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Kick off and manage model training runs from the command line. Discover recipes and trainable base models, queue new runs (with override flags for the trickier trainingConfig knobs), cancel in-flight jobs, and zoom in on errors without scrolling through raw logs. Alias: inf train The full training loop is paste-able from the terminal: 
# 1. Materialize training and eval datasets
inf dataset create -n my-train-split -t training --file ./train.jsonl
inf dataset create -n my-eval-split  -t eval     --file ./eval.jsonl
# → Datasets ds_trn_abc12 / ds_evl_def34 created.

# 2. Create (or reuse) a rubric the evals will score against
inf eval rubric create -n my-rubric -f ./rubric.md
# → Rubric rub_xyz56 / version rv_ver78 created.

# 3. Pick a recipe and base model
inf training recipes
inf training models

# 4. Queue the training run
inf training create \
  --name distill-hardreasoning-qwen3.5-4b-v2 \
  --recipe inf-public-training-recipe:qwen-3.5-4b-fft \
  --training-dataset ds_trn_abc12 \
  --eval-dataset     ds_evl_def34 \
  --rubric           rub_xyz56 \
  --sample-packing   false \
  --num-epochs       5 \
  --task-id distill-hardreasoning-v2
# → Training job job_90ab12 queued.

# 5. Track progress until it finishes
inf training poll job_90ab12

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inf training models

Discover the base models you can fine-tune and (with --judge) the judge models that can score checkpoints. 
inf training models

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Options

FlagRequiredDescriptionDefault
--judgeNoList judge models instead of base models (use with --judge-model)Off
The table shows each model’s canonical alias, full name, and ID prefix. Pair with --json when scripting to preserve full IDs — those full IDs are what you pass to --base-model / --judge-model on inf training create.

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Examples

# List base models you can fine-tune
inf training models

# List judge models instead
inf training models --judge

# Dump full IDs for scripting
inf training models --json | jq -r '.[].id'

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inf training recipes

Recipes bundle a base model + judge model + GPU plan + full trainingConfig. inf training recipes lists everything visible to the active project (public recipes + the project’s own recipes). 
inf training recipes

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Options

FlagRequiredDescriptionDefault
--include-archivedNoInclude archived recipesOff
--public-onlyNoShow only public recipesOff
--project-onlyNoShow only the active project’s recipesOff
Only super-admins can fork a public recipe into a project recipe. If you need to customize a recipe’s trainingConfig, use the override flags on inf training create rather than trying to clone the recipe.

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inf training recipes get

Inspect a specific recipe, including its full trainingConfig. Useful for spotting knobs you may want to override at queue time. 
inf training recipes get <id>

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Arguments

ArgumentRequiredDescription
idYesRecipe ID

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Examples

# List recipes visible to the active project
inf training recipes

# Inspect a specific recipe (including its trainingConfig)
inf training recipes get inf-public-training-recipe:qwen-3.5-4b-fft

# Only show project-owned recipes
inf training recipes --project-only

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inf training create

Queue a new training run. You can either specify a recipe (recommended — it pre-fills base model, judge, GPU plan, and trainingConfig) or pass individual flags. Override flags let you tweak specific trainingConfig fields without forking the recipe. 
inf training create \
  --name <name> \
  --training-dataset <id> \
  --eval-dataset <id> \
  --rubric <id>
Alias: inf training queue

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Options

FlagRequiredDescriptionDefault
-n, --name <name>YesDisplay name for the run—
--training-dataset <id>YesTraining-type dataset ID (also accepts --training-dataset-id)—
--eval-dataset <id>YesEval-type dataset ID (also accepts --eval-dataset-id)—
--rubric <id>YesRubric ID (also accepts --rubric-id)—
--rubric-version-id <id>NoPin to a specific rubric versionLatest version
--recipe <id>NoTraining recipe ID — pre-fills base model, judge, GPU plan, and trainingConfig (also --recipe-id)—
--base-model <id>NoBase model to fine-tune (overrides the recipe, also --base-model-id)Recipe default
--judge-model <id>NoJudge model for evals (overrides the recipe, also --judge-model-id)Recipe default
--num-nodes <n>NoNumber of training nodesRecipe default
--gpus-per-node <n>NoGPUs per node (1–8)Recipe default
--task-id <id>NoAssociate the run with an existing task—
--sample-packing <bool>NoOverride trainingConfig.sample_packing (true/false)Recipe default
--num-epochs <n>NoOverride trainingConfig.num_epochsRecipe default
--max-steps <n>NoOverride trainingConfig.max_steps (use -1 for “no cap”)Recipe default
--learning-rate <n>NoOverride trainingConfig.learning_rateRecipe default
--dry-runNoPrint the exact tRPC payload that would be sent and exit — nothing is createdOff
GPU hardware selection is managed server-side and is not configurable from the CLI. When --rubric-version-id is omitted, the CLI fetches the latest version of --rubric before queueing — pin a version if you need reproducibility across re-runs. Prints the new training job ID along with an inf training poll <id> follow-up command. The run status starts as queued while datasets are prepared, then moves to running.

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Escape hatches for recipe-pinned configs

The public recipes are opinionated: sample_packing is often true, and num_epochs / max_steps are tuned for the typical dataset size. When your dataset is small or shaped differently, those defaults can cause torchrun to crash (for example, with args.max_steps must be set to a positive value if dataloader does not have a length, was -1 — which is what happens when sample packing collapses a tiny dataset into fewer batches than the FSDP shard count). The --sample-packing, --num-epochs, --max-steps, and --learning-rate flags give you override knobs without needing to fork the recipe (only super-admins can). Pair them with --dry-run to sanity-check the resulting payload before burning a job slot.

