Warm Start from Fine-Tuned Models ↗

fireworks guide intermediate fine-tuning models
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Summary: Continue training from a previously fine-tuned model with RFT

Original Documentation

Documentation Index#

Fetch the complete documentation index at: https://docs.fireworks.ai/llms.txt Use this file to discover all available pages before exploring further.

Continue training from a previously fine-tuned model with RFT

Fireworks supports RFT training on warm start and already-fine-tuned models. Upload models to Fireworks and use the warm start option to continue training (e.g. from an SFT LoRA) with RFT, rather than start from scratch with a base model.

When to use warm start#

Use the --warm-start-from flag when you want to:

  • Start RFT from an SFT model you’ve trained with Fireworks
  • Continue training from an existing fine-tuned LoRA adapter you’ve uploaded to Fireworks

Basic usage#

eval-protocol create rft \
  --warm-start-from accounts/your-account/models/<SFT_MODEL_ID> \
  --output-model <RFT_MODEL_ID>

When using --warm-start-from, do NOT include --base-model. The base model is automatically determined from the LoRA adapter.

# Wrong, includes --base-model
eval-protocol create rft \
  --base-model accounts/fireworks/models/llama-v3p1-8b-instruct \
  --warm-start-from accounts/your-account/models/<SFT_MODEL_ID>

SFT to RFT workflow#

Get started with supervised fine-tuning on Fireworks:

    firectl sftj create \
      --base-model accounts/fireworks/models/<BASE_MODEL_ID> \
      --dataset accounts/your-account/datasets/<DATASET_ID> \
      --output-model <MODEL_ID>
    ```

Or if you already have a LoRA adapter, upload it to Fireworks:

```bash
    firectl model create <MODEL_ID> /path/to/files/ \
      --base-model "accounts/fireworks/models/<BASE_MODEL_ID>"
    ```

<span class="callout-start" data-callout-type="note"></span>
  Learn more about uploading custom LoRA adapters in the [Custom Models guide](/models/uploading-custom-models#importing-fine-tuned-models).
<span class="callout-end"></span>
  <span class="step-end"></span>

  <span class="step-marker" data-step-title="Start RFT from SFT model"></span>
Use an existing model as a starting point, and combine with standard RFT parameters.

```bash
    eval-protocol create rft \
      --warm-start-from accounts/your-account/models/<SFT_MODEL_ID> \
      --output-model <RFT_MODEL_ID> \
      --epochs 2 \
      --learning-rate 5e-5 \
      --temperature 0.8
    ```
  <span class="step-end"></span>
<span class="steps-end"></span>

## Troubleshooting

<AccordionGroup>
  <Accordion title="Error: 'not of kind base_model, but HF_PEFT_ADDON'">
This means you specified both `--base-model` and `--warm-start-from`. Remove the `--base-model` flag.
  </Accordion>

  <Accordion title="Model not found">
Verify the model exists in your account:

```bash
    firectl model list --account accounts/your-account
    ```
  </Accordion>
</AccordionGroup>
Link last verified June 7, 2026. View original ↗
Source: Fireworks AI Docs
Link last verified: 2026-06-07