Praison AI

List Checkpoints

Discover what a training run saved without ls

List the checkpoints a training run saved — no ls, no guessing the path.

bash
praisonai-train checkpoints
praisonai-train checkpoints -d outputs --json
Diagram

praisonai-train checkpoints

📁 output_dir

🔍 scan checkpoint-N

📏 sum file sizes

✅ newest step first

Quick Start

  1. List the checkpoints

    Point at the run’s output directory and see every saved step, newest first.

    bash
    praisonai-train checkpoints -d outputs
    text
    checkpoint-1000   4.21 GB   outputs/checkpoint-1000
checkpoint-200    4.21 GB   outputs/checkpoint-200
2 checkpoint(s); newest is step 1000.
  2. Get JSON for scripts

    Add --json to emit a machine-readable array your shell can parse.

    bash
    praisonai-train checkpoints -d outputs --json
    json
    [
  {"step": 1000, "path": "outputs/checkpoint-1000", "bytes": 4521984000},
  {"step": 200, "path": "outputs/checkpoint-200", "bytes": 4521984000}
]

How It Works

The command scans output_dir for checkpoint-<n> directories, sums each one’s file sizes, and sorts by step — newest first.

Diagram
Filesystempraisonai-train checkpointsUserFilesystempraisonai-train checkpointsUsercheckpoints -d outputsscan for checkpoint-<n>matching directoriessum st_size of every filebytes per checkpointsort by step (newest first)listing + summary line

Only directories named checkpoint-<n> count — checkpoint-final is skipped, not read as step 0. Sorting is numeric, so checkpoint-1000 comes before checkpoint-200.

Configuration Options

OptionShortTypeDefaultDescription
--model-dir-dPathoutputsDirectory the run wrote to (i.e. its output_dir).
--json-jboolFalseEmit a JSON array instead of the human listing.

JSON Output

Each entry has three fields — the step number, the checkpoint path, and its size in bytes.

FieldTypeDescription
stepintThe checkpoint’s step number (the <n> in checkpoint-<n>).
pathstrPath to the checkpoint directory.
bytesintSize on disk — sum of st_size of every file under the checkpoint.
json
{"step": 1000, "path": "outputs/checkpoint-1000", "bytes": 4521984000}

Common Patterns

Auto-pick the newest step for export

Pipe --json through jq to grab the newest checkpoint path, then hand it to export.

bash
NEWEST=$(praisonai-train checkpoints -d outputs --json | jq -r '.[0].path')
praisonai-train export ollama --model-dir "$NEWEST" --ollama me/my-model

The list is sorted newest-first, so .[0] is always the latest step.

Confirm a run actually saved

Run it right after an interrupted job to see what survived before deciding whether to resume.

bash
praisonai-train checkpoints -d outputs

Best Practices

Run against output_dir, not final_model_dir

Checkpoints live under the run’s output_dir (default outputs), not final_model_dir (default lora_model). Point -d at the same output_dir the run used.

Use save_total_limit to control disk

Each checkpoint is the full adapter size. Set save_total_limit in config.yaml to keep only the newest few — the listing then stays short and disk stays bounded.

Combine –json with jq in shell scripts

--json gives a stable {step, path, bytes} schema. Parse it with jq to auto-select a checkpoint instead of hardcoding a step number that changes every run.

Related

Checkpointing

Config keys — save_strategy, save_steps, save_total_limit, resume_from_checkpoint.

 Export a trained model

Publish a chosen checkpoint to HF, GGUF, or Ollama.

 Infer CLI

Prompt a model you just trained and watch it stream.

 Train CLI

Full flag reference for every subcommand.