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Give Fireworks your data and configuration. The platform handles scheduling, training, checkpointing, and model output. Training data uses the OpenAI-compatible chat completion format, so existing OpenAI SFT datasets work with no conversion required.

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How to launch managed training

These interfaces create the same underlying managed jobs: The Fireworks CLI is called firectl. Install the training skill to use your agent, or continue with the method-specific managed guides below. For custom Python training loops, start with the Training API overview.

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Methods

Supervised Fine Tuning - Text

Train text models with labeled examples of desired outputs

Supervised Fine Tuning - Vision

Train vision-language models with image and text pairs

Preference Optimization (DPO / ORPO)

Train on preferred and non-preferred response pairs using DPO or ORPO

Reinforcement Fine Tuning

Train models using custom reward functions for complex reasoning tasks

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Supported base models

Fireworks supports fine-tuning for major open source model families, including DeepSeek, Qwen, Kimi, Gemma, GLM, and Llama. Eligibility is decided per model and per method: a model can support SFT without supporting DPO or RFT. Models is the live per-model matrix: the surfaces and methods each base model is enabled for, the training shapes behind it, and each shape’s maximum context length. Check it before creating a job, and set the job context from a shape that supports the method you picked, using firectl sftj create, firectl dpo-job create, or the corresponding RFT command. Custom models uploaded by users are not automatically tunable. To use managed fine-tuning with an uploaded custom base model, the model must have a corresponding Hugging Face URL. Fireworks uses that URL to infer the training renderer and locate compatible training shapes. A custom model is supported only when Fireworks can resolve both a supported renderer and at least one compatible training shape. After the Hugging Face URL is set, tunability is refreshed by a background operation that runs about every 30 minutes, so the model may take up to 30 minutes to show as Tunable: true. We are working to make this refresh faster. To browse the broader catalog (including non-tunable inference models), visit the Model Library for text models or vision models.

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Tuning modes and context length

Managed fine-tuning runs Low-Rank Adaptation (LoRA) only. It supports the full context lengths exposed by the available training shapes, matching the same long-context capabilities used by cookbook recipes. LoRA gives you efficient adapter training and flexible deployment, including multiple LoRAs on a single base model deployment. For full-parameter tuning, use the Training API.
Deprecation notice: The deployedModel request key for routing to LoRA addons is deprecated and will not be supported for any new deployments. Please migrate to the model field with the <model_name>#<deployment_name> format described in Routing requests to LoRA addons.