> ## Documentation Index
> Fetch the complete documentation index at: https://fireworks.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Managed Fine-Tuning Overview
> Fine-tune models with Fireworks-managed infrastructure — no custom code required.
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.
## How to launch managed training
These interfaces create the same underlying managed jobs:
| Interface | Use when |
| ---------------- | ---------------------------------------------------------------- |
| **Fireworks UI** | You want guided configuration and visual monitoring |
| **CLI or API** | You want scripted and reproducible job operations |
| **Your agent** | You want help configuring, running, and troubleshooting training |
The Fireworks CLI is called `firectl`. [Install the training skill](/docs/fine-tuning/agent/use-with-coding-agents) to use your agent, or continue with the method-specific managed guides below. For custom Python training loops, start with the [Training API overview](/docs/fine-tuning/training-api/introduction).
## Methods
Train text and vision models with labeled examples of desired outputs
Train on preferred and non-preferred response pairs using DPO or ORPO
Train models using custom reward functions for complex reasoning tasks
## 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**](/docs/fine-tuning/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](https://app.fireworks.ai/models?filter=LLM\&tunable=true) or [vision models](https://app.fireworks.ai/models?filter=vision\&tunable=true).
## Tuning modes and context length
Managed fine-tuning runs **[Low-Rank Adaptation (LoRA)](https://arxiv.org/abs/2106.09685)** 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](/docs/fine-tuning/deploying-loras#multi-lora-deployment) on a single base model deployment. For full-parameter tuning, use the [Training API](/docs/fine-tuning/training-api/introduction).
**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 `#` format described in [Routing requests to LoRA addons](/docs/fine-tuning/deploying-loras#multi-lora-deployment).