Nebius Token Factory fine-tuning vs Tinker

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.

Tinker

Full control of the training loop with the GPUs abstracted away, plus recipes for SFT, DPO, RL and distillation. LoRA only; no full-parameter training.

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.

Tinker

1. Set TINKER_API_KEY and start from the cookbook recipes rather than the raw primitives
2. Read the 'Avoid Client-Side Timeouts and Retries' guide before wrapping sampling calls in your own retries; the SDK already retries sampling with stable request IDs
3. Save intermediate checkpoints with a TTL between 1 hour and 10 years; storage bills at $0.10 a GB-month until they expire
4. Read models.json for current prices before a run; sampling tokens cost more than training tokens on the open models
5. Check the model deprecations page before pinning a base model; 18 were retired on 2026-06-12