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The Together AI platform records metrics at every training and evaluation step. You can pull them during a job to watch loss curves live, or after the job completes to compare runs.

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Poll until the job is done

A fine-tuning job moves through the states: pending → queued → running → uploading → completed. Queue wait is typically under an hour but varies with platform load. Once a job is running, multiply the duration of the first epoch by n_epochs to estimate remaining training time. Use this loop to poll until the job reaches a terminal state, then fetch the metrics. The terminal states are completed, error, and cancelled. 
import os
import time
from together import Together

client = Together()

job_id = "ft-xxxx-yyyy"
deadline = time.time() + 6 * 60 * 60  # safety cap: 6 hours

while True:
    job = client.fine_tuning.retrieve(id=job_id)
    print(job.status)
    if job.status in ("completed", "error", "cancelled"):
        break
    if time.time() > deadline:
        raise TimeoutError(f"Job {job_id} still {job.status} after 6 hours")
    time.sleep(60)

if job.status != "completed":
    raise RuntimeError(f"Job ended with status: {job.status}")

metrics = client.fine_tuning.list_metrics(job_id)
for step in metrics.metrics:
    print(step)
Expected job durations: A small LoRA job on an 8B model with under 1,000 examples typically completes in 10 to 30 minutes after queue. A full job on a 70B model with hundreds of thousands of examples can take several hours. Save your job ID: you can poll from any session without re-uploading data.

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Retrieve metrics

The list_metrics call returns every recorded step. The CLI renders ASCII charts by default; pass --json to get raw output. 
metrics = client.fine_tuning.list_metrics("<JOB_ID>")
for step in metrics.metrics:
    print(step)

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Filter by step or time

All filter parameters are optional. Omit them to retrieve every recorded step. 
from datetime import datetime

metrics = client.fine_tuning.list_metrics(
    "<JOB_ID>",
    global_step_from=100,
    global_step_to=500,
    logged_at_from=datetime.fromisoformat("2026-01-01T00:00:00+00:00"),
    logged_at_to=datetime.fromisoformat("2026-01-02T00:00:00+00:00"),
)

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Downsample with resolution

For long runs, pass resolution to cap the response at a fixed number of uniformly sampled training steps. Eval metrics are always returned in full regardless of this setting. 
metrics = client.fine_tuning.list_metrics("<JOB_ID>", resolution=50)

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Sample output

Training and eval steps are returned as separate objects. Training steps contain train/* keys, eval steps contain eval/*. When both fire at the same step, both objects appear: 
[
  { "timestamp": 1779196193564587000, "train/global_step": 1, "train/epoch": 0.1, "train/loss": 2.43, "train/grad_norm": 1.21, "train/learning_rate": 1e-5 },
  { "timestamp": 1779196253564587000, "train/global_step": 2, "train/epoch": 0.2, "train/loss": 2.11, "train/grad_norm": 0.94, "train/learning_rate": 9e-6 },
  { "timestamp": 1779196313564587000, "train/global_step": 3, "train/epoch": 0.3, "train/loss": 1.98, "train/grad_norm": 0.87, "train/learning_rate": 8e-6 },
  { "timestamp": 1779196314564587000, "train/global_step": 3, "train/epoch": 0.3, "eval/loss": 2.05 }
]

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Parameters

ParameterTypeDescription
global_step_fromintegerReturn only metrics with global_step ≥ this value.
global_step_tointegerReturn only metrics with global_step ≤ this value.
logged_at_fromstring or datetimeReturn only metrics logged at or after this ISO 8601 timestamp.
logged_at_tostring or datetimeReturn only metrics logged at or before this ISO 8601 timestamp.
resolutionintegerMaximum number of uniformly sampled training metric points. Does not affect eval metrics.

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Available metrics

Every job reports train/global_step, train/loss, train/grad_norm, train/learning_rate, and timestamp. When you supply validation_file and set n_evals > 0, the response also includes eval/loss and other validation metrics.

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Preference-tuning jobs

A DPO job emits everything above and adds reward and divergence metrics to the same list_metrics payload. They show up as extra train/* keys during training and, when evaluation is enabled, matching eval/* keys:
  • Reward and accuracy: The reward assigned to the preferred and non-preferred responses, plus the share of examples where the preferred reward is higher.
  • KL divergence: How far the trained model’s output distribution has drifted from the reference model.
  • Per-side log probabilities: Separate values for the preferred and non-preferred outputs, useful for debugging stalled runs.
Retrieval, filtering, and downsampling work the same way regardless of method. For what these values mean and how to interpret them during a run, see DPO metrics.

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Stream to Weights & Biases

Pass wandb_api_key when creating the job to mirror these metrics to your W&B workspace in real time. See the quickstart for the call structure.