AI Firewall and guardrails for LLM-based Elixir applications

Interactive Phoenix LiveView demonstrations of the Crucible Framework - showcasing ensemble voting, request hedging, statistical analysis, and more with mock LLMs

Fairness and bias detection library for Elixir AI/ML systems

Intermediate Representation for the Crucible ML reliability ecosystem

Industrial ML training orchestration - backend-agnostic workflow engine for supervised, reinforcement, and preference learning. Provides composable workflows, declarative stage DSL, comprehensive telemetry, and port/adapter patterns for any ML backend. The missing orchestration layer that makes ML cookbooks trivially thin.

Data validation and quality library for ML pipelines in Elixir

ML model deployment for the Crucible ecosystem. vLLM and Ollama integration, canary deployments, A/B testing, traffic routing, health checks, rollback strategies, and inference serving for Elixir-based ML workflows.

Experimental research framework for running AI benchmarks at scale

Explainable AI (XAI) tools for the Crucible framework

Request hedging for tail latency reduction in distributed systems

Metrics aggregation and alerting for ML experiments—multi-backend export (Prometheus, InfluxDB, Datadog, OpenTelemetry), advanced aggregations (percentiles, histograms, moving averages), threshold-based alerting with anomaly detection (z-score, IQR), and time-series storage. Research-grade observability for the NSAI ecosystem.

Model evaluation harness for standardized benchmarking—comprehensive metrics (F1, BLEU, ROUGE, METEOR, BERTScore, pass@k), statistical analysis (confidence intervals, effect size, bootstrap CI, ANOVA), multi-model comparison, and report generation. Research-grade evaluation for LLM and ML experiments.

Phoenix LiveView dashboard for the Crucible ML reliability stack

ML model registry for the Crucible ecosystem. Artifact storage, model versioning, lineage tracking, metadata management, model comparison, reproducibility, and integration with training pipelines for Elixir-based ML workflows.

Statistical testing and analysis framework for AI research

Adversarial testing and robustness evaluation for the Crucible framework

CrucibleFramework: A scientific platform for LLM reliability research on the BEAM

ML training orchestration for the Crucible ecosystem. Distributed training, hyperparameter optimization, checkpointing, model versioning, metrics collection, early stopping, LR scheduling, gradient accumulation, and mixed precision training with Nx/Scholar integration.

Structured causal reasoning chain logging for LLM transparency

Multi-model ensemble voting strategies for LLM reliability

HuggingFace Datasets for Elixir - A native Elixir port of the popular HuggingFace datasets library. Stream, load, and process ML datasets from the HuggingFace Hub with full BEAM/OTP integration. Supports Parquet streaming, dataset splitting, shuffling, and seamless integration with Nx tensors for machine learning workflows.

Dataset management and caching for AI research benchmarks

No description provided.

🚀 Accelerate ML training on the BEAM with CrucibleTrain's unified infrastructure for diverse model types and workflows.

Dataset management library for ML experiments—loaders for SciFact, FEVER, GSM8K, HumanEval, MMLU, TruthfulQA, HellaSwag; git-like versioning with lineage tracking; transformation pipelines; quality validation with schema checks and duplicate detection; GenStage streaming for large datasets. Built for reproducible AI research.

Advanced telemetry collection and analysis for AI research

Training IR for reproducible ML jobs across Crucible and Kitchen. Defines model specs, adapters, learning config, checkpointing, validation, and resource envelopes to standardize training pipelines.

ML feedback loop management for the Crucible ecosystem. Quality monitoring, data drift detection, model performance tracking, data curation, active learning, human-in-the-loop workflows, and continuous improvement for Elixir-based ML.