Axolotl vs Nebius Token Factory fine-tuning

Axolotl

Axolotl runs a whole fine-tuning job from one YAML file and ships a JSON Schema of its config plus bundled agent docs. It is 0.x software with telemetry on by default, no terms or privacy policy, and the owner supplies the GPU.

Nebius Token Factory fine-tuning

Supervised fine-tuning on 49 open base models through OpenAI-style /v1/fine_tuning/jobs calls, with LoRA or full weights and every checkpoint file downloadable. No fine-tuning price was found outside the script-drawn console, and the docs say tuned models deploy only to dedicated endpoints, with custom weights in beta on request.

Axolotl

1. Set AXOLOTL_DO_NOT_TRACK=1 before any command, or training waits 10 seconds and sends usage events to PostHog
2. Run axolotl agent-docs and axolotl config-schema --field <name> before writing a config; both work offline from the installed package
3. Install torch first, then uv pip install --no-build-isolation axolotl[deepspeed], on Python 3.12 or later with PyTorch 2.13 or later
4. Take example configs from the same release tag as the installed version; minor releases remove and rename config keys
5. Resume an interrupted run with axolotl train config.yml --resume-from-checkpoint <path>, then axolotl merge-lora and axolotl export only when shipping

Nebius Token Factory fine-tuning

1. Use the OpenAI client with base_url https://api.tokenfactory.nebius.com/v1/ and NEBIUS_API_KEY. Upload JSONL with purpose=fine-tune, then create the job.
2. Set hyperparameters.lora to true for an adapter. The default is false, which runs full fine-tuning.
3. Poll GET /v1/fine_tuning/jobs/{job_id} no faster than every 15 seconds. There is no idempotency key, so list jobs before recreating one after a timeout.
4. Download every ID in a checkpoint's result_files before relying on hosted copies. The terms allow deletion of tuned models at three days' notice.
5. The spec requires wandb.api_key although the guide omits it, and it also accepts mlflow and hf integrations. Check the price in the console before starting a job.