user data to a server","I need to build a privacy-first search application with client-side semantic matching","I want to reduce server load by offloading embedding computation to client browsers","I need to enable offline semantic search in web applications"],"best_for":["privacy-focused teams building client-side AI applications","web developers reducing server infrastructure costs through client-side inference","organizations handling sensitive data (healthcare, legal) requiring on-device processing","developers building offline-first web applications with semantic search"],"limitations":["Browser inference is 5-10x slower than GPU inference due to JavaScript/WebAssembly overhead; suitable for <100 embeddings/session","Model size (~1.3GB) is impractical for browser download; requires quantization or model distillation for <100MB footprint","WebGPU support is limited to Chromium-based browsers (Chrome, Edge); Firefox and Safari lack GPU acceleration","No persistent storage in browser; embeddings must be recomputed per session or stored in IndexedDB (limited to 50MB)","Memory constraints in browsers limit batch processing; single-document inference only"],"requires":["modern browser with WebAssembly support (Chrome 57+, Firefox 52+, Safari 14.1+)","transformers.js 2.6.0+ library","JavaScript/TypeScript environment","optional: WebGPU support for GPU acceleration (Chrome 113+)"],"input_types":["text strings (JavaScript strings)","batch arrays of text"],"output_types":["JavaScript Float32Array containing embeddings","JSON-serializable embedding arrays"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-whereisai--uae-large-v1__cap_9","uri":"capability://automation.workflow.azure.deployment.compatibility.with.managed.inference.endpoints","name":"azure deployment compatibility with managed inference endpoints","description":"Supports direct deployment to Azure Machine Learning endpoints with pre-configured inference containers and auto-scaling. Integrates with Azure's managed inference infrastructure for production-grade serving with built-in monitoring, logging, and A/B testing capabilities. Enables one-click deployment from Hugging Face Hub to Azure without custom container configuration.","intents":["I want to deploy embeddings to Azure without writing custom inference code","I need production-grade serving with auto-scaling and monitoring","I want to integrate embeddings into Azure ML pipelines and workflows","I need to run A/B tests comparing different embedding models on Azure"],"best_for":["Azure-native teams deploying models within existing Azure infrastructure","enterprises requiring managed inference with SLA guarantees and monitoring","organizations needing integration with Azure ML pipelines and workflows","teams leveraging Azure's auto-scaling and cost optimization features"],"limitations":["Azure-specific deployment; no direct support for AWS SageMaker or GCP Vertex AI","Managed endpoints add 10-20% cost premium compared to self-managed inference","Cold start latency ~5-10 seconds for first inference after scaling down","Limited customization of inference containers; advanced optimization requires custom Docker images","Requires Azure subscription and familiarity with Azure ML concepts"],"requires":["Azure subscription with ML workspace","Azure CLI or Python SDK","Hugging Face Hub model access","appropriate Azure compute quota (GPU or CPU)"],"input_types":["text strings via REST API","batch requests with JSON payloads"],"output_types":["JSON responses with embeddings","REST API responses with HTTP status codes"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","transformers library 4.34.0+","sentence-transformers 2.2.0+ (recommended for simplified API)","torch 1.13.0+ or onnxruntime 1.15.0+ for inference","4GB+ RAM for model loading (11.5B parameters quantized to FP32)","onnxruntime 1.15.0+ or openvino 2023.0+","Python 3.8+ (for conversion utilities)","2GB+ RAM for inference (quantized model)","CPU with AVX2 support recommended for ONNX Runtime optimizations","text-embeddings-inference server (Docker image or binary)"],"failure_modes":["1024-dimensional embeddings consume ~4KB per vector in memory; large-scale deployments (>10M documents) require vector database infrastructure","Inference latency ~50-100ms per passage on CPU, ~10-20ms on GPU depending on sequence length and hardware","Maximum sequence length 512 tokens; longer documents require chunking strategy, introducing boundary artifacts","Trained on general web text; domain-specific terminology (medical, legal, scientific) may have degraded embedding quality without fine-tuning","No built-in support for weighted token importance or custom pooling strategies beyond mean pooling","INT8 quantization introduces ~1-2% accuracy degradation on MTEB benchmarks; not suitable for applications requiring maximum precision","ONNX Runtime CPU inference is 3-5x slower than GPU inference; batch processing required for throughput >100 embeddings/sec","OpenVINO optimization is Intel CPU-specific; ARM-based edge devices (Raspberry Pi, mobile) may not benefit from graph optimizations","Requires separate model conversion pipeline; updates to base model necessitate re-quantization and re-export","Limited debugging visibility into quantization artifacts; difficult to diagnose embedding quality issues post-quantization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7439898448295321,"quality":0.32,"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":"2026-05-24T12:16:22.766Z","last_scraped_at":"2026-05-03T14:23:02.600Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":1337383,"model_likes":237}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=whereisai--uae-large-v1","compare_url":"https://unfragile.ai/compare?artifact=whereisai--uae-large-v1"}},"signature":"GW0+Y1Eve0/KDyi1KzbzmIYertmjTsj2PlPXoBDYlD9oaEosuIzMUAee7SC2O2wNFeb0zxoQxS83jzFOLHUcCQ==","signedAt":"2026-06-20T15:04:38.599Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/whereisai--uae-large-v1","artifact":"https://unfragile.ai/whereisai--uae-large-v1","verify":"https://unfragile.ai/api/v1/verify?slug=whereisai--uae-large-v1","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"}}