Fireworks AI Fine-tuning vs Unsloth

Fireworks AI Fine-tuning

SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available. Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless.

Unsloth

The Apache-2.0 core runs on customer hardware and keeps model weights there. Users supply and pay for the GPU.

Fireworks AI Fine-tuning

1. Add a payment method before the first job; without one the account has 0 training GPUs and 10 requests a minute
2. Check firectl model get -a fireworks <MODEL-ID> for Tunable: true before uploading a dataset
3. Pass your own supervisedFineTuningJobId on create, so after a timeout you can GET the job by that name instead of guessing whether it started
4. Deploy the LoRA to an on-demand deployment with a BF16 shape if several adapters will share it, and delete the deployment when evaluation ends
5. Download with firectl model download and keep the exact base model; the adapter alone won't run

Unsloth

1. Install with uv pip install unsloth --torch-backend=auto on a CUDA machine; the desktop app is for people
2. Start from the notebook for the model family in unslothai/notebooks; it sets LoRA targets and the chat template
3. Save the LoRA adapter while iterating and merge to 16-bit or GGUF only when you ship
4. If Studio must be reachable by other agents, pass --disable-tools and keep it on 127.0.0.1 behind a tunnel
5. Pin the exact unsloth version; releases land several times a week and don't flag breaking changes