Skip to main content
The Fireworks dashboard provides a visual interface for creating RFT jobs with guided parameter selection. Perfect for first-time users and exploring options.
Before launching, review Training Prerequisites & Validation for requirements, validation checks, and common errors.

​
When to use Web UI

Start with the UI to learn the options, then switch to CLI for faster iteration and automation.
FeatureCLI (eval-protocol)Web UI
Best forExperienced users, automationFirst-time users, exploration
Parameter discoveryNeed to know flag namesGuided with tooltips
SpeedFast - single commandSlower - multiple steps
AutomationEasy to script and reproduceManual process
Batch operationsEasy to launch multiple jobsOne at a time
ReproducibilityExcellent - save commandsManual tracking needed

​
Launch training via Web UI

1

Navigate to Fine-Tuning

  1. Go to Fireworks Dashboard
  2. Click Fine-Tuning in the left sidebar
  3. Click Fine-tune a Model
Fine-tuning dashboard showing list of jobs
2

Select Reinforcement Fine-Tuning

  1. Choose Reinforcement as the tuning method
  2. Select your base model from the dropdown
The UI shows only models that support fine-tuning. Popular choices appear at the top.
Not sure which model to choose? Start with llama-v3p1-8b-instruct for a good balance of quality and speed.
3

Configure Dataset

  1. Upload new dataset or select existing from your account
  2. Preview dataset entries to verify format
  3. The UI validates your JSONL format automatically
Dataset selection interface
Each dataset row should have messages array:
{
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is 25 * 4?"}
  ]
}
4

Select Evaluator

  1. Choose from your uploaded evaluators
  2. Preview evaluator code and test results
  3. View recent evaluation metrics
If you haven’t uploaded an evaluator yet, you’ll need to do that first via CLI:
pytest test_evaluator.py -vs
For remote evaluators, you’ll enter your server URL in the environment configuration section.
5

Set Training Parameters

Configure how the model learns:Core parameters:
  • Output model name: Custom name for your fine-tuned model
  • Epochs: Number of passes through the dataset (start with 1)
  • Learning rate: How fast the model updates (use default 1e-4)
  • LoRA rank: Model capacity (8-16 for most tasks)
  • Batch size: Training throughput (use default 32k tokens)
The UI shows helpful tooltips for each parameter. See Parameter Tuning for detailed guidance.
6

Configure Rollout Parameters

Control how the model generates responses during training:
  • Temperature: Sampling randomness (0.7 for balanced exploration)
  • Top-p: Probability mass cutoff (0.9-1.0)
  • Top-k: Token candidate limit (40 is standard)
  • Number of rollouts (n): Responses per prompt (4-8 recommended)
  • Max tokens: Maximum response length (2048 default)
Higher temperature and more rollouts increase exploration but also cost.
7

Review and Launch

  1. Review all settings in the summary panel
  2. See estimated training time and cost
  3. Click Start Fine-Tuning to launch
The dashboard will redirect you to the job monitoring page where you can track progress in real-time.

​
Next steps

Prerequisites & Validation

Review requirements, validation, and common errors

Monitor training

Track job progress, inspect rollouts, and debug issues

Parameter tuning

Learn how to adjust parameters for better results