{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_taylor-ai","slug":"taylor-ai","name":"Taylor AI","type":"product","url":"https://www.trytaylor.ai","page_url":"https://unfragile.ai/taylor-ai","categories":["model-training"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_taylor-ai__cap_0","uri":"capability://automation.workflow.no.code.model.training.interface.with.dataset.upload.and.configuration","name":"no-code model training interface with dataset upload and configuration","description":"Provides a visual, form-based interface for non-ML practitioners to upload labeled datasets (CSV, JSON, or text formats), configure training hyperparameters (learning rate, batch size, epochs), and select base open-source model architectures without writing code. The platform abstracts away YAML configs, dependency management, and training loop implementation, translating UI selections into backend training jobs that execute on user-controlled infrastructure or managed cloud instances.","intents":["I want to train a custom model on my proprietary data without hiring ML engineers","I need to configure and launch a training job without understanding PyTorch or TensorFlow","I want to experiment with different model sizes and hyperparameters through a GUI rather than editing config files"],"best_for":["Non-technical product managers and domain experts with labeled datasets","Small teams without dedicated ML infrastructure or expertise","Organizations prioritizing time-to-model over state-of-the-art performance"],"limitations":["Abstraction layer may hide advanced tuning options (gradient accumulation, mixed precision, custom loss functions) that power users need","UI-driven configuration limits reproducibility compared to version-controlled code-based training scripts","No built-in experiment tracking or hyperparameter search — each training run requires manual configuration changes"],"requires":["Labeled dataset with 1000+ examples (minimum for meaningful fine-tuning)","CSV, JSON, or plain text format for input data","Access to Taylor AI platform (freemium account or paid tier)","Internet connectivity for UI interaction"],"input_types":["CSV files with text and label columns","JSON-L (newline-delimited JSON) with structured examples","Plain text files with one example per line"],"output_types":["Trained model checkpoint (PyTorch .pt or Hugging Face format)","Training metrics (loss curves, validation accuracy, F1 scores)","Model configuration file (JSON or YAML)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_taylor-ai__cap_1","uri":"capability://automation.workflow.local.and.on.premise.model.training.execution.with.data.residency.guarantees","name":"local and on-premise model training execution with data residency guarantees","description":"Executes training jobs on user-controlled infrastructure (on-premise servers, private cloud VPCs, or local machines) rather than Taylor AI's servers, ensuring training data never leaves the organization's network boundary. The platform provides containerized training environments (Docker images with pre-installed dependencies) and orchestration scripts that can be deployed to Kubernetes clusters, VMs, or bare metal, with encrypted communication back to the Taylor AI control plane for monitoring and artifact retrieval.","intents":["I need to train models on sensitive data that cannot leave our corporate network due to compliance (HIPAA, GDPR, SOC 2)","I want to avoid vendor lock-in by running training on infrastructure I control and own","I need to train on large datasets that would be expensive to transfer to cloud providers"],"best_for":["Enterprises with strict data residency and compliance requirements (healthcare, finance, government)","Organizations with existing on-premise GPU infrastructure seeking to maximize utilization","Teams prioritizing data sovereignty and long-term cost control over managed service convenience"],"limitations":["Requires operational overhead to manage containerized training environments, GPU drivers, and networking","No built-in auto-scaling — users must provision sufficient compute capacity upfront","Troubleshooting training failures requires access to logs and infrastructure monitoring, not delegated to platform support","Data transfer between on-premise storage and training containers must be manually configured (no built-in data pipeline)"],"requires":["Docker or Kubernetes runtime environment","GPU compute (NVIDIA CUDA 11.8+ or AMD ROCm) for practical training speeds","Network connectivity to Taylor AI control plane (for job submission and monitoring)","Linux-based infrastructure (Ubuntu 20.04+, CentOS 8+, or Kubernetes nodes)","Minimum 16GB RAM and 100GB storage per training node"],"input_types":["Local file paths to training data (mounted volumes or network storage)","S3-compatible object storage (MinIO, on-premise S3)","NFS or SMB network shares"],"output_types":["Model checkpoints written to local storage or mounted volumes","Training logs streamed to local file system or centralized logging (ELK, Splunk)","Metrics exported to Prometheus or custom monitoring systems"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_taylor-ai__cap_10","uri":"capability://tool.use.integration.api.based.model.serving.with.rate.limiting.authentication.and.usage.analytics","name":"api-based model serving with rate limiting, authentication, and usage analytics","description":"Hosts trained models as REST or gRPC APIs with built-in authentication (API keys, OAuth), rate limiting, request/response logging, and usage analytics (requests per day, latency percentiles, error