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The model uses masked language modeling pretraining combined with adversarial examples during fine-tuning to learn representations that are resistant to input perturbations and adversarial attacks. It processes raw text through subword tokenization, contextual embedding layers, and a classification head to output class probabilities.","intents":["Classify text documents into predefined categories while maintaining accuracy under adversarial or noisy inputs","Deploy a text classifier that resists evasion attacks and maintains performance on perturbed text","Use a smaller 7B parameter model for text classification with better robustness than standard fine-tuned RoBERTa"],"best_for":["Teams building content moderation systems that need robustness against adversarial text manipulation","Developers deploying text classifiers in security-sensitive applications (spam detection, toxicity filtering)","Organizations requiring smaller models (7B params) with adversarial resilience for edge deployment"],"limitations":["Inference latency ~200-500ms per sample on CPU; requires GPU for batch processing >32 samples","Fixed vocabulary from RoBERTa tokenizer; out-of-vocabulary handling limited to subword fallback","No multi-label classification support — outputs single class prediction per input","Adversarial robustness gains come at ~5-10% accuracy cost on clean, non-adversarial test sets","Requires fine-tuning on downstream task; zero-shot classification performance is limited"],"requires":["PyTorch 1.9+","Transformers library 4.20+","Python 3.8+","Minimum 4GB RAM for inference; 16GB+ recommended for batch processing","CUDA 11.0+ for GPU acceleration (optional but recommended)"],"input_types":["raw text strings (UTF-8 encoded)","text sequences up to 512 tokens (RoBERTa context window)"],"output_types":["class label (string)","class probabilities (float array, softmax normalized)","logits (raw model outputs before softmax)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-trustsafeai--radar-vicuna-7b__cap_1","uri":"capability://data.processing.analysis.batch.text.classification.with.configurable.confidence.thresholding","name":"batch text classification with configurable confidence thresholding","description":"Processes multiple text inputs in parallel through the RoBERTa encoder, accumulating embeddings and computing class probabilities for each sample. Supports configurable confidence thresholds to filter low-confidence predictions, enabling downstream systems to handle uncertain classifications separately. Batching is handled via HuggingFace's pipeline API which manages tokenization, padding, and attention mask generation automatically.","intents":["Classify large document collections (100s-1000s of texts) efficiently in batch mode","Filter classification results by confidence threshold to separate high-confidence from uncertain predictions","Integrate text classification into data processing pipelines with automatic batching and padding"],"best_for":["Data engineers processing document corpora for content categorization or filtering","ML teams building data annotation pipelines that need automated pre-classification","Developers deploying classification as part of larger ETL workflows"],"limitations":["Batch size limited by available GPU memory; typical max batch size 32-64 on 8GB VRAM","All texts in batch must be padded to longest sequence length, causing wasted computation on shorter texts","No streaming/online classification — requires full batch in memory before processing","Confidence thresholding is post-hoc; cannot dynamically adjust model behavior based on input difficulty"],"requires":["PyTorch 1.9+","Transformers 4.20+","Python 3.8+","GPU with 8GB+ VRAM for batch_size >= 32 (CPU inference possible but slow)"],"input_types":["list of text strings","batch size parameter (integer)","confidence threshold parameter (float, 0.0-1.0)"],"output_types":["list of class labels","list of confidence scores (per-sample max probability)","list of full probability distributions (per-class probabilities)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-trustsafeai--radar-vicuna-7b__cap_2","uri":"capability://tool.use.integration.multi.provider.cloud.deployment.with.azure.huggingface.endpoints.compatibility","name":"multi-provider cloud deployment with azure/huggingface endpoints compatibility","description":"Model is packaged and registered on HuggingFace Model Hub with built-in compatibility for HuggingFace Inference Endpoints and Azure ML deployment pipelines. The model