Training Overview

Fine-tuning adapts a base model to your task by training it on your own data, so it learns your formats, tone, tools, and edge cases instead of relying on prompt instructions alone. Fireworks runs the training for you, without the burden of building and maintaining your own GPU or training infrastructure.

Fine-tuning is worth it when you want:

• Higher task quality - beat a general-purpose model on your specific workload, and often match or exceed a larger closed model.
• Lower latency and cost - a smaller specialized model can replace a bigger one at a fraction of the per-token cost.
• Consistent behavior - bake in formats, style, and tool-use so you stop paying for long prompts and few-shot examples on every request.
• Ownership and no infra - you keep the resulting weights, and Fireworks handles the GPUs, scheduling, and checkpointing.

Coming from OpenAI? Fireworks uses the same OpenAI-compatible chat completion format for training data — the same messages array with role, content, tool_calls, and weight fields. You can use your existing SFT datasets with no conversion required. See the SFT dataset format for the full schema and examples.

Choose a method

Pick a method based on the data or signal you have. All three run as standard jobs on Managed Fine-Tuning, or as custom loops you write yourself on the Training API.

	SFT	DPO	RL
Good for	Classification, extraction, format and tone adherence, distillation	Steering the model toward a goal you cannot measure objectively, such as style, helpfulness, or safety	Tasks where you have no verified outputs to learn from, but you can tell whether an outcome was good or bad. Pushing the model beyond state-of-the-art
Data you supply	Verified input/output pairs, or successful trajectories	Preference pairs, single-turn only: one prompt, a chosen and a rejected response	Prompts, plus an evaluator that can tell a good outcome from a bad one
Dataset size	Hundreds of examples, or roughly 10M+ tokens	Hundreds to thousands of pairs	Dozens to thousands of prompts, sometimes more. Often fewer than 100 is enough
Consider alternatives if	You have very few examples, or no high-quality verified outputs to learn from	Outputs can be judged objectively, or you already have high-quality verified pairs. Both point to SFT or RL	You have no way at all to judge an outcome, including an LLM judge. Simpler methods are untried, or you want a quick fine-tuning experiment
Guides	Text · Vision · Cookbook	Managed DPO / ORPO · Cookbook	Managed RFT · Cookbook

Verifiable means you can reliably judge whether a model output is good (rules, unit tests, programmatic checks). RL fits reasoning and agentic tasks where full ground-truth labels are hard to write.

The Training API also supports custom methods (GRPO, distillation, and others) via the Python SDK. See Cookbook recipes and Managed Fine-Tuning for model support and pricing.

Choose a surface

Pick a surface (managed or Training API, serverless or dedicated). The surface decides how much of the model you update and which interfaces are available to you.

Answer the question below and the flow takes you to your surface, which links to its guide. Click any answered question to change it, or show every path at once.

Compare the last branch in detail on serverless versus dedicated, and check per-model support on Models.

Managed Fine-Tuning vs Training API

Fireworks offers two ways to train: Managed Fine-Tuning (Fireworks runs the loop) and the Training API (you write the loop in Python).

Choose Managed Fine-Tuning when	Choose the Training API when
You need a standard SFT, DPO, ORPO, or RFT job	You need a custom loss, reward, rollout, trajectory, or optimizer-step loop
You want Fireworks to own scheduling, training, and checkpointing	You want to fork or write Python training logic
A supported model and managed configuration cover the task	You need inference in the loop, distillation, per-step diagnostics, or research algorithms

Managed Fine-Tuning

Standard jobs with a platform-managed loop.

Training API

Programmable loops built from cookbook recipes or the SDK.

Serverless vs Dedicated infrastructure

Infrastructure applies to the Training API only. Managed Fine-Tuning uses platform-managed compute.

Choose Serverless Training when	Choose Dedicated Training when
Supported LoRA SFT or RL covers the workload	You need full-parameter, DPO, ORPO, distillation, or broader model support
You want shared pooled compute with no trainer or sampler deployment provisioning	You need explicit trainer, deployment, checkpoint, reconnect, or promotion control
Per-token billing fits a small or bursty experiment	A sustained, highly utilized time-based run fits the workload
In-session sampling is sufficient	You need provisioned rollout or evaluation deployments

Serverless Training

Shared pooled trainer, no provisioning, per-token billing.

Dedicated Training

Provisioned trainer and deployment resources with explicit lifecycle control.

