CLI Reference¶

xaytune installs a xaytune command-line tool for running training, evaluation, export, and component listing from the terminal.

xaytune [command] [options]

Global Options¶

Flag	Description
`--version`	Print xaytune version and exit
`--help`	Show help message

train¶

Run a training recipe from a YAML config file.

xaytune train --config <path> [--override key=value ...] [--resume] [--dry-run]

Option	Required	Description
`--config`	Yes	Path to a YAML config file
`--override`	No	Config override in dot notation (repeatable)
`--resume`	No	Resume training from the last checkpoint
`--dry-run`	No	Validate and print the resolved config as JSON without training

Examples¶

# Basic training run
xaytune train --config configs/examples/lora_finetune.yaml

# Override model and learning rate
xaytune train --config configs/examples/lora_finetune.yaml \
    --override model.name=mistralai/Mistral-7B-v0.3 \
    --override trainer.learning_rate=1e-4

# Validate config without training
xaytune train --config configs/examples/lora_finetune.yaml --dry-run

# Resume from checkpoint
xaytune train --config configs/examples/lora_finetune.yaml --resume

eval¶

Evaluate a model on benchmarks or a custom dataset.

xaytune eval --model <path> [--benchmarks <list>] [--dataset <path>] [--metrics <list>] [--num-fewshot <n>]

Option	Required	Description
`--model`	Yes	Model path or Hugging Face Hub name
`--benchmarks`	No	Comma-separated benchmark names (e.g., `mmlu,gsm8k`)
`--dataset`	No	Path to a JSONL evaluation dataset
`--metrics`	No	Comma-separated metric names (e.g., `loss,perplexity`)
`--num-fewshot`	No	Number of few-shot examples for benchmarks

Note

Provide either --benchmarks or --dataset, not both.

Examples¶

# Benchmark evaluation
xaytune eval --model output/my-finetune --benchmarks mmlu,gsm8k --num-fewshot 5

# Custom dataset evaluation
xaytune eval --model output/my-finetune --dataset data/eval.jsonl --metrics loss,perplexity

export¶

Export and convert models. Has three subcommands: merge, gguf, and push.

export merge¶

Merge LoRA adapters into the base model.

xaytune export merge --checkpoint <path> --output <path>

Option	Required	Description
`--checkpoint`	Yes	Path to a LoRA/QLoRA checkpoint directory
`--output`	Yes	Output directory for the merged model

export gguf¶

Convert a model to GGUF format.

xaytune export gguf --model <path> --output <path> [--quant <type>]

Option	Required	Default	Description
`--model`	Yes	--	Path to model directory
`--output`	Yes	--	Output GGUF file path
`--quant`	No	`Q4_K_M`	Quantization type

export push¶

Push a model to the Hugging Face Hub.

xaytune export push --model <path> --repo <repo>

Option	Required	Description
`--model`	Yes	Path to model directory
`--repo`	Yes	HF Hub repo name (e.g., `username/model-name`)

Examples¶

# Full export pipeline
xaytune export merge --checkpoint output/lora-finetune --output output/merged
xaytune export gguf --model output/merged --output model.gguf --quant Q5_K_M
xaytune export push --model output/merged --repo username/my-model

compare¶

Compare two models side-by-side on the same benchmarks.

xaytune compare <model_a> <model_b> --benchmarks <list> [--num-fewshot <n>]

Option	Required	Description
(positional)	Yes	Exactly two model paths
`--benchmarks`	Yes	Comma-separated benchmark names
`--num-fewshot`	No	Number of few-shot examples

Example¶

xaytune compare output/model-a output/model-b --benchmarks mmlu,gsm8k,hellaswag

Output is a table showing scores for each model across all benchmark metrics.

list¶

List registered components (recipes, data formats, metrics, reward functions).

xaytune list [type]

Argument	Required	Description
`type`	No	Component type: `recipes`, `formats`, `metrics`, `rewards`

If no type is given, all registries are listed.

Examples¶

# List everything
xaytune list

# List only data formats
xaytune list formats

# List only metrics
xaytune list metrics

Example Config Files¶

xaytune ships example configs in configs/examples/:

File	Description
`full_finetune.yaml`	Full parameter fine-tuning
`lora_finetune.yaml`	LoRA fine-tuning
`qlora_finetune.yaml`	QLoRA fine-tuning
`pretrain.yaml`	Pre-training
`dpo_align.yaml`	DPO alignment
`grpo_align.yaml`	GRPO alignment
`orpo_align.yaml`	ORPO alignment
`simpo_align.yaml`	SimPO alignment
`ppo_align.yaml`	PPO alignment
`reinforce_align.yaml`	REINFORCE alignment