Fireworks AI fine-tuning: prepare data and compare runs
Prepare a supervised fine-tuning experiment on Fireworks, follow the documented job steps, and compare the result with a saved baseline.
Updated 5 min read
For Fireworks AI fine-tuning, choose a supported base model, prepare a training dataset, create a job, and evaluate the result on examples excluded from training. Fireworks’ training overview (opens in a new tab) distinguishes managed training from its Training API. Keep the dataset references, job settings and observed results in fenbs, so another person or AI can continue the experiment without mistaking a completed training run for a proven improvement.
What should you decide before training?
Define one behavior you want to improve. For example, suppose an internal writing helper must turn a public product description into a short catalog entry with three required fields. Decide which fields are required, what an acceptable answer contains, and which mistakes would make the answer unusable.
Run those checks on the untuned base model first. This gives you a baseline: a result to compare with the trained version. Save the actual outputs, including awkward ones. Otherwise, it is easy to remember the first model as worse than it was and the latest run as better than the evidence shows.
Use the AI model guides and our guide to choosing AI models for selection questions. This page focuses on a controlled training experiment. Our ChatGPT API training guide covers a different provider and should not supply the commands for a Fireworks job.
How should you prepare the dataset?
The Fireworks supervised fine-tuning guide (opens in a new tab) documents JSONL datasets: one JSON object on each line, with a messages array. Supervised fine-tuning means training from examples of the response you want. Check the supported base models and current dataset requirements in that guide before uploading anything.
- Choose examples that show the same task you plan to evaluate, including ordinary and difficult cases.
- Review the desired answers for accuracy and consistency. Remove examples you do not have permission to use.
- Prepare the documented JSONL message format, keeping each example on its own line.
- Set aside separate evaluation prompts that you will not use to teach the desired answers.
- Give the dataset a version reference and record what changed from the previous version.
For the catalog example, keep the required fields consistent across examples. If one answer contains unsupported product claims and another carefully avoids them, resolve that disagreement before training. More examples do not resolve contradictory instructions. Use public or invented content for your first format check.
Where do you start and inspect a job?
In the documented web workflow, go to Training > New job and follow the wizard. Then open the job details to monitor progress. You need a Fireworks account, supported model access and training capacity; do not assume every model or training route has the same capabilities.
For the command-line route, the SFT guide gives the commands below. These assume your approved dataset is already available and you have configured the tool and credentials. Replace each placeholder with its real nonsecret identifier; do not paste the angle-bracket text unchanged.
firectl sftj create --base-model <MODEL_ID> --dataset <DATASET_ID> --output-model <FINE_TUNED_MODEL_ID>
firectl sftj get <JOB_ID>
Record the job ID returned by the creation step. Give each experiment its own output model reference so the record clearly identifies what you tested. Do not describe commands copied into a note as commands you ran; add the observed state after you inspect the actual job.
What belongs in the record for each run?
Fireworks exposes job artifacts and configuration, but a training job is not established as a general-purpose notebook. Keep a small experiment record alongside those references. The table below is a suggested record structure, not a set of dashboard field names.
| Keep | Why it matters |
|---|---|
| Dataset version and dataset ID | Tells another person which examples were used. |
| Base model ID and output model ID | Identifies what you started with and what you evaluated afterward. |
| Job ID and settings | Points to the actual run rather than a remembered configuration. |
| Held-out prompts and expected behavior | Keeps the comparison independent of the examples used for training. |
| Observed results and next step | Distinguishes a measured improvement from a promising but unchecked idea. |
Keep sensitive training content out of shared work notes. Link to an approved location or retain only the dataset identifier and a description that your team is allowed to share. A fenbs record can keep the experiment’s purpose and next step available without copying the underlying dataset into another tool.
How do you compare the trained model fairly?
Use the same held-out prompts and judging rules for the base and trained versions. Check the behavior you decided mattered before training. If the catalog helper produces valid fields but invents product details, the format improvement is real and the factual failure remains. Record both rather than reducing the result to “better.”
Fireworks’ evaluation guide (opens in a new tab) separates evaluation from production serving. Follow the route appropriate to your training artifacts. For serverless Training API work, sampler checkpoints and promoted model IDs are different artifacts; keep the exact reference for what you tested.
If the model seems to learn the wrong text or ignore the intended answer, the SFT guide documents Render Samples on job details for inspecting token IDs and loss masks. These show how the example was represented and which parts contributed to training. Inspect the examples before launching another run with guessed settings.
Our AI model evaluation guide covers useful scoring checks, while AI model comparison covers the eventual choice. If a new run changes both the data and several settings, label it accordingly; you cannot attribute its outcome to one change alone.
What remains after a promising result?
A useful experiment still needs a serving decision. Fireworks warns that preemptible evaluation deployments can disappear; they are not a production availability guarantee. Its training guidance directs production use toward dedicated serving. Confirm the current serving instructions for the artifact you plan to keep.
Then test the application that will call the model, including its handling of incomplete or unusable answers. Use the separate Fireworks API guide for the coding-client or application connection. Fine-tuning the response behavior does not automatically preserve the decisions behind your experiment.
Fireworks also documents external tools through MCP, a standard way for an AI to use an outside service, in its Responses guide (opens in a new tab). That capability does not establish compatibility with fenbs’ browser sign-in. Use a supported assistant separately when saving the work record.
Never lose the work you did in an AI session
After a useful AI session, save it in fenbs: the key observations, findings, research and context. Then carry on from exactly where you left off, on any device and in any other AI. The next AI you connect reads it at the start of the session, so you never have to explain the same problem again.
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How to keep your training results with fenbs
Keep what you learned so you can carry on from exactly where you left off, on any device and in any other AI. Save a clear result for each attempt rather than expecting a model to explain why you trained it.
- Create a free account.
- Add a task for the experiment and write what a better answer should do.
- For the work you do with Fireworks AI, connect a supported assistant to fenbs.
- Save the run references, what passed, what failed and the next step. Leave out keys and private training examples.
fenbs keeps the experiment notes and the work still to do. It does not train, evaluate or host the model. If you only need the training job’s technical metadata, the job record may be enough; fenbs helps when another person or assistant needs to continue the investigation.