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Examples

# Minimal queue with a recipe
inf training create \
  --name distill-v1 \
  --recipe inf-public-training-recipe:qwen-3.5-4b-fft \
  --training-dataset ds_trn_abc12 \
  --eval-dataset     ds_evl_def34 \
  --rubric           rub_xyz56

# Override recipe defaults for a small dataset and verify the payload first
inf training create \
  --name distill-hardreasoning-qwen3.5-4b-v2 \
  --recipe inf-public-training-recipe:qwen-3.5-4b-fft \
  --training-dataset ds_trn_abc12 \
  --eval-dataset     ds_evl_def34 \
  --rubric           rub_xyz56 \
  --sample-packing false \
  --num-epochs 5 \
  --dry-run

# Use the `queue` alias
inf training queue \
  --name distill-v1 \
  --base-model model_abc \
  --judge-model model_xyz \
  --training-dataset ds_trn_abc12 \
  --eval-dataset     ds_evl_def34 \
  --rubric           rub_xyz56

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inf training list

Display a table of training runs in the active project. 
inf training list
Alias: inf training ls

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Options

FlagRequiredDescriptionDefault
-s, --status <status>NoFilter by status: exporting_datasets, queued, running, cycling, completed, failed, cancelled, or timed_outAll statuses
-l, --limit <n>NoMaximum number of results20
--offset <n>NoOffset for pagination0
The table shows the run ID (8-char prefix), name, status (color-coded), base model, progress (currentStep/totalSteps), current loss, and creation date.

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Examples

# Show only running training jobs
inf training list --status running

# Get the first 50 training runs
inf training list --limit 50

# Page through results
inf training list --limit 20 --offset 40

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inf training get

View detailed information about a specific training run. Without --error, prints the full detail view. With --error, dumps only the fields that matter when a run crashed — ideal for triaging failed jobs from CI or a script. 
inf training get <id>

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Arguments

ArgumentRequiredDescription
idYesThe training job ID

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Options

FlagRequiredDescriptionDefault
--errorNoPrint only the status, status detail, and error message in a highlighted block (for failed runs)Off

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Output

The default detail view includes every field on the training job:
FieldDescription
id, nameRun identifier and display name
statusOne of the status values listed under inf training list
baseModelIdBase model the run fine-tunes from
adapterAdapter type (e.g. LoRA)
currentStep / totalStepsProgress counters
currentLossMost recent training loss
numNodesNumber of nodes participating in the run
provider / providerRunIdUnderlying training provider and their internal run ID
evalDatasetName / rubricNameEval configuration (if the run is configured for mid-training evals)
filteredDatasetNameTraining dataset name
startedAt / completedAt / createdAtLifecycle timestamps
statusDetail / errorMessagePopulated when the run fails or ends in a non-success state
With --error, the output collapses to just status, statusDetail, and errorMessage.

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Examples

# Full detail view
inf training get job_abc123

# Just the error payload for a failed run
inf training get job_abc123 --error

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inf training cancel

Cancel a queued or running training job. Completed and already-cancelled jobs will reject the call. 
inf training cancel <id>

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Arguments

ArgumentRequiredDescription
idYesThe training job ID

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Options

FlagRequiredDescriptionDefault
-y, --yesYes in non-TTY environmentsSkip the confirmation promptOff
In an interactive terminal, the CLI asks for confirmation unless -y is passed. In non-TTY environments (CI, scripts) the command refuses to run without -y.

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Examples

# Cancel interactively
inf training cancel job_abc123

# Cancel non-interactively (required in CI)
inf training cancel job_abc123 --yes

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inf training logs

Stream or view log entries for a training run. Logs are color-coded by level, and each line is prefixed with the training phase it came from (torchrun_init, training, inference_export, …) so you can tell setup crashes apart from training crashes at a glance. 
inf training logs <id>

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Arguments

ArgumentRequiredDescription
idYesThe training job ID

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Options

FlagRequiredDescriptionDefault
-l, --limit <n>NoMaximum number of log entries50
--level <level>NoFilter by log level (e.g. error, warn, info)All levels
--phase <phase>NoFilter by training phase (substring match — e.g. torchrun_init, training)No filter
-f, --followNoContinuously poll for new logs (like tail -f)Off
Log entries are timestamped and color-coded: errors in red, warnings in yellow, info in blue. In follow mode, the CLI polls for new logs every 3 seconds until you press Ctrl+C.

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Examples

# View the last 50 log entries
inf training logs job_abc123

# Stream logs in real time
inf training logs job_abc123 --follow

# Only show error logs
inf training logs job_abc123 --level error

# Only show setup-phase logs (everything before training starts)
inf training logs job_abc123 --phase torchrun_init

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inf training poll

Wait for a training run to complete, printing status updates as the status changes. 
inf training poll <id>

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Arguments

ArgumentRequiredDescription
idYesThe training job ID

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Options

FlagRequiredDescriptionDefault
-i, --interval <seconds>NoPoll interval in seconds10
The CLI prints a status line each time the status changes, showing the status, progress, and current loss. It exits automatically when the run reaches completed, failed, cancelled, or timed_out. On a failed exit, the CLI points you at inf training get <id> --error for the full error payload.

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Examples

# Poll every 10 seconds (default)
inf training poll job_abc123

# Poll every 30 seconds
inf training poll job_abc123 --interval 30