card includes metadata for automatic containerization, API schema generation, and region-specific deployment configuration. Supports both REST API access via HuggingFace's hosted inference service and direct deployment to Azure Container Instances or Azure ML endpoints with minimal configuration.","intents":["Deploy the model as a scalable REST API without managing infrastructure","Integrate text classification into Azure ML pipelines for automated data processing","Access the model via HuggingFace's managed inference API with automatic scaling and monitoring"],"best_for":["Teams using Azure ML for MLOps and model lifecycle management","Developers wanting serverless model deployment without container orchestration","Organizations standardized on HuggingFace ecosystem for model distribution and inference"],"limitations":["HuggingFace Endpoints incur per-request pricing (~$0.0001-0.001 per inference depending on model size)","Azure deployment requires Azure subscription and familiarity with Azure ML SDK","Cold start latency ~2-5 seconds on first request to HuggingFace Endpoints due to container spin-up","No custom preprocessing or postprocessing hooks in managed endpoints — limited to standard model inference","Regional availability limited to HuggingFace's supported regions (US, EU); latency increases for other regions"],"requires":["HuggingFace account for Endpoints deployment","Azure subscription (for Azure ML deployment option)","HuggingFace Transformers 4.20+ (for local testing before deployment)","API key for HuggingFace Endpoints or Azure credentials"],"input_types":["HTTP POST request with JSON payload containing text field","text string (max 512 tokens after tokenization)"],"output_types":["JSON response with class label and confidence scores","HTTP status codes (200 for success, 429 for rate limit, 500 for server error)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-trustsafeai--radar-vicuna-7b__cap_3","uri":"capability://code.generation.editing.fine.tuning.on.custom.text.classification.datasets.with.adversarial.robustness.preservation","name":"fine-tuning on custom text classification datasets with adversarial robustness preservation","description":"Supports transfer learning by fine-tuning the pretrained RADAR-Vicuna-7B weights on custom labeled datasets while maintaining adversarial robustness properties. Uses standard supervised fine-tuning with optional adversarial example augmentation during training. The fine-tuning process leverages HuggingFace Trainer API with configurable learning rates, batch sizes, and adversarial training parameters. Preserves the RoBERTa backbone's robustness while adapting the classification head to new label spaces.","intents":["Adapt the model to custom classification tasks (domain-specific categories) while retaining adversarial robustness","Fine-tune on proprietary datasets without losing the robustness benefits of RADAR pretraining","Experiment with adversarial training intensity to balance accuracy and robustness on downstream tasks"],"best_for":["ML teams with labeled datasets (100s-10,000s of examples) for domain-specific classification","Organizations requiring both high accuracy and robustness for security-critical applications","Researchers studying adversarial robustness in text classification"],"limitations":["Requires labeled training data; no semi-supervised or few-shot learning built-in","Fine-tuning on small datasets (<100 examples) risks overfitting; requires careful regularization","Adversarial training increases training time by 2-3x compared to standard supervised fine-tuning","No automatic hyperparameter tuning; requires manual experimentation with learning rate, batch size, adversarial perturbation magnitude","GPU memory requirements scale with batch size; typical fine-tuning needs 16GB+ VRAM for batch_size=16"],"requires":["PyTorch 1.9+","Transformers 4.20+","Datasets library 2.0+ (for data loading)","Python 3.8+","GPU with 16GB+ VRAM (fine-tuning on CPU is impractical)","Labeled dataset in CSV/JSON format with text and label columns"],"input_types":["training dataset (text, label pairs)","validation dataset (optional, for early stopping)","hyperparameters (learning_rate, batch_size, num_epochs, adversarial_perturbation_magnitude)"],"output_types":["fine-tuned model weights (PyTorch checkpoint)","training metrics (loss, accuracy, F1 per epoch)","evaluation metrics on validation set"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-trustsafeai--radar-vicuna-7b__cap_4","uri":"capability://data.processing.analysis.interpretability.via.attention.visualization.and.token.level.attribution","name":"interpretability