{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-sshleifer--distilbart-cnn-6-6","slug":"sshleifer--distilbart-cnn-6-6","name":"distilbart-cnn-6-6","type":"model","url":"https://huggingface.co/sshleifer/distilbart-cnn-6-6","page_url":"https://unfragile.ai/sshleifer--distilbart-cnn-6-6","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-6-6__cap_0","uri":"capability://text.generation.language.abstractive.summarization.with.distilled.bart","name":"abstractive-summarization-with-distilled-bart","description":"Performs abstractive text summarization using a 6-layer encoder-decoder BART architecture distilled from the full 12-layer model, reducing parameters by ~50% while maintaining quality. The model uses cross-attention between encoder and decoder with learned positional embeddings, trained on CNN/DailyMail and XSum datasets to generate human-readable summaries that paraphrase rather than extract source text. Inference runs efficiently on CPU or GPU via PyTorch/JAX backends with support for batch processing and variable-length inputs up to 1024 tokens.","intents":["I need to automatically summarize long news articles or documents into concise overviews for quick consumption","I want to reduce computational cost of summarization in production while maintaining reasonable quality","I need to integrate summarization into a batch processing pipeline that handles thousands of documents daily","I want to generate abstractive summaries that rephrase content rather than just extracting key sentences"],"best_for":["teams building content curation or news aggregation platforms","developers deploying summarization at scale with resource constraints","organizations processing CNN/DailyMail-style news content","edge deployments or mobile inference scenarios requiring model compression"],"limitations":["Distillation reduces model capacity — may struggle with highly technical or domain-specific jargon outside training distribution","Fixed 1024-token input limit requires preprocessing of longer documents","Abstractive approach can hallucinate facts not present in source text, especially on out-of-distribution inputs","Optimized for English news; cross-lingual performance not evaluated","No built-in confidence scoring or uncertainty quantification for summary quality","Beam search decoding adds ~100-300ms latency per document depending on hardware"],"requires":["PyTorch 1.9+ or JAX/Flax for inference","Transformers library 4.5+","Minimum 2GB RAM for single-document inference, 8GB+ for batch processing","HuggingFace Hub access or local model weights (~300MB disk space)"],"input_types":["raw text (English)","pre-tokenized sequences","batched text arrays"],"output_types":["text (summary)","token logits","attention weights"],"categories":["text-generation-language","model-compression"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sshleifer--distilbart-cnn-6-6__cap_1","uri":"capability://data.processing.analysis.batch.document.summarization.with.variable.length.handling","name":"batch-document-summarization-with-variable-length-handling","description":"Processes multiple documents in parallel batches with automatic padding/truncation to handle variable input lengths up to 1024 tokens. The implementation uses PyTorch DataLoader patterns or manual batching with attention masks to efficiently pack sequences, enabling GPU utilization across multiple documents simultaneously. Supports both greedy decoding and beam search (configurable beam width) for summary generation, with optional length constraints to control output verbosity.","intents":["I need to summarize hundreds or thousands of documents efficiently in a single batch job","I want to maximize GPU utilization by processing multiple documents in parallel","I need to handle documents of varying lengths without manual preprocessing","I want to control summary length with min/max token constraints"],"best_for":["data engineering teams processing document corpora","batch inference pipelines in data warehouses or ETL workflows","researchers evaluating summarization on benchmark datasets","production systems with non-real-time summarization requirements"],"limitations":["Batch processing requires all documents to fit in GPU memory after padding — large batches may cause OOM on consumer GPUs","Padding overhead increases computation for short documents in mixed-length batches","No dynamic batching — batch size must be fixed at inference time","Beam search decoding scales quadratically with beam width, adding latency for large beams (width > 4)","No streaming/incremental output — must wait for full batch completion"],"requires":["PyTorch 1.9+ with CUDA 11.0+ for GPU acceleration (CPU fallback available but slow)","Transformers library 4.5+ with batch processing utilities","GPU with 6GB+ VRAM for batch_size >= 8, or CPU with 16GB+ RAM","Tokenizer compatible with BART (included in model package)"],"input_types":["list of text strings","pre-tokenized token IDs with attention masks","pandas DataFrame with text column"],"output_types":["list of summary strings","token IDs with attention masks","structured JSON with summaries and metadata"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sshleifer--distilbart-cnn-6-6__cap_2","uri":"capability://automation.workflow.multi.backend.inference.pytorch.jax.rust","name":"multi-backend-inference-pytorch-jax-rust","description":"Supports inference execution across three distinct backends: PyTorch (default, optimized for NVIDIA/AMD GPUs), JAX (for TPU and advanced compilation), and