{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-sshleifer--distilbart-cnn-12-6","slug":"sshleifer--distilbart-cnn-12-6","name":"distilbart-cnn-12-6","type":"model","url":"https://huggingface.co/sshleifer/distilbart-cnn-12-6","page_url":"https://unfragile.ai/sshleifer--distilbart-cnn-12-6","categories":["model-training"],"tags":["transformers","pytorch","jax","rust","bart","text2text-generation","summarization","en","dataset:cnn_dailymail","dataset:xsum","license:apache-2.0","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-sshleifer--distilbart-cnn-12-6__cap_0","uri":"capability://text.generation.language.abstractive.text.summarization.with.distilled.bart.architecture","name":"abstractive text summarization with distilled bart architecture","description":"Performs extractive-to-abstractive summarization using a 12-layer encoder / 6-layer decoder BART model distilled from the full 16/16 BART-large architecture. The model uses cross-attention between encoder and decoder with learned positional embeddings and applies byte-pair encoding (BPE) tokenization via the BART tokenizer. It generates summaries by predicting token sequences conditioned on the full input document, enabling paraphrasing and semantic compression rather than pure extraction.","intents":["I need to automatically condense long news articles into 1-2 sentence summaries for a news aggregation app","I want to reduce inference latency and memory footprint compared to full-size BART while maintaining summary quality","I need to batch-process thousands of documents for summarization without GPU memory constraints","I want to fine-tune a pre-trained summarization model on domain-specific documents (legal, medical, technical)"],"best_for":["teams building production summarization pipelines with latency/cost constraints","developers deploying on edge devices or resource-constrained environments","ML engineers prototyping summarization features before scaling to larger models","organizations processing high-volume document streams (news, research, support tickets)"],"limitations":["Distillation reduces model capacity — struggles with highly technical or domain-specific jargon not well-represented in CNN/DailyMail training data","Fixed maximum input length of 1024 tokens — longer documents require truncation or sliding-window approaches","Abstractive generation can hallucinate facts not present in source text, especially for out-of-distribution inputs","No built-in handling of multi-document summarization — processes single documents only","Inference latency still ~500-800ms per document on CPU; GPU required for real-time batch processing at scale"],"requires":["PyTorch 1.9+ or JAX/Flax for model loading and inference","Transformers library 4.0+","Minimum 2GB RAM for single-document inference; 8GB+ recommended for batch processing","CUDA 11.0+ for GPU acceleration (optional but strongly recommended)"],"input_types":["raw text (English language documents)","pre-tokenized sequences (token IDs as integers)","batched text inputs (multiple documents in parallel)"],"output_types":["generated summary text (variable length, typically 50-150 tokens)","token logits and attention weights (for interpretability)","beam search candidates (multiple summary hypotheses with scores)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sshleifer--distilbart-cnn-12-6__cap_1","uri":"capability://tool.use.integration.multi.framework.model.serialization.and.deployment","name":"multi-framework model serialization and deployment","description":"Supports model loading and inference across PyTorch, JAX/Flax, and Rust backends through the Hugging Face model hub's unified checkpoint format. The model weights are stored in a framework-agnostic SafeTensors format, enabling automatic conversion and optimization for different runtime environments. Includes pre-configured deployment templates for Azure ML, AWS SageMaker, and Hugging Face Inference Endpoints with built-in batching and quantization support.","intents":["I need to deploy the same model across multiple cloud providers without rewriting inference code","I want to use JAX for research/experimentation but deploy with PyTorch in production","I need to run inference in a Rust service for performance-critical applications","I want to automatically optimize the model for different hardware targets (CPU, GPU, TPU)"],"best_for":["platform teams managing multi-language ML infrastructure","organizations with heterogeneous deployment targets (cloud, edge, on-prem)","researchers prototyping in JAX/TensorFlow but deploying PyTorch models","teams requiring framework-agnostic model versioning and governance"],"limitations":["SafeTensors conversion adds ~2-5 second overhead on first load (cached thereafter)","Rust bindings require manual compilation