> ## Documentation Index
> Fetch the complete documentation index at: https://docs.together.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Training metrics

> Retrieve and visualize per-step training metrics for a fine-tuning job.

The Together platform records metrics at every training and evaluation step. You can retrieve them at any point during or after a job — useful for tracking loss curves, diagnosing runs, and comparing experiments.

## Retrieve metrics

<CodeGroup>
  ```python Python theme={null}
  import os
  from together import Together

  client = Together(api_key=os.environ.get("TOGETHER_API_KEY"))

  response = client.fine_tuning.list_metrics("ft-your-job-id")
  for step in response.metrics:
      print(step)
  ```

  ```shell Shell theme={null}
  together fine-tuning list-metrics "ft-your-job-id"
  ```
</CodeGroup>

## Output format

By default, the CLI renders ASCII charts. Use `--json` to get raw JSON output instead.

```shell Shell theme={null}
# ASCII charts (default)
together fine-tuning list-metrics "ft-your-job-id"

# JSON output
together fine-tuning list-metrics "ft-your-job-id" --json

# Save JSON to a file
together fine-tuning list-metrics "ft-your-job-id" --json > metrics.json

# Save ASCII charts to a file
together fine-tuning list-metrics "ft-your-job-id" > plots.txt
```

The JSON output is a list of metric objects. Training and eval steps are returned as separate objects: training steps contain `train/*` keys, while eval steps contain `eval/*` keys. When an eval checkpoint occurs at the same step as a training step, both objects appear for that step:

```json theme={null}
[
  {"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 },
  ...
]
```

## Filter by step or time

All filter parameters are optional. Omit them to retrieve all recorded metrics.

<CodeGroup>
  ```python Python theme={null}
  from datetime import datetime

  response = client.fine_tuning.list_metrics(
      "ft-your-job-id",
      global_step_from=100,
      global_step_to=500,
      logged_at_from=datetime.fromisoformat("2024-01-01T00:00:00+00:00"),
      logged_at_to=datetime.fromisoformat("2024-01-02T00:00:00+00:00"),
  )
  ```

  ```shell Shell theme={null}
  together fine-tuning list-metrics "ft-your-job-id" \
    --global-step-from 100 \
    --global-step-to 500 \
    --logged-at-from 2024-01-01T00:00:00 \
    --logged-at-to 2024-01-02T00:00:00 \
    --json
  ```
</CodeGroup>

## Downsample with resolution

For long training runs, use `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.

<CodeGroup>
  ```python Python theme={null}
  response = client.fine_tuning.list_metrics(
      "ft-your-job-id",
      resolution=50,
  )
  ```

  ```shell Shell theme={null}
  together fine-tuning list-metrics "ft-your-job-id" \
    --resolution 50 \
    --json
  ```
</CodeGroup>

## Parameters

| Parameter          | Type               | Description                                                                                         |
| ------------------ | ------------------ | --------------------------------------------------------------------------------------------------- |
| `global_step_from` | integer            | Return only metrics with `global_step` ≥ this value.                                                |
| `global_step_to`   | integer            | Return only metrics with `global_step` ≤ this value.                                                |
| `logged_at_from`   | string or datetime | Return only metrics logged at or after this ISO 8601 timestamp.                                     |
| `logged_at_to`     | string or datetime | Return only metrics logged at or before this ISO 8601 timestamp.                                    |
| `resolution`       | integer            | Maximum number of uniformly sampled training metric points to return. Does not affect eval metrics. |

## Available metrics

Every job reports `train/global_step`, `train/loss`, `timestamp`, and related training metrics. When you supply a `validation_file` and set `n_evals > 0`, the response also includes `eval/loss` and other validation metrics. [Preference fine-tuning](/docs/preference-fine-tuning) jobs additionally report reward, accuracy, KL divergence, and log-probability metrics for both preferred and non-preferred responses.