rates). The platform provides SDKs for common languages (Python, JavaScript, Go) and handles scaling based on traffic, with optional caching for repeated requests and support for batch inference.","intents":["I want to expose my trained model as an API without building and managing API infrastructure","I need to monitor API usage and performance to understand how my model is being used in production","I want to control access to my model API through authentication and rate limiting"],"best_for":["Teams deploying models to production without dedicated backend infrastructure","Organizations requiring API-based model access for multiple applications or teams","Developers building applications that consume model predictions via standard HTTP/gRPC APIs"],"limitations":["API latency depends on network round-trip time and server load; not suitable for ultra-low-latency applications (< 10ms)","Rate limiting may throttle high-throughput applications; batch inference is recommended for bulk predictions","Usage analytics are aggregated; no per-user or per-application cost attribution without custom configuration","API versioning requires manual management; no automatic backward compatibility when model is updated"],"requires":["Trained model checkpoint","Internet connectivity for API access","API credentials (API key or OAuth token) for authentication","Client library or HTTP client for API calls"],"input_types":["Text input (for NLP models) or structured data (JSON)","Batch requests (multiple examples in single API call)"],"output_types":["Model predictions (text, probabilities, embeddings, etc.)","API response metadata (latency, model version, request ID)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_taylor-ai__cap_11","uri":"capability://data.processing.analysis.model.interpretability.and.explainability.analysis.for.predictions","name":"model interpretability and explainability analysis for predictions","description":"Provides tools to understand model predictions through feature importance analysis (SHAP, attention visualization), example-based explanations (similar training examples), and prediction confidence scores. For text models, the platform highlights which input tokens contributed most to the prediction; for classification models, it shows which features pushed the decision toward each class.","intents":["I need to understand why my model made a specific prediction for debugging or user explanation","I want to identify which features or input tokens are most important for my model's decisions","I need to detect potential biases in my model by analyzing predictions across different input groups"],"best_for":["Teams deploying models in regulated industries (finance, healthcare) requiring explainability for compliance","ML practitioners debugging model failures and understanding failure modes","Organizations building user-facing applications where users expect explanations for model decisions"],"limitations":["Explainability methods (SHAP, attention) add computational overhead; not suitable for real-time inference","Explanations may be misleading for complex models; feature importance doesn't always reflect causal relationships","No built-in bias detection; requires manual analysis of explanations across demographic groups","Interpretability is task-dependent; some explanation methods work better for classification than generation"],"requires":["Trained model checkpoint","Input examples to explain (text, structured data)","Training data for reference (for example-based explanations)"],"input_types":["Model prediction input (text, structured data)","Training data for reference examples"],"output_types":["Feature importance scores (SHAP values, attention weights)","Visualization (highlighted input tokens, feature importance plots)","Similar training examples (for example-based explanations)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_taylor-ai__cap_12","uri":"capability://automation.workflow.collaborative.model.development.with.team.access.control.and.audit.logging","name":"collaborative model development with team access control and audit logging","description":"Enables multiple team members to collaborate on model training and evaluation with role-based access control (read-only, editor, admin), audit logging of all changes (training runs, model updates, configuration changes), and commenting/annotation on training runs and model versions. The platform tracks who made which changes and when, supporting compliance requirements and enabling teams to understand model development history.","intents":["I want my team to collaborate on model training without giving everyone full access to sensitive infrastructure","I need an audit trail of all model changes for compliance and reproducibility","I want to annotate training runs with notes and decisions for future reference"],"best_for":["Teams with multiple ML practitioners working on the same models","Organizations requiring audit trails for compliance (financial services, healthcare)","Projects where model development decisions need to be documented and justified"],"limitations":["Role-based access control may be too coarse-grained; no fine-grained permissions (e.g., 'can view metrics but not download model')","Audit logging adds storage overhead; long-term retention of audit logs requires external storage","No built-in conflict resolution for concurrent training runs; users must coordinate to avoid duplicate work","Commenting and annotation are basic; no integration with external issue tracking or documentation systems"],"requires":["Taylor AI account with team management