Training Prerequisites & Validation ↗

    eval-protocol create dataset my-dataset --file dataset.jsonl
    ```

Or via the [Fireworks dashboard](https://app.fireworks.ai/dashboard/datasets).
  </Accordion>

  <Accordion title="Evaluator created">
Your reward function must be tested and uploaded. For local evaluators, upload via pytest:

```bash
    cd evaluator_directory
    pytest my-evaluator-name.py -vs
    ```

The test automatically registers your evaluator with Fireworks. For remote environment testing, deploy your HTTP service first.
  </Accordion>

  <Accordion title="Fireworks API key configured">
Set your API key as an environment variable:

```bash
    export FIREWORKS_API_KEY="fw_your_api_key_here"
    ```

Or store it in a `.env` file in your project directory.
  </Accordion>

  <Accordion title="Base model selected">
Choose a base model that supports fine-tuning. Popular options:

* `accounts/fireworks/models/llama-v3p1-8b-instruct` - Good balance of quality and speed
* `accounts/fireworks/models/qwen3-0p6b` - Fast training for experimentation
* `accounts/fireworks/models/llama-v3p1-70b-instruct` - Best quality, slower training

Check available models at [fireworks.ai/models](https://fireworks.ai/models).
  </Accordion>
</AccordionGroup>

## Job validation

Before starting training, Fireworks validates your configuration:

<AccordionGroup>
  <Accordion title="Dataset format validation">
* ✅ Valid JSONL format
* ✅ Each line has `messages` array
* ✅ Messages have `role` and `content` fields
* ✅ File size within limits
* ❌ Missing fields → error with specific line numbers
* ❌ Invalid JSON → syntax error details
  </Accordion>

  <Accordion title="Evaluator validation">
* ✅ Evaluator code syntax is valid
* ✅ Required dependencies are available
* ✅ Entry point function exists
* ✅ Test runs completed successfully
* ❌ Import errors → missing dependencies
* ❌ Syntax errors → code issues
  </Accordion>

  <Accordion title="Resource availability">
* ✅ Sufficient GPU quota
* ✅ Base model supports fine-tuning
* ✅ Account has RFT permissions
* ❌ Insufficient quota → request increase
* ❌ Invalid model → choose different base model
  </Accordion>

  <Accordion title="Parameter validation">
* ✅ Parameters within valid ranges
* ✅ Compatible parameter combinations
* ❌ Invalid ranges → error with allowed values
* ❌ Conflicting options → resolution guidance
  </Accordion>
</AccordionGroup>

If validation fails, you'll receive specific error messages with instructions to fix the issues.

## Common errors and fixes

<AccordionGroup>
  <Accordion title="Invalid dataset format">
**Error**: `Dataset validation failed: invalid JSON on line 42`

**Fix**:

1. Open your JSONL file
2. Check line 42 for JSON syntax errors
3. Common issues: missing quotes, trailing commas, unescaped characters
4. Validate JSON at jsonlint.com

**Error**: `Missing required field 'messages'`

**Fix**: Each dataset row must have a `messages` array:

```json
    {"messages": [{"role": "user", "content": "..."}]}
    ```
  </Accordion>

  <Accordion title="Evaluator not found">
**Error**: `Evaluator 'my-evaluator' not found in account`

**Fix**:

1. Upload your evaluator first:
   ```bash
       cd evaluator_directory
       pytest my-evaluator-name.py -vs
       ```
2. Or specify evaluator ID if using UI:
   * Check [Evaluators dashboard](https://app.fireworks.ai/dashboard/evaluators)
   * Copy exact evaluator ID
  </Accordion>

  <Accordion title="Insufficient quota">
**Error**: `Insufficient GPU quota for this job`

**Fix**:

1. Check your current quota at [Account Settings](https://app.fireworks.ai/account/settings)
2. Request a quota increase through the dashboard
3. Or choose a smaller base model to reduce GPU requirements
  </Accordion>

  <Accordion title="Parameter out of range">
**Error**: `Learning rate 1e-2 outside valid range [1e-5, 5e-4]`

**Fix**: Adjust the parameter to be within the allowed range:

```bash
    --learning-rate 1e-4  # Use default value
    ```

See [CLI Reference](tools-sdks/firectl/commands/reinforcement-fine-tuning-job-create) for all available parameters.
  </Accordion>

  <Accordion title="Evaluator build timeout">
**Error**: `Evaluator build timed out after 10 minutes`

**Fix**:

1. Check build logs in [Evaluators dashboard](https://app.fireworks.ai/dashboard/evaluators)
2. Common issues:
   * Large dependencies taking too long to install
   * Network issues downloading packages
   * Syntax errors in requirements.txt
3. Wait for build to complete, then retry launching your job
4. Consider splitting large dependencies or using lighter alternatives
  </Accordion>
</AccordionGroup>

## What happens after launching

Once your job is created, here's what happens:

<span class="steps-start"></span>
  <span class="step-marker" data-step-title="Job queued"></span>
Your job enters the queue and waits for available GPU resources. Queue time depends on current demand.

**Status**: `PENDING`
  <span class="step-end"></span>

  <span class="step-marker" data-step-title="Dataset validation"></span>
Fireworks validates your dataset to ensure it meets format requirements and quality standards. This typically takes 1-2 minutes.

**Status**: `VALIDATING`
  <span class="step-end"></span>

  <span class="step-marker" data-step-title="Training starts"></span>
The system begins generating rollouts, evaluating them, and updating model weights. You'll see:

* Rollout generation and evaluation
* Reward curves updating in real-time
* Training loss decreasing

**Status**: `RUNNING`
  <span class="step-end"></span>

  <span class="step-marker" data-step-title="Monitor progress"></span>
Track training via the dashboard. See [Monitor Training](/fine-tuning/monitor-training) for details on interpreting metrics and debugging issues.

**Status**: `RUNNING` → `COMPLETED`
  <span class="step-end"></span>

  <span class="step-marker" data-step-title="Job completes"></span>
When training finishes, your fine-tuned model is ready for deployment.

**Status**: `COMPLETED`

Next: [Deploy your model](/fine-tuning/deploying-loras) for inference.
  <span class="step-end"></span>
<span class="steps-end"></span>

## Next steps

<span class="card-group-start" data-cols="3"></span>
  <span class="card-start" data-card-title="Launch with CLI" data-card-icon="terminal" data-card-href="/fine-tuning/cli-reference"></span>
Use eval-protocol CLI for fast, scriptable launches
  <span class="card-end"></span>

  <span class="card-start" data-card-title="Launch with Web UI" data-card-icon="browser" data-card-href="/fine-tuning/web-ui-guide"></span>
Use the dashboard for visual, guided job creation
  <span class="card-end"></span>

  <span class="card-start" data-card-title="Monitor training" data-card-icon="chart-line" data-card-href="/fine-tuning/monitor-training"></span>
Track job progress, inspect rollouts, and debug issues
  <span class="card-end"></span>
<span class="card-group-end"></span>