Documentation Index
Fetch the complete documentation index at: https://docs.eigenai.com/llms.txt
Use this file to discover all available pages before exploring further.
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. |
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. |
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. |