enabled","Team members with valid credentials","Role definitions (read-only, editor, admin)"],"input_types":["Team member email addresses and roles","Comments and annotations on training runs"],"output_types":["Audit log (CSV or JSON) with timestamps, user, and action details","Team access control configuration","Annotated training run history"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_taylor-ai__cap_2","uri":"capability://code.generation.editing.open.source.model.selection.and.architecture.customization","name":"open-source model selection and architecture customization","description":"Provides a curated catalog of open-source base models (LLaMA, Mistral, Falcon, BLOOM variants) that users can select for fine-tuning, with options to inspect and modify model architecture (layer count, attention heads, embedding dimensions) before training. The platform exposes model configuration as editable JSON/YAML, allowing users to create custom variants without forking the original codebase, and supports exporting modified architectures to standard Hugging Face format for portability.","intents":["I want to fine-tune a specific open-source model that fits my domain (e.g., domain-specific LLaMA variant for legal documents)","I need to reduce model size for edge deployment by pruning layers or reducing embedding dimensions","I want to experiment with different model architectures without committing to a single vendor's proprietary model"],"best_for":["ML-aware teams who understand model architecture tradeoffs and want customization beyond hyperparameter tuning","Organizations building edge AI applications requiring smaller, domain-specific models","Teams seeking to avoid lock-in by maintaining models in standard open-source formats (Hugging Face, ONNX)"],"limitations":["Architecture modifications require understanding of transformer internals (attention mechanisms, layer normalization, position embeddings) — not suitable for non-technical users","Custom architectures may not be compatible with all downstream tools and optimizations (quantization, distillation) designed for standard models","No automated validation of custom architectures — invalid configurations may only fail during training, wasting compute time","Limited guidance on architecture design tradeoffs (e.g., how reducing attention heads affects downstream task performance)"],"requires":["Familiarity with transformer model architecture and Hugging Face model config format","Understanding of model size/performance tradeoffs for your target hardware","Base model weights compatible with your infrastructure (e.g., 7B-13B models for consumer GPUs, 70B+ for enterprise clusters)"],"input_types":["Model architecture JSON configuration (Hugging Face config.json format)","Base model identifier (e.g., 'meta-llama/Llama-2-7b')","Custom model weights in PyTorch or SafeTensors format"],"output_types":["Modified model configuration file (JSON)","Custom model checkpoint with modified architecture","Hugging Face model card with architecture documentation"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_taylor-ai__cap_3","uri":"capability://automation.workflow.model.versioning.and.checkpoint.management.with.rollback.capability","name":"model versioning and checkpoint management with rollback capability","description":"Maintains a version history of trained model checkpoints, allowing users to compare metrics across training runs, revert to previous model versions, and manage multiple model variants (e.g., v1.0 for production, v1.1-experimental for A/B testing). The platform stores metadata (training date, hyperparameters, validation metrics, data version) alongside each checkpoint and provides APIs to query version history and download specific checkpoints for deployment or analysis.","intents":["I want to compare performance metrics across multiple training runs to identify the best model version","I need to roll back to a previous model version if a new training run degrades performance on production data","I want to maintain separate model versions for different use cases (e.g., classification vs. generation) without managing separate training pipelines"],"best_for":["Teams running iterative model improvement cycles with frequent retraining","Organizations requiring audit trails and reproducibility for compliance (financial services, healthcare)","ML practitioners comparing multiple training strategies and hyperparameter configurations"],"limitations":["Storage costs scale with number of checkpoints — large models (70B+ parameters) consume significant disk space per version","No built-in deduplication of checkpoint storage — identical model weights across versions are stored separately","Version comparison UI may be limited to high-level metrics (loss, accuracy) without deep-dive analysis of per-sample predictions","Rollback is manual — no automatic reversion based on performance thresholds or monitoring alerts"],"requires":["Sufficient storage capacity for multiple model checkpoints (minimum 50GB for 7B-parameter models, 500GB+ for 70B models)","Metadata tracking enabled during training (automatic, but requires database connectivity)","API credentials or UI access to retrieve checkpoint history"],"input_types":["Training run identifiers and date ranges","Metric filters (e.g., 'show versions with validation accuracy > 0.85')","Model variant tags or labels"],"output_types":["Version history table with metrics and timestamps","Model checkpoint files (PyTorch .pt or Hugging Face format)","Comparison reports (CSV or JSON) with side-by-side