Prerequisites
- A base model to fine-tune. You can pick one from your workspace, the public library, the platform, or HuggingFace.
- A training dataset. See Add a dataset, or pick one from the library or HuggingFace.
Steps
1. Start a new training run
In the Train tab, click Jobs in the left sidebar to see your Training Jobs, with totals for running, completed, stopped, and failed runs. Click + New Training in the top right.
2. Name the run and pick a base model
Give the run a Training Name. This name is saved to the Knowledge API and reused as the default when you push the merged model to BenchGen later. Open the Base Model dropdown and choose a source:| Source | What it is |
|---|---|
| My Models | Models already in your workspace. |
| Public Library | Models shared by the community. |
| Platform | Models published on BenchGen. |
| HuggingFace | Any public model from the Hub. Search by name. |
3. Choose a dataset
Open the Dataset dropdown and pick from My Datasets, Public Library, Fine-tune Datasets (datasets exported from Eval runs), or HuggingFace.
4. Configure the LoRA parameters
| Parameter | Range | Default | What it controls |
|---|---|---|---|
| Rank (r) | 4–64 | 8 | Adapter capacity. |
| Alpha | 4–128 | 16 | Scaling factor. A common ratio is Alpha = 2 × rank. |
| Dropout | 0.0–0.5 | 0.05 | Regularization. |
| Learning Rate | 1e-5 to 5e-4 | 1e-4 | Step size for gradient updates. |
5. (Optional) Adjust advanced options
Expand Advanced Options for finer control:| Setting | Default | Notes |
|---|---|---|
| Quantization | Full Precision | Equivalent to Q4/Q8 in GGUF, but for training. Use a lower precision for large models when VRAM is limited. |
| Epochs | 2 | Full passes over the dataset. |
| Batch Size | 4 | Examples per gradient step. |
| Max Steps | 50 | Caps the run. Set -1 or click Full to train over the entire dataset. |
| Auto GPU Selection | On | Lets the backend pick the first compatible free GPU or accelerator from the Ray cluster. |
6. Start training
Click Start Training. The job opens to its detail page with a status of Training.
7. Monitor progress
The Training Progress card streams the current step and percent complete, along with elapsed time, ETA, speed (it/s), epoch, loss, learning rate, and gradient norm. Expand Training Logs for the raw output, or click Cancel Training if you need to stop early.
8. Training completes
When the run finishes, the status changes to Completed and the progress card shows Finished with the final loss and runtime. The Actions panel unlocks so you can download the adapter or merge and save the model.
Next Steps
Training is complete and the adapter is ready. Merge it into the base model and push it to BenchGen so you can run and evaluate it.
Merge & save the model
Combine the adapter with the base model and save it to the platform.
Run inference
Test the adapter in a chat interface before merging.