SFT

Supervised Fine-Tuning (SFT) trains a language model on labeled conversation data so it learns to follow instructions, adopt a persona, or respond in a specific style.

Prerequisites

An EigenAI account with available credits.
A training dataset in JSONL format.

Create an SFT job

Click Fine-Tune a Model on the Fine-tuning page, then select the SFT tab to open the 6-step wizard.

Step 1 — Model

Select a Base model.

Model	Price
Qwen3-4B-Instruct-2507	$0.4 / M tokens
Qwen3-30B-A3B-Instruct-2507	$2.8 / M tokens
Qwen3-30B-A3B-Thinking-2507	$2.8 / M tokens
Qwen3-235B-A22B-Instruct-2507	$20 / M tokens

Step 2 — Dataset

Upload your training data or select a previously uploaded dataset.

Field	Description
Dataset format	The format of your training file. Currently supports Conversation (chat JSONL).
Select existing dataset	Reuse a dataset you have already uploaded.
File upload	Drag and drop a `.jsonl` file or click to browse.

Your training file must be a JSONL file where each line is a JSON object containing a messages array in OpenAI chat format:

{"messages": [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello!"}, {"role": "assistant", "content": "Hi there!"}]}

Step 3 — Evaluation

Choose how to validate the model during training.

Option	Description
Do not use a validation dataset	Skip evaluation for the fastest training run.
Automatically split a portion of the training dataset	Reserve part of your training data for evaluation. (Coming soon)
Use a custom dataset for evaluation	Provide a separate validation file. (Coming soon)

Step 4 — Params

Configure the training hyperparameters.

Parameter	Auto default	Description
Number of epochs	5	How many times the model trains over the full dataset.
Learning rate multiplier	1.0× (effective rate: 2.00e-5)	Scales the base learning rate.
Model output name	`ft-<timestamp>-<random-string>`	The name shown for the resulting fine-tuned model.

Each parameter has an Auto mode that applies sensible defaults and a Custom mode for manual entry.

Step 5 — WandB

Optionally connect Weights & Biases to track your experiment.

Field	Description
WandB API key	Your WandB API key. Leave blank to skip tracking.

Step 6 — Review

Review the estimated cost before starting training.

Field	Description
Model	The selected base model.
Tokens	Estimated number of tokens in your dataset.
Epochs	Number of training epochs.
Estimated cost	Total credit cost for this training run.

Check the acknowledgment box and click Confirm & Create to submit the job.

Eigen AI

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Prerequisites

Create an SFT job

Step 1 — Model

Step 2 — Dataset

Step 3 — Evaluation

Step 4 — Params

Step 5 — WandB

Step 6 — Review

​Prerequisites

​Create an SFT job

​Step 1 — Model

​Step 2 — Dataset

​Step 3 — Evaluation

​Step 4 — Params

​Step 5 — WandB

​Step 6 — Review

Prerequisites

Create an SFT job

Step 1 — Model

Step 2 — Dataset

Step 3 — Evaluation

Step 4 — Params

Step 5 — WandB

Step 6 — Review