metrics"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_taylor-ai__cap_4","uri":"capability://automation.workflow.model.inference.and.deployment.with.multi.format.export","name":"model inference and deployment with multi-format export","description":"Enables trained models to be exported to multiple inference-ready formats (Hugging Face Transformers, ONNX, TensorRT, vLLM) and deployed to various inference engines without retraining or format conversion. The platform provides inference APIs (REST endpoints or gRPC) that can be hosted on Taylor AI infrastructure or user-controlled servers, with support for batching, streaming responses, and hardware acceleration (GPU, TPU, CPU optimization).","intents":["I want to deploy my trained model as a REST API without writing deployment code or managing containers","I need to export my model to ONNX format for inference on edge devices or in-browser execution","I want to compare inference latency and throughput across different hardware backends (GPU, CPU, TPU) before production deployment"],"best_for":["Teams deploying models to production without dedicated MLOps infrastructure","Organizations requiring model portability across multiple inference engines and hardware","Developers building applications that need low-latency inference (chatbots, real-time recommendations)"],"limitations":["Export to non-standard formats (ONNX, TensorRT) may require quantization or pruning, potentially degrading model accuracy","Inference latency depends on hardware and batch size — no automatic optimization recommendations","Multi-format export adds complexity; not all model architectures are compatible with all export targets (e.g., custom attention mechanisms may not export to ONNX)","Streaming inference (for LLMs) requires specific runtime support; not all export formats support token-by-token generation"],"requires":["Trained model checkpoint in Taylor AI format","Target inference framework (Hugging Face, ONNX Runtime, TensorRT, vLLM, etc.)","Hardware specifications for inference (GPU model, CPU cores, memory) if optimizing for specific targets","API credentials for hosted inference endpoints"],"input_types":["Model checkpoint file","Target export format specification (ONNX, TensorRT, vLLM, etc.)","Inference configuration (batch size, max sequence length, quantization settings)"],"output_types":["Exported model files (ONNX .onnx, TensorRT .engine, Hugging Face directory)","Inference API endpoint (REST or gRPC URL)","Performance benchmarks (latency, throughput, memory usage per hardware backend)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_taylor-ai__cap_5","uri":"capability://data.processing.analysis.data.preparation.and.labeling.workflow.with.quality.validation","name":"data preparation and labeling workflow with quality validation","description":"Provides tools for importing raw text data, applying data cleaning transformations (deduplication, tokenization, format normalization), and optionally integrating with labeling services (crowdsourcing platforms, internal annotation teams) to generate labeled datasets. The platform validates data quality (checking for label imbalance, missing values, outliers) and provides statistics (dataset size, class distribution, token count) to help users assess whether their data is sufficient for training.","intents":["I have raw text data but need to clean, deduplicate, and format it for training","I want to check if my dataset is large and balanced enough before investing in model training","I need to set up a labeling workflow for unlabeled data without building custom annotation infrastructure"],"best_for":["Teams with raw data but limited data engineering expertise","Organizations needing to validate dataset quality before committing to training","Projects requiring external labeling but wanting to avoid building custom annotation pipelines"],"limitations":["Data cleaning transformations are limited to common operations (deduplication, lowercasing, whitespace normalization) — complex domain-specific cleaning requires external preprocessing","Labeling integration may require manual setup with third-party platforms; no built-in crowdsourcing platform","Quality validation provides statistical summaries but not semantic analysis (e.g., detecting mislabeled examples or data drift)","No built-in active learning — cannot automatically identify which unlabeled examples would be most valuable to label"],"requires":["Raw text data in CSV, JSON, or plain text format","Minimum dataset size (1000+ examples recommended for meaningful fine-tuning)","For labeling workflows: access to labeling service (Mechanical Turk, Scale AI, internal team) or manual labeling capability"],"input_types":["CSV files with text column","JSON-L with text and optional metadata","Plain text files (one example per line)","Unlabeled text for labeling workflow integration"],"output_types":["Cleaned and deduplicated dataset (CSV or JSON-L)","Data quality report (dataset statistics, class distribution, token count)","Labeled dataset ready for training"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_taylor-ai__cap_6","uri":"capability://data.processing.analysis.model.performance.monitoring.and.evaluation.on.custom.test.sets","name":"model performance monitoring and evaluation on custom test sets","description":"Evaluates trained models on user-provided test datasets and generates detailed performance reports (accuracy, precision, recall, F1, confusion matrices for classification; BLEU, ROUGE, perplexity for generation tasks). The platform supports custom evaluation metrics via user-defined Python functions and tracks