Rust (via ONNX Runtime for edge deployment). The model weights are framework-agnostic and can be loaded and converted between formats, with HuggingFace Transformers library handling backend abstraction. Each backend has different performance characteristics: PyTorch offers best GPU support, JAX enables XLA compilation for TPU, and Rust/ONNX provides minimal-dependency deployment.","intents":["I need to run summarization on TPU infrastructure for cost-effective large-scale inference","I want to deploy the model to edge devices or serverless functions with minimal dependencies","I need to switch between GPU and CPU inference without code changes","I want to use ONNX Runtime for optimized inference on heterogeneous hardware"],"best_for":["teams with multi-hardware infrastructure (GPU, TPU, CPU)","edge computing and mobile deployment scenarios","serverless/FaaS platforms with strict dependency constraints","research teams exploring different compilation strategies"],"limitations":["JAX backend requires additional setup (jax, flax libraries) and is less documented than PyTorch","Rust/ONNX backend requires model conversion and may have numerical precision differences vs PyTorch","No automatic backend selection — must be explicitly specified at load time","Performance characteristics vary significantly by backend and hardware; benchmarking required per deployment","TPU support (JAX) limited to Google Cloud or research environments","ONNX conversion may lose some dynamic features or require quantization trade-offs"],"requires":["PyTorch 1.9+ OR JAX 0.3+ OR ONNX Runtime 1.10+","Transformers library 4.5+ with backend support","For JAX: jax, flax, optax packages","For ONNX: onnxruntime package and converted model weights","For TPU: Google Cloud TPU access and JAX TPU runtime"],"input_types":["text (all backends)","pre-tokenized token IDs (all backends)","numpy/jax arrays (JAX backend)"],"output_types":["text summaries (all backends)","token logits (PyTorch/JAX)","ONNX tensor outputs (Rust/ONNX)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sshleifer--distilbart-cnn-6-6__cap_3","uri":"capability://text.generation.language.cnn.dailymail.and.xsum.optimized.summarization","name":"cnn-dailymail-and-xsum-optimized-summarization","description":"Model is specifically fine-tuned on CNN/DailyMail (news articles with multi-sentence summaries) and XSum (single-sentence abstractive summaries) datasets, making it optimized for news and journalistic content. The training process involved distillation from a full BART model trained on these datasets, preserving the learned patterns for news summarization while reducing model size. This specialization means the model performs best on news-like text with clear structure and journalistic conventions.","intents":["I need to summarize news articles or news-like content with high quality","I want a model pre-trained on benchmark datasets so I don't need to fine-tune","I need both multi-sentence and single-sentence summary capabilities","I want to evaluate summarization quality on standard benchmarks"],"best_for":["news aggregation and content curation platforms","media companies processing wire feeds or article archives","researchers benchmarking against CNN/DailyMail and XSum leaderboards","teams building summarization without resources for custom fine-tuning"],"limitations":["Optimized for English news; performance degrades on technical, scientific, or domain-specific text","May not generalize well to non-news domains (e.g., medical, legal, financial documents)","Training data is from 2016-2019; may not understand recent events or modern terminology","Bias toward journalistic writing style; may produce awkward summaries for casual or technical prose","No multi-lingual support despite being trained on English-only datasets","Fine-tuning on custom domains requires significant labeled data to avoid catastrophic forgetting"],"requires":["Understanding of CNN/DailyMail and XSum dataset characteristics","Text preprocessing to match news article format (title + body structure)","Transformers library 4.5+ with model loading utilities","Optional: labeled data if fine-tuning on custom domain"],"input_types":["news articles (text)","journalistic prose","structured news with title and body"],"output_types":["multi-sentence summaries (CNN/DailyMail style)","single-sentence summaries (XSum style)","text"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sshleifer--distilbart-cnn-6-6__cap_4","uri":"capability://tool.use.integration.huggingface.hub.integration.and.deployment","name":"huggingface-hub-integration-and-deployment","description":"Model is hosted on HuggingFace Hub with native integration into the Transformers library, enabling one-line loading via `AutoModelForSeq2SeqLM.from_pretrained('sshleifer/distilbart-cnn-6-6')`. Supports HuggingFace Inference API for serverless inference, Azure deployment via HuggingFace endpoints, and local caching of model weights. The Hub provides model cards, usage examples, and community discussions, with automatic versioning and reproducibility through commit hashes.","intents":["I want to quickly load and use a pre-trained summarization model without manual setup","I need to deploy the model to a serverless inference API without managing infrastructure","I want to version and track model changes across my organization","I need to share the model with team members or make it publicly available"],"best_for":["developers prototyping summarization features quickly","teams using HuggingFace ecosystem (Transformers, Datasets, Accelerate)","organizations deploying to Azure or HuggingFace Inference endpoints","researchers sharing models and reproducible results"],"limitations":["Requires internet connection for initial model download (unless cached locally)","HuggingFace Inference API has rate limits and latency (100-500ms per request)","Model weights are public; no private/proprietary deployment option on Hub","Hub integration requires Transformers library; not usable with raw PyTorch/JAX without wrapper","Caching behavior can be unpredictable across different environments","Azure deployment adds additional latency and cost compared to local inference"],"requires":["Transformers library 4.5+","Internet connection for model download","HuggingFace account (optional, for private models or uploads)","Python 3.6+","For Azure deployment: Azure account and HuggingFace Inference Endpoints subscription"],"input_types":["text (via Transformers pipeline)","raw strings"],"output_types":["text summaries","HuggingFace API JSON responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sshleifer--distilbart-cnn-6-6__cap_5","uri":"capability://text.generation.language.configurable.beam.search.and.decoding.strategies","name":"configurable-beam-search-and-decoding-strategies","description":"Supports multiple decoding strategies for summary generation: greedy decoding (fastest, lowest quality), beam search with configurable beam width (quality vs speed trade-off), and length-constrained decoding with min/max token limits. The implementation uses PyTorch's built-in beam search utilities with support for early stopping, length penalty, and repetition penalty to control output characteristics. Developers can configure beam width (1-10), length penalties, and other hyperparameters to tune quality vs latency.","intents":["I want to control the trade-off between summary quality and inference latency","I need to enforce minimum and maximum summary lengths for my use case","I want to reduce repetitive or redundant text in generated summaries","I need to experiment with different decoding strategies to find optimal settings"],"best_for":["teams optimizing summarization for specific latency/quality targets","applications with strict output length requirements","researchers tuning decoding hyperparameters","production systems balancing inference cost and quality"],"limitations":["Beam search latency scales with beam width; width > 4 adds significant overhead","Length penalties are heuristic-based and may not always produce desired output lengths","Repetition penalty can sometimes suppress legitimate repeated words","No built-in constraint satisfaction — length limits are soft (not guaranteed)","Hyperparameter tuning requires manual experimentation; no automatic optimization","Decoding strategies are applied post-training; cannot fix fundamental model limitations"],"requires":["Transformers library 4.5+ with generation utilities","PyTorch 1.9+ for beam search implementation","Understanding of decoding hyperparameters (beam_width, length_penalty, etc.)","Optional: validation set to tune hyperparameters"],"input_types":["text","token IDs"],"output_types":["text summaries","token IDs with scores"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+ or JAX/Flax for inference","Transformers library 4.5+","Minimum 2GB RAM for single-document inference, 8GB+ for batch processing","HuggingFace Hub access or local model weights (~300MB disk space)","PyTorch 1.9+ with CUDA 11.0+ for GPU acceleration (CPU fallback available but slow)","Transformers library 4.5+ with batch processing utilities","GPU with 6GB+ VRAM for batch_size >= 8, or CPU with 16GB+ RAM","Tokenizer compatible with BART (included in model package)","PyTorch 1.9+ OR JAX 0.3+ OR ONNX Runtime 1.10+","Transformers library 4.5+ with backend support"],"failure_modes":["Distillation reduces model capacity — may struggle with highly technical or domain-specific jargon outside training distribution","Fixed 1024-token input limit requires preprocessing of longer documents","Abstractive approach can hallucinate facts not present in source text, especially on out-of-distribution inputs","Optimized for English news; cross-lingual performance not evaluated","No built-in confidence scoring or uncertainty quantification for summary quality","Beam search decoding adds ~100-300ms latency per document depending on hardware","Batch processing requires all documents to fit in GPU memory after padding — large batches may cause OOM on consumer GPUs","Padding overhead increases computation for short documents in mixed-length batches","No dynamic batching — batch size must be fixed at inference time","Beam search decoding scales quadratically with beam width, adding latency for large beams (width > 4)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.44527415471723336,"quality":0.22,"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":33640,"model_likes":33}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=sshleifer--distilbart-cnn-6-6","compare_url":"https://unfragile.ai/compare?artifact=sshleifer--distilbart-cnn-6-6"}},"signature":"GYOUKtX28vluwvHa8uqMkaV4GE3bWApgvxPtWthUjDyP8SJj8nEL8MdT95F14B9cDSwQail1zae7D2HGz+bQBw==","signedAt":"2026-06-21T13:22:23.969Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sshleifer--distilbart-cnn-6-6","artifact":"https://unfragile.ai/sshleifer--distilbart-cnn-6-6","verify":"https://unfragile.ai/api/v1/verify?slug=sshleifer--distilbart-cnn-6-6","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"}}