Train

Model training and fine-tuning

The train command enables model training and fine-tuning capabilities.

Usage

praisonai train [OPTIONS] [DATASET]

Arguments

Argument	Description
DATASET	Training dataset path

Options

Option	Short	Description	Default
--model	-m	Base model to fine-tune	gpt-4o-mini
--verbose	-v	Verbose output	false

Examples

Train with dataset

praisonai train training_data.jsonl

Train with specific model

praisonai train data.jsonl --model gpt-4o

Agent Training

Train agents through iterative feedback loops with the train agents subcommand.

Usage

praisonai train agents [OPTIONS] [AGENT_FILE]

Options

Option	Short	Description	Default
--iterations	-n	Number of training iterations	3
--human	-h	Use human feedback instead of LLM grading	false
--scenarios	-s	Path to scenarios JSON file	-
--input	-i	Single input text for training	-
--expected	-e	Expected output for the input	-
--output	-o	Output directory for training data	-
--model	-m	LLM model for grading	gpt-4o-mini
--storage-backend	-	Storage backend: file, sqlite, or redis://url	file
--storage-path	-	Path for storage backend	-

Examples

# Simple training with single input
praisonai train agents --input "What is Python?"

# Training with expected output
praisonai train agents --input "What is 2+2?" --expected "4"

# Training with scenarios file
praisonai train agents --scenarios scenarios.json

# Human feedback mode
praisonai train agents --input "Explain AI" --human

# More iterations
praisonai train agents --input "Hello" --iterations 5

# With agent file
praisonai train agents my_agent.yaml --scenarios scenarios.json

Storage Backend Options

Store training data in different backends:

# SQLite backend (recommended for production)
praisonai train agents --input "Hello" --storage-backend sqlite --storage-path ~/.praisonai/train.db

# Redis backend (for distributed systems)
praisonai train agents --input "Hello" --storage-backend redis://localhost:6379

# File backend (default)
praisonai train agents --input "Hello" --storage-backend file --storage-path ~/.praisonai/train

See Also

• Eval - Evaluation and testing
• Storage Backends - Pluggable storage backends