performance over time as models are retrained, enabling detection of performance degradation or drift.","intents":["I want to evaluate my trained model on a held-out test set to estimate real-world performance","I need to track how model performance changes across retraining iterations to detect regressions","I want to compute domain-specific evaluation metrics (e.g., legal document classification accuracy) beyond standard metrics"],"best_for":["Teams requiring quantitative performance validation before production deployment","Organizations tracking model performance over time for compliance and audit purposes","ML practitioners comparing multiple model variants or training strategies"],"limitations":["Evaluation is offline (batch) — no real-time performance monitoring on production inference","Custom metrics require Python coding; not accessible to non-technical users","No built-in statistical significance testing — cannot determine if performance differences are meaningful","Evaluation metrics may not correlate with business outcomes (e.g., high accuracy doesn't guarantee user satisfaction)"],"requires":["Test dataset in same format as training data (CSV, JSON-L, or text)","Ground truth labels for test examples","For custom metrics: Python 3.9+ and familiarity with metric function signatures"],"input_types":["Test dataset (CSV, JSON-L, or text format)","Model checkpoint to evaluate","Custom metric functions (Python code)"],"output_types":["Performance report (JSON or HTML) with standard metrics","Confusion matrix and per-class metrics (for classification)","Performance trends over time (CSV or visualization)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_taylor-ai__cap_7","uri":"capability://automation.workflow.fine.tuning.with.parameter.efficient.methods.lora.qlora.for.reduced.compute","name":"fine-tuning with parameter-efficient methods (lora, qlora) for reduced compute","description":"Implements parameter-efficient fine-tuning techniques (Low-Rank Adaptation, Quantized LoRA) that train only a small fraction of model parameters (1-5% of total) while keeping base model weights frozen, dramatically reducing memory and compute requirements. The platform automatically applies these techniques during training and stores only the small adapter weights, which can be merged with the base model at inference time or kept separate for modular deployment.","intents":["I want to fine-tune a large model (70B+ parameters) on consumer hardware without expensive GPUs","I need to train multiple task-specific adapters on the same base model without duplicating model weights","I want to reduce training time and memory usage while maintaining model quality"],"best_for":["Teams with limited GPU memory or compute budget seeking to fine-tune large models","Organizations building multi-task systems requiring separate adapters for different domains","Researchers experimenting with model adaptation without access to enterprise-scale infrastructure"],"limitations":["Parameter-efficient methods may reduce model quality compared to full fine-tuning, especially for significant domain shifts","Adapter merging at inference time adds latency; keeping adapters separate requires runtime support for adapter loading","Quantization (QLoRA) introduces additional approximation error; not suitable for tasks requiring maximum accuracy","Limited guidance on adapter rank selection (r parameter) — incorrect choices may bottleneck performance"],"requires":["Base model compatible with LoRA (most transformer models supported)","GPU with minimum 8GB VRAM (vs. 40GB+ for full fine-tuning of 70B models)","Understanding of LoRA rank and alpha hyperparameters for optimal results"],"input_types":["Training dataset (same format as full fine-tuning)","LoRA configuration (rank, alpha, target modules)"],"output_types":["Adapter weights file (small, typically 10-100MB for 7B-70B models)","Merged model checkpoint (if merging adapters with base model)","Training metrics (loss, validation accuracy)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_taylor-ai__cap_8","uri":"capability://automation.workflow.multi.gpu.and.distributed.training.orchestration.with.automatic.scaling","name":"multi-gpu and distributed training orchestration with automatic scaling","description":"Automatically distributes training across multiple GPUs (data parallelism, tensor parallelism) or multiple machines (distributed training via PyTorch DDP or DeepSpeed) without requiring users to modify training code or understand distributed training concepts. The platform detects available hardware, configures communication backends (NCCL for GPU, Gloo for CPU), and handles gradient synchronization and checkpointing across nodes.","intents":["I want to train a large model faster by using multiple GPUs without learning distributed training frameworks","I need to scale training across a GPU cluster as my dataset grows","I want to reduce training time from weeks to days without rewriting training code"],"best_for":["Teams with access to multi-GPU infrastructure seeking to accelerate training","Organizations training large models (13B+ parameters) where single-GPU training is impractical","ML practitioners wanting distributed training benefits without framework expertise"],"limitations":["Distributed training adds communication overhead — scaling efficiency degrades with more GPUs (typically 70-90% efficiency with 8 GPUs, 50-70% with 16+)","Requires high-bandwidth interconnects (NVLink, InfiniBand) for optimal performance; standard Ethernet may bottleneck","Debugging distributed training failures is complex — synchronization issues and deadlocks are difficult to diagnose","Automatic scaling may not be optimal for all model architectures; some models benefit from specific parallelism strategies (e.g., pipeline parallelism for very large models)"],"requires":["Multiple GPUs (2+ for meaningful speedup) or multi-node cluster","NVIDIA CUDA 11.8+ or AMD ROCm for GPU training","High-bandwidth network connectivity between nodes (for multi-node training)","Sufficient memory per GPU (distributed training reduces per-GPU memory usage but doesn't eliminate it)"],"input_types":["Training dataset (same format as single-GPU training)","Model and training configuration"],"output_types":["Trained model checkpoint (same format as single-GPU training)","Training logs with per-GPU metrics and communication overhead statistics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_taylor-ai__cap_9","uri":"capability://automation.workflow.model.quantization.and.compression.for.edge.deployment.and.inference.optimization","name":"model quantization and compression for edge deployment and inference optimization","description":"Reduces model size and inference latency through quantization (INT8, INT4, mixed precision) and pruning techniques, enabling deployment to edge devices (mobile, IoT) or reducing inference costs on cloud infrastructure. The platform provides post-training quantization (no retraining required) and quantization-aware training (QAT) options, with automatic calibration on representative data and validation to ensure accuracy loss is acceptable.","intents":["I want to deploy my model to mobile devices or edge hardware with limited memory and compute","I need to reduce inference latency and cost by compressing the model without significant accuracy loss","I want to compare accuracy/latency tradeoffs across different quantization strategies"],"best_for":["Teams building mobile or edge AI applications with strict latency/memory constraints","Organizations seeking to reduce inference costs by compressing models for cloud deployment","ML practitioners optimizing models for specific hardware targets (mobile SoCs, embedded GPUs)"],"limitations":["Quantization introduces approximation error; INT4 quantization may degrade accuracy by 2-5% depending on model and task","Post-training quantization is simpler but less accurate than QAT; QAT requires retraining and more compute","Quantized models may not be compatible with all inference engines; INT4 support is limited compared to INT8","Calibration data selection is critical for quantization quality; insufficient or unrepresentative calibration data leads to poor results"],"requires":["Trained model checkpoint","Calibration dataset (representative examples for quantization calibration)","Target hardware specifications (for optimization recommendations)","Acceptable accuracy loss threshold (e.g., 'no more than 1% accuracy drop')"],"input_types":["Model checkpoint","Calibration dataset (CSV, JSON-L, or text)","Quantization configuration (bit width, strategy)"],"output_types":["Quantized model checkpoint (INT8, INT4, or mixed precision)","Accuracy/latency comparison report","Deployment-ready model for target hardware (ONNX, TensorRT, CoreML, etc.)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Labeled dataset with 1000+ examples (minimum for meaningful fine-tuning)","CSV, JSON, or plain text format for input data","Access to Taylor AI platform (freemium account or paid tier)","Internet connectivity for UI interaction","Docker or Kubernetes runtime environment","GPU compute (NVIDIA CUDA 11.8+ or AMD ROCm) for practical training speeds","Network connectivity to Taylor AI control plane (for job submission and monitoring)","Linux-based infrastructure (Ubuntu 20.04+, CentOS 8+, or Kubernetes nodes)","Minimum 16GB RAM and 100GB storage per training node","Trained model checkpoint"],"failure_modes":["Abstraction layer may hide advanced tuning options (gradient accumulation, mixed precision, custom loss functions) that power users need","UI-driven configuration limits reproducibility compared to version-controlled code-based training scripts","No built-in experiment tracking or hyperparameter search — each training run requires manual configuration changes","Requires operational overhead to manage containerized training environments, GPU drivers, and networking","No built-in auto-scaling — users must provision sufficient compute capacity upfront","Troubleshooting training failures requires access to logs and infrastructure monitoring, not delegated to platform support","Data transfer between on-premise storage and training containers must be manually configured (no built-in data pipeline)","API latency depends on network round-trip time and server load; not suitable for ultra-low-latency applications (< 10ms)","Rate limiting may throttle high-throughput applications; batch inference is recommended for bulk predictions","Usage analytics are aggregated; no per-user or per-application cost attribution without custom configuration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:33.648Z","last_scraped_at":"2026-04-05T13:23:42.559Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=taylor-ai","compare_url":"https://unfragile.ai/compare?artifact=taylor-ai"}},"signature":"RHnjyWbkN028FGA4wBeE3xjfvGjGb086zjIkALckuFDtGctaUcOLJZgCs7RpaRzKftv+p9T5c/8NQ/xahPMaBw==","signedAt":"2026-06-20T03:42:44.119Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/taylor-ai","artifact":"https://unfragile.ai/taylor-ai","verify":"https://unfragile.ai/api/v1/verify?slug=taylor-ai","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"}}