via attention visualization and token-level attribution","description":"Exposes attention weights from the RoBERTa transformer layers, enabling visualization of which input tokens the model attends to when making classification decisions. Supports extraction of attention patterns from multiple layers and heads, and can compute token-level attribution scores (e.g., via gradient-based methods or attention rollout) to identify which words most influence the final classification. Integrates with libraries like Captum or custom attribution scripts for deeper interpretability analysis.","intents":["Understand which words or phrases drive the model's classification decision for a given text","Debug misclassifications by visualizing attention patterns and identifying spurious correlations","Build trust in model predictions by providing human-interpretable explanations for end users"],"best_for":["Data scientists debugging model errors and understanding failure modes","Teams building explainable AI systems for regulated industries (finance, healthcare, legal)","Researchers studying attention mechanisms in adversarially-trained models"],"limitations":["Attention weights are not guaranteed to be faithful explanations; high attention to a token doesn't always mean causal importance","Visualization requires manual implementation or external libraries (Captum, BertViz); no built-in UI","Attribution methods (gradient-based, attention rollout) add 50-200ms overhead per sample","Interpretability is post-hoc; cannot modify model behavior based on attention patterns without retraining","Adversarial training may make attention patterns less interpretable due to distributed robustness across tokens"],"requires":["PyTorch 1.9+","Transformers 4.20+","Optional: Captum library for gradient-based attribution","Optional: BertViz for attention visualization","Python 3.8+"],"input_types":["text string","layer index (which transformer layer to extract attention from)","attribution method (attention, gradient, integrated_gradients)"],"output_types":["attention weight matrices (batch_size, num_heads, seq_len, seq_len)","token-level attribution scores (seq_len,)","visualization (HTML/image for attention heatmaps)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.9+","Transformers library 4.20+","Python 3.8+","Minimum 4GB RAM for inference; 16GB+ recommended for batch processing","CUDA 11.0+ for GPU acceleration (optional but recommended)","Transformers 4.20+","GPU with 8GB+ VRAM for batch_size >= 32 (CPU inference possible but slow)","HuggingFace account for Endpoints deployment","Azure subscription (for Azure ML deployment option)","HuggingFace Transformers 4.20+ (for local testing before deployment)"],"failure_modes":["Inference latency ~200-500ms per sample on CPU; requires GPU for batch processing >32 samples","Fixed vocabulary from RoBERTa tokenizer; out-of-vocabulary handling limited to subword fallback","No multi-label classification support — outputs single class prediction per input","Adversarial robustness gains come at ~5-10% accuracy cost on clean, non-adversarial test sets","Requires fine-tuning on downstream task; zero-shot classification performance is limited","Batch size limited by available GPU memory; typical max batch size 32-64 on 8GB VRAM","All texts in batch must be padded to longest sequence length, causing wasted computation on shorter texts","No streaming/online classification — requires full batch in memory before processing","Confidence thresholding is post-hoc; cannot dynamically adjust model behavior based on input difficulty","HuggingFace Endpoints incur per-request pricing (~$0.0001-0.001 per inference depending on model size)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6725033576070174,"quality":0.2,"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":"2026-05-03T14:23:00.976Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":1328536,"model_likes":9}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=trustsafeai--radar-vicuna-7b","compare_url":"https://unfragile.ai/compare?artifact=trustsafeai--radar-vicuna-7b"}},"signature":"Y8X4GX/d3e3w+cod1MiYHNBZjOo6pqDOtcYg3QSpWTzghKpqy7/RJaLlkpKHnybuG5OJwPzzqW9yqgwtsxm5Cg==","signedAt":"2026-06-22T04:17:25.870Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/trustsafeai--radar-vicuna-7b","artifact":"https://unfragile.ai/trustsafeai--radar-vicuna-7b","verify":"https://unfragile.ai/api/v1/verify?slug=trustsafeai--radar-vicuna-7b","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"}}