for custom CUDA versions — pre-built wheels only support CUDA 11.8 and 12.1","JAX backend requires XLA compilation on first inference pass (~10-30 seconds depending on batch size)","No automatic quantization — INT8/FP16 requires explicit configuration per framework"],"requires":["Hugging Face transformers library 4.0+ (for PyTorch backend)","JAX 0.3.0+ and Flax 0.4.0+ (for JAX backend, optional)","Rust 1.56+ and candle library (for Rust backend, optional)","SafeTensors library 0.2.0+ for checkpoint loading"],"input_types":["PyTorch tensors (torch.Tensor)","JAX arrays (jax.Array)","NumPy arrays (numpy.ndarray)","raw text strings (auto-tokenized)"],"output_types":["PyTorch tensors with gradients (for training)","JAX arrays (immutable, JIT-compilable)","NumPy arrays (framework-agnostic)","structured outputs (token logits, attention, hidden states)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sshleifer--distilbart-cnn-12-6__cap_2","uri":"capability://data.processing.analysis.batch.inference.with.dynamic.padding.and.attention.masking","name":"batch inference with dynamic padding and attention masking","description":"Implements efficient batch processing through dynamic padding (sequences padded to max length in batch, not global max) and sparse attention masking that prevents the model from attending to padding tokens. Uses PyTorch's native batching with attention_mask tensors and JAX's vmap for automatic vectorization. Supports variable-length inputs within a batch without performance degradation through intelligent bucketing and mask generation.","intents":["I need to process 1000s of documents with varying lengths efficiently without padding waste","I want to maximize GPU utilization by batching documents of different sizes together","I need to implement streaming inference where documents arrive asynchronously","I want to reduce memory footprint by avoiding unnecessary padding in attention computations"],"best_for":["teams processing high-volume document streams with variable lengths","ML engineers optimizing GPU utilization and cost per inference","applications with strict latency SLAs requiring efficient batching strategies","resource-constrained environments (mobile, edge) where memory is premium"],"limitations":["Dynamic padding adds ~5-10% overhead for mask generation and application per batch","Attention masking is computed on-device — no pre-computation or caching across batches","Maximum batch size limited by GPU memory (typically 32-128 for full model on 16GB VRAM)","Bucketing strategy requires knowing document length distribution in advance for optimal performance"],"requires":["PyTorch 1.9+ with CUDA support (for GPU batching)","Transformers library 4.0+ (handles mask generation automatically)","GPU with minimum 8GB VRAM for batch_size > 16"],"input_types":["list of text strings (variable length)","pre-tokenized sequences (token IDs with length metadata)","batched tensors with attention_mask"],"output_types":["batched summary tensors (batch_size x summary_length)","per-sample attention weights (for interpretability)","batch-level metrics (throughput, latency percentiles)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sshleifer--distilbart-cnn-12-6__cap_3","uri":"capability://code.generation.editing.transfer.learning.and.fine.tuning.on.custom.datasets","name":"transfer learning and fine-tuning on custom datasets","description":"Provides pre-trained weights initialized from CNN/DailyMail and XSum datasets, enabling rapid fine-tuning on domain-specific summarization tasks through standard PyTorch training loops or Hugging Face Trainer API. Supports parameter-efficient fine-tuning via LoRA (Low-Rank Adaptation) adapters that freeze base model weights and train only 0.1-1% of parameters. Includes built-in evaluation metrics (ROUGE, BERTScore) and checkpoint management for early stopping.","intents":["I want to adapt the model to summarize medical research papers or legal documents with domain-specific terminology","I need to fine-tune on a small labeled dataset (100-1000 examples) without catastrophic forgetting","I want to reduce fine-tuning time and memory by using LoRA instead of full model training","I need to evaluate summary quality on my custom test set using standard metrics"],"best_for":["domain experts fine-tuning on specialized corpora (legal, medical, financial)","teams with limited labeled data (< 10k examples) wanting to leverage pre-training","researchers experimenting with different fine-tuning strategies and hyperparameters","organizations with GPU constraints wanting parameter-efficient adaptation"],"limitations":["Fine-tuning on very small datasets (< 100 examples) risks overfitting — requires careful regularization and validation","LoRA adapters add ~5-10% inference latency due to adapter