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Crate entrenar

Crate entrenar 

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entrenar has moved to aprender-train.

This crate re-exports aprender-train for backward compatibility. New code should depend on aprender-train directly.

Modules§

aprender_compat
Aprender Compatibility Layer
autograd
Tape-based autograd engine
cli
CLI module for entrenar
config
Declarative YAML configuration
dashboard
Dashboard Module (Phase 2: ENT-003, ENT-004)
decision
Decision pattern storage and CITL trainer module
distill
Knowledge Distillation
ecosystem
Ecosystem Integration (Phase 9)
efficiency
Efficiency & Cost Tracking Module (ENT-008 through ENT-012)
error
Error types for Entrenar
eval
Model Evaluation Framework (APR-073)
finetune
Fine-tuning pipeline for code generation models
generative
Generative Models for Code Synthesis
gpu
GPU resource management and multi-node training infrastructure.
inference
Inference — model inference and serving utilities
integrity
Behavioral Integrity & Lineage Module (ENT-013, ENT-014, ENT-015)
io
Model I/O - Loading and saving models
lora
LoRA (Low-Rank Adaptation) implementation
merge
Model merging methods (TIES, DARE, SLERP)
moe
Mixture of Experts (MoE) layer
monitor
Real-time Training Monitoring Module
numerical
Numerical utilities for numerically stable computation
optim
Optimizers for training neural networks
pipeline
Pipeline — standardized connectors for training pipelines
prune
Neural network pruning integration for Entrenar
quality
Quality Gates Module (ENT-005, ENT-006, ENT-007)
quant
Quantization: QAT and PTQ
research
Academic Research Artifacts (Phase 7)
run
Run Struct with Renacer Integration (ENT-002)
safety
Panic safety and graceful degradation
search
MCTS (Monte Carlo Tree Search) for Code Generation
server
REST/HTTP API Server (#67)
sovereign
Sovereign Deployment Module (ENT-016 through ENT-018)
sovereign_array
Sovereign array types — Vec-backed replacements for ndarray Array1/Array2.
staging
Model Staging Workflows (GH-70)
storage
Experiment Storage Module (ENT-001)
tokenizer
Subword Tokenization Module (#26)
trace
Training Trace Module (ITP-SPEC-001)
tracking
Experiment Tracking Module (GH-31)
train
High-level training loop
training
Training — top-level training module re-exports
transformer
Transformer module with full model implementation and weight loading
yaml_mode
YAML Mode Training - Declarative, No-Code Training Interface

Macros§

array
Macro to create Array2 from nested arrays (replaces ndarray::array!)

Structs§

Context
Context for managing the computational graph
Tensor
Tensor with automatic differentiation support

Enums§

Error

Functions§

backward
Perform backward pass on a tensor

Type Aliases§

Result