See the detailed serverless versus dedicated comparison.

Choose how to interact

• Skill — the only interface that drives both surfaces. Your coding agent configures, runs, and troubleshoots training through the Fireworks training skill.
• Fireworks UI, firectl, or the REST API — managed jobs only. Guided creation and monitoring in the UI, reproducible job and resource automation from the CLI or API.
• Python SDK — Training API loops only, on serverless or dedicated. Start from a cookbook recipe.

CLI or API vs Python SDK: firectl and the REST API manage managed jobs and platform resources. The Python SDK runs Training API loops you author yourself (loss, rollouts, optimizer steps).

GPU quota prerequisite

Managed jobs and dedicated Training API runs need training GPU quota, granted automatically by spending tier. Serverless Training uses a shared pool with its own model, concurrency, and rate limits instead of dedicated training GPU quota.

Training GPU quota by spending tier

Tier	How to reach it	B200 / B300 (Blackwell)	H200	H100 / A100
No payment method	—	0	0	0
Tier 1	Valid payment method and billing profile	0	16	8
Tier 2	Spend or add $50 in credits	16	16	16
Tier 3	Spend or add $500 in credits	24	24	24
Tier 4	Spend or add $5,000 in credits	32	32	32

Check your quota with the Fireworks CLI (firectl quota list). A job rejected with HTTP 429 quota_exceeded (sometimes a 403 on the job poll) is a tier issue, not a dataset/config problem.

Need more training quota than your tier allows? Reach out for enterprise support and we’ll help size the right allocation for your workload.

Models

Model availability is decided per model and per surface — managed jobs by method (SFT, DPO, RFT), Training API jobs by parameter mode (LoRA or full-parameter). Check the live catalog before you launch.

Models

Browse the base model catalog with per-model surface, method, and training-shape support.

Training security

Across every training surface, one principle holds: your training data is never used to train Fireworks-owned or shared models. Inference follows Zero Data Retention by default. Step-by-step BYOB IAM, CMEK KMS setup, and secure RFT automation live in the Fireworks training skill — secure training operations.

Choose a surface by data-privacy needs

Surface	Where your training data lives	What Fireworks retains	Your deletion controls
Managed Training	Fireworks-managed storage (GCS); reference link in our database	Dataset, checkpoints, traces	Delete dataset after job; request checkpoint/trace deletion
Managed + BYOB	Your cloud bucket; read in-place during training only	Checkpoints and traces only	Revoke bucket access after job; request checkpoint/trace deletion
Training API	No dataset file on Fireworks — transient tokenized batches only	Checkpoints and traces only	Request checkpoint/trace deletion

Checkpoints and traces are retained ~30 days by default (deletable on request). Strictest governance: BYOB (dataset never copied to Fireworks) or the Training API (no stored dataset file).

Bring your own bucket (BYOB)

Register an external URL so Fireworks reads your dataset during the job without persisting a copy, then revoke access after the job:

firectl dataset create my-dataset --external-url gs://your-bucket/path/train.jsonl

Supported: GCS, AWS S3, and Azure Blob, with least-privilege IAM to Fireworks service accounts provided at onboarding. For AWS S3, lock the IAM trust policy with both accounts.google.com:sub and accounts.google.com:oaud (your Fireworks account ID) so tokens for other accounts are rejected. Full IAM trust policies, OIDC audience, and rotation are in the secure training operations skill reference.

Customer-managed encryption keys (CMEK)

CMEK encrypts datasets and checkpoints on Fireworks-managed storage with your cloud KMS key — revoke the key and Fireworks cannot decrypt. Supported today: AWS KMS + Managed SFT (DPO, RFT, Azure Key Vault, and GCP KMS coming soon). It does not cover in-memory training compute or inference request/response. Setup, IAM, and rotation detail: secure training operations skill reference · Data Security Overview.

Secure RFT and customer controls

For RFT under strict governance, combine a BYOB dataset with evaluators and rollout servers kept in your own environment (see Remote Environment Setup). To delete checkpoints, traces, or rollout data, contact your Fireworks account team; datasets are deletable from the console or API after a job completes.

Before launch

Verify current model support, shapes, access status, pricing, limits, and quota in the linked live pages. A coding agent asks for confirmation before upload, registration, paid inference, job creation, promotion, deployment, or another mutation. Material changes require approval again; promotion and deployment are confirmed separately.