weight merging","ROUGE metrics correlate imperfectly with human judgment — requires manual evaluation for quality assurance","No multi-task fine-tuning support — requires separate models for different summarization styles","Fine-tuning on GPU requires 16GB+ VRAM; CPU fine-tuning is prohibitively slow (hours per epoch)"],"requires":["PyTorch 1.9+ with CUDA 11.0+","Transformers library 4.0+","Datasets library for data loading and preprocessing","GPU with minimum 16GB VRAM (8GB with gradient checkpointing)","Labeled training data in text-summary pairs (CSV, JSON, or Hugging Face Dataset format)"],"input_types":["text-summary pairs (CSV with 'text' and 'summary' columns)","Hugging Face Dataset objects","JSON Lines format (one example per line)"],"output_types":["fine-tuned model checkpoint (PyTorch .pt or SafeTensors format)","training metrics (loss, ROUGE scores per epoch)","evaluation results (ROUGE-1/2/L, BERTScore on test set)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sshleifer--distilbart-cnn-12-6__cap_4","uri":"capability://planning.reasoning.interpretability.and.attention.visualization","name":"interpretability and attention visualization","description":"Exposes encoder and decoder attention weights at all 12 encoder and 6 decoder layers, enabling visualization of which input tokens the model attends to when generating each summary token. Supports extraction of hidden states from any layer for probing tasks and feature analysis. Includes utilities for attention head analysis and cross-attention pattern visualization to understand encoder-decoder alignment.","intents":["I want to understand which parts of a document the model focuses on when generating each summary sentence","I need to debug why the model generates incorrect or hallucinated facts by inspecting attention patterns","I want to extract intermediate representations for downstream tasks like document classification","I need to validate that the model is learning linguistically meaningful patterns before deployment"],"best_for":["ML researchers studying attention mechanisms and model interpretability","teams validating model behavior before production deployment","developers debugging summarization failures on edge cases","educators teaching transformer architectures with concrete visualizations"],"limitations":["Attention weights don't directly explain model decisions — high attention doesn't guarantee relevance (attention is not explanation)","Extracting all attention heads and hidden states increases memory usage by 2-3x during inference","Visualization tools require manual implementation or external libraries (e.g., BertViz) — not built-in","Cross-attention patterns are difficult to interpret for long documents due to high dimensionality"],"requires":["PyTorch 1.9+ with output_attentions=True flag enabled","Transformers library 4.0+","Optional: BertViz or similar visualization library for attention heatmaps"],"input_types":["text strings (with output_attentions=True)","pre-tokenized sequences with attention_mask"],"output_types":["attention tensors (num_layers x batch_size x num_heads x seq_length x seq_length)","hidden states (num_layers x batch_size x seq_length x hidden_dim)","attention visualizations (heatmaps, flow diagrams)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sshleifer--distilbart-cnn-12-6__cap_5","uri":"capability://automation.workflow.quantization.and.model.compression.for.edge.deployment","name":"quantization and model compression for edge deployment","description":"Supports INT8 post-training quantization and FP16 mixed-precision inference through PyTorch's native quantization APIs and ONNX Runtime. Reduces model size from 306M parameters (~1.2GB in FP32) to ~300MB (INT8) or ~600MB (FP16) without retraining. Enables deployment on mobile devices, embedded systems, and resource-constrained cloud instances with minimal accuracy loss (< 2% ROUGE degradation).","intents":["I need to deploy summarization on mobile devices or IoT devices with < 500MB storage","I want to reduce inference latency by 30-50% using quantized models on CPU-only servers","I need to lower cloud costs by using cheaper instance types that can't fit full-precision models","I want to enable on-device inference without sending documents to external APIs for privacy"],"best_for":["mobile app developers deploying on iOS/Android with storage constraints","edge computing teams running inference on Raspberry Pi, Jetson, or similar devices","cost-conscious teams optimizing cloud inference spend","organizations with privacy requirements preventing cloud API calls"],"limitations":["INT8 quantization introduces 1-3% ROUGE score degradation on out-of-distribution inputs","Quantized models require ONNX Runtime or specialized inference engines — not compatible with