CLI

Installation

The CLI is included with the Python SDK:

pip install fleethq

Authentication

fleet login     # authenticate via browser
fleet logout    # remove saved credentials
fleet whoami    # show current account

Models

List uploaded base models:

fleet models

Show a model:

fleet models <model_id>

Download a model (returns presigned URLs for all files):

fleet models download <model_id>

Delete a model:

fleet models delete <model_id>

Fine-tuned models

List models produced by training jobs:

fleet fine-tunes

Show a fine-tuned model:

fleet fine-tunes <model_id>

Delete a fine-tuned model:

fleet fine-tunes delete <model_id>

Checkpoints

List all checkpoints:

fleet checkpoints

List checkpoints for a specific job:

fleet checkpoints <job_id>

Delete a checkpoint:

fleet checkpoints delete <model_id>

Datasets

List uploaded datasets:

fleet datasets

Show a dataset:

fleet datasets <dataset_id>

Upload a dataset file:

fleet datasets upload ./train.jsonl
fleet datasets upload ./train.jsonl my-dataset-name

Download a dataset (returns a presigned URL):

fleet datasets download <dataset_id>

Delete a dataset:

fleet datasets delete <dataset_id>

Jobs

List recent jobs:

fleet jobs

Show a job with live status:

fleet jobs <job_id>

Estimate cost and runtime before launching a job:

fleet jobs estimate <model_id> <dataset_id>
fleet jobs estimate <model_id> <dataset_id> --method qlora

Prints a table of all tiers with estimated runtime range, cost range, and the recommended tier marked with *. Available methods: full, lora (default), qlora.

Stream logs for a job:

fleet jobs logs <job_id>

Show training metrics:

fleet jobs metrics <job_id>

Show evaluation results (eval jobs only):

fleet jobs eval <job_id>

Prints loss, perplexity, number of samples evaluated, and wall-clock duration.

Cancel a running job:

fleet jobs cancel <job_id>

Delete a job record:

fleet jobs delete <job_id>

Deployments

List deployments:

fleet deployments

Deploy a model:

fleet deployments create <model_id>
fleet deployments create <model_id> fleet:standard

Run inference on a deployment:

fleet deployments infer <deployment_id> "What is LoRA?"

Delete a deployment:

fleet deployments delete <deployment_id>

Billing

Show account balance, storage usage, and recent activity:

fleet billing

Show paginated transaction history:

fleet billing transactions
fleet billing transactions --page=2 --limit=50

Settings

Show current account settings:

fleet settings

Update a setting:

fleet settings set <key> <value>

Setting	Default	Description
`max_checkpoints`	`1`	Maximum checkpoints kept per job. Oldest deleted automatically when exceeded.
`prune_checkpoints_on_completion`	`true`	Delete all checkpoints when a job completes successfully.
`auto_recharge_enabled`	`false`	Automatically top up balance when it drops below the threshold.
`auto_recharge_threshold`	`10.00`	Balance level (USD) that triggers an auto-recharge.
`auto_recharge_amount`	`25.00`	Amount (USD) to charge when auto-recharge triggers.

auto_recharge_status is read-only, visible in fleet settings output. Values: no_card, ok, pending, failed.

Hardware tiers

Tier	GPU memory	Best for
`fleet:cpu`	CPU only	Testing, tiny models
`fleet:micro`	6–12 GB	Small models, quick tests
`fleet:economy`	16–20 GB	GPT-2, small Llama variants
`fleet:standard`	24–32 GB	Most fine-tunes (default)
`fleet:pro`	40–48 GB	Large models, longer context
`fleet:ultra`	80 GB+	Frontier models

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