CLI Overview

Entrenar provides two command-line tools for training, research, and benchmarking:

• entrenar - Main CLI for training, model operations, and research workflows
• entrenar-bench - Specialized tool for distillation benchmarking and cost analysis

Installation

Both tools are installed when you add entrenar to your project:

cargo install entrenar

Main CLI Commands

entrenar <COMMAND> [OPTIONS]

Commands:
  train      Train a model from YAML configuration
  validate   Validate a configuration file without training
  info       Display information about a configuration
  init       Generate a YAML configuration template
  quantize   Quantize a model
  merge      Merge multiple models
  research   Academic research artifacts and workflows
  bench      Run latency benchmarks
  inspect    Inspect model/data files
  audit      Run bias, fairness, privacy, or security audits
  monitor    Monitor for data drift
  completion Generate shell completions

Global Options

Quick Examples

Training

# Train from YAML config
entrenar train config.yaml

# Train with overrides
entrenar train config.yaml --epochs 10 --lr 0.001

# Dry run (validate only)
entrenar train config.yaml --dry-run

Model Operations

# Quantize a model
entrenar quantize model.safetensors --output model_q4.json --bits 4

# Merge models with TIES
entrenar merge model1.safetensors model2.safetensors --output merged.safetensors --method ties

# Merge with SLERP
entrenar merge model1.safetensors model2.safetensors --output merged.safetensors --method slerp --weight 0.7

Research Workflows

# Initialize a research artifact
entrenar research init --id my-dataset --title "My Dataset" --author "Alice Smith"

# Generate citation
entrenar research cite artifact.yaml --year 2024 --format bibtex

# Create RO-Crate package
entrenar research bundle artifact.yaml --output ./package --zip

Benchmarking

# Run latency benchmark
entrenar bench config.yaml --warmup 5 --iterations 100

# Benchmark with multiple batch sizes
entrenar bench config.yaml --batch-sizes 1,8,32,64

# JSON output for CI
entrenar bench config.yaml --format json

Data & Model Inspection

# Inspect SafeTensors model
entrenar inspect model.safetensors

# Inspect with verbose tensor details
entrenar inspect model.safetensors -v

# Inspect data file
entrenar inspect data/train.parquet --mode summary

Auditing

# Bias audit
entrenar audit predictions.parquet --type bias --threshold 0.8

# Security audit
entrenar audit model.safetensors --type security

# Privacy audit
entrenar audit data.parquet --type privacy

Drift Monitoring

# Monitor for data drift
entrenar monitor data/current.parquet --threshold 0.2

# Monitor with baseline
entrenar monitor data/current.parquet --baseline data/training.parquet

# JSON output for alerting
entrenar monitor data/current.parquet --format json

Shell Completions

# Generate bash completions
entrenar completion bash > ~/.local/share/bash-completion/completions/entrenar

# Generate zsh completions
entrenar completion zsh > ~/.zsh/completions/_entrenar

# Generate fish completions
entrenar completion fish > ~/.config/fish/completions/entrenar.fish

Benchmark CLI Commands

entrenar-bench <COMMAND> [OPTIONS]

Commands:
  temperature       Sweep temperature hyperparameter
  alpha             Sweep alpha hyperparameter
  compare           Compare distillation strategies
  ablation          Run ablation study
  cost-performance  Analyze cost vs performance trade-offs
  recommend         Recommend configurations based on constraints

Quick Examples

# Temperature sweep
entrenar-bench temperature --start 1.0 --end 8.0 --step 0.5

# Compare strategies
entrenar-bench compare --strategies kd,progressive,attention

# Cost-performance analysis
entrenar-bench cost-performance --gpu a100-80gb

# Get recommendations
entrenar-bench recommend --max-cost 50 --min-accuracy 0.85

Output Formats

Both CLIs support multiple output formats:

Environment Variables

Exit Codes

See Also

• Research Commands - Academic research CLI reference
• Benchmark Commands - Distillation benchmarking CLI reference
• Inspect Command - Model and data inspection
• Audit Command - Bias, fairness, privacy, security audits
• Monitor Command - Drift detection and monitoring
• Completion Command - Shell completion generation
• Declarative Training - YAML configuration