standard PyTorch inference","Calibration data required for post-training quantization — requires representative examples from target domain","FP16 inference requires GPU support — CPU-only deployment limited to INT8","Quantized models lose gradient information — cannot be fine-tuned without dequantization"],"requires":["PyTorch 1.9+ with quantization support","ONNX Runtime 1.10+ (for INT8 inference)","Calibration dataset (100-1000 representative examples)","Optional: TensorRT (NVIDIA) or CoreML (Apple) for platform-specific optimization"],"input_types":["text strings (auto-tokenized)","pre-tokenized sequences","batched inputs for calibration"],"output_types":["quantized model checkpoint (INT8 or FP16)","ONNX model file (platform-agnostic)","platform-specific formats (CoreML for iOS, TFLite for Android)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sshleifer--distilbart-cnn-12-6__cap_6","uri":"capability://tool.use.integration.api.agnostic.model.serving.and.endpoint.compatibility","name":"api-agnostic model serving and endpoint compatibility","description":"Compatible with Hugging Face Inference Endpoints, Azure ML, AWS SageMaker, and custom REST/gRPC servers through standardized model card and pipeline configuration. Automatically handles tokenization, batching, and output formatting across different serving platforms. Supports both synchronous request-response and asynchronous batch processing patterns without code changes.","intents":["I want to deploy the model on Hugging Face Inference Endpoints without writing custom inference code","I need to serve the model on Azure ML or AWS SageMaker with auto-scaling and monitoring","I want to build a custom REST API that handles variable batch sizes and timeouts gracefully","I need to support both real-time and batch inference patterns from the same model"],"best_for":["teams deploying on managed ML platforms (Hugging Face, Azure, AWS)","startups wanting zero-ops model serving without infrastructure expertise","organizations requiring multi-cloud deployment flexibility","teams building internal ML platforms with standardized model serving"],"limitations":["Hugging Face Inference Endpoints have cold-start latency of 5-10 seconds on first request","Azure ML and SageMaker require custom container images for non-standard configurations","Batch processing APIs have different timeout limits per platform (30s on HF, 15min on SageMaker)","No built-in request queuing or priority handling — requires external load balancer","Monitoring and logging vary by platform — no unified observability across endpoints"],"requires":["Hugging Face account (for HF Inference Endpoints)","Azure subscription and ML workspace (for Azure ML)","AWS account and SageMaker access (for SageMaker)","Docker (for custom container deployments)","API key or authentication credentials per platform"],"input_types":["JSON request bodies with 'inputs' field (text string or list of strings)","multipart form data with text files","streaming request bodies (for real-time processing)"],"output_types":["JSON responses with 'generated_text' field","structured outputs with confidence scores and metadata","streaming responses (Server-Sent Events format)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+ or JAX/Flax for model loading and inference","Transformers library 4.0+","Minimum 2GB RAM for single-document inference; 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GPU required for real-time batch processing at scale","SafeTensors conversion adds ~2-5 second overhead on first load (cached thereafter)","Rust bindings require manual compilation for custom CUDA versions — pre-built wheels only support CUDA 11.8 and 12.1","JAX backend requires XLA compilation on first inference pass (~10-30 seconds depending on batch size)","No automatic quantization — INT8/FP16 requires explicit configuration per framework","Dynamic padding adds ~5-10% overhead for mask generation and application per batch","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7374584168380178,"quality":0.24,"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":"2026-05-03T14:22:54.515Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":1111635,"model_likes":317}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=sshleifer--distilbart-cnn-12-6","compare_url":"https://unfragile.ai/compare?artifact=sshleifer--distilbart-cnn-12-6"}},"signature":"L765Mt07EYL9vGG57RAhDtiKpf7DMbUOkTFEBJzIOfzBlmJqNy18ViQk+SFoyGkWcPfhVfhaWRkQWbUX3hK+Aw==","signedAt":"2026-06-22T12:10:29.603Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sshleifer--distilbart-cnn-12-6","artifact":"https://unfragile.ai/sshleifer--distilbart-cnn-12-6","verify":"https://unfragile.ai/api/v1/verify?slug=sshleifer--distilbart-cnn-12-6","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"}}