CH·02CLI reference
gpu fine-tune create
Create a fine-tuning job
Create a fine-tuning job
Synopsis
Submits a fine-tuning job. Provide an already-uploaded dataset with --training-file, or pass a local JSONL with --dataset to upload it first. With --wait-timeout>0 the CLI polls the job until it reaches a terminal status (succeeded/failed/cancelled); --wait-timeout=0 (the default) returns immediately so you can track progress with gpu fine-tune get and gpu fine-tune events.
gpu fine-tune create [flags]
Examples
# Upload a local dataset and start a QLoRA job in one step
gpu fine-tune create --model qwen2.5-7b-instruct --dataset ./train.jsonl --method qlora
# Use a previously uploaded file id
gpu fine-tune create --model qwen2.5-7b-instruct --training-file file-abc123
# Tune the LoRA hyperparameters and cap GPU-hour spend
gpu fine-tune create --model qwen2.5-7b-instruct --dataset ./train.jsonl \
--lora-r 32 --lora-alpha 64 --lora-dropout 0.1 --max-budget 25
# Block until the job finishes (poll up to 2 hours)
gpu fine-tune create --model qwen2.5-7b-instruct --dataset ./train.jsonl --wait-timeout 2h
Options
--dataset string Local JSONL dataset to upload before creating the job
--gpu-preference string Optional GPU preference hint
-h, --help help for create
--lora-alpha int LoRA alpha (default 32)
--lora-dropout float LoRA dropout (default 0.05)
--lora-r int LoRA rank (r) (default 16)
--max-budget float Max GPU-hour budget in USD (0 = no cap)
--method string Tuning method: lora | qlora (default "qlora")
--model string Tunable base model id (e.g., qwen2.5-7b-instruct) — required
--training-file string Already-uploaded training file id
--wait-timeout duration How long to poll the job for a terminal status; 0 = don't poll, return immediately
Options inherited from parent commands
--api-base string API base URL (override with GPUAI_API_BASE env) (default "https://api.gpu.ai/v1")
--debug Enable debug logging to stderr
-o, --output string Output format: table|json (default table on TTY, json otherwise)
SEE ALSO
- gpu fine-tune - Manage fine-tuning jobs