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Managed fine-tuning, the Training API, and serverless training all draw from the same base model catalog, but availability is decided per model: managed jobs by method (SFT, DPO), Training API jobs by parameter mode (LoRA or full-parameter).

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Model availability

Pick a model to see the surfaces and methods it is enabled for, plus any training shapes that back it. Switch to All models for the full matrix.

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Next steps

Managed Fine-Tuning

Hand Fireworks your data and let the platform run the job

Training API

Write your own training loop against a Tinker-compatible API

Serverless Models

Serverless Training API model catalog with per-token pricing

Training Shapes

What a shape pins and how to reference one

Dedicated Training

Provision a trainer and sampler on reserved GPU capacity

Pricing

Current rates across training and inference