Training on Baseten

Train custom models with developer-first training infrastructure on Baseten.

Baseten provides a flexible training platform that enables you to bring your own training scripts, use the latest training techniques, and fine-tune the newest models.

Train models and serve them in production, all on one platform. Baseten automatically stores your checkpoints during training and makes them ready for deployment. You don’t need to download weights, re-upload them, or manage separate infrastructure. Your fine-tuned model goes from checkpoint to production endpoint in a single command.

The core workflow requires just two commands:

# Train your model
truss train push config.py

# Deploy from the checkpoint
truss train deploy_checkpoints --job-id <job_id>

Choosing between Truss Train and Loops

Baseten supports two training paths. Truss Train is the bring-your-own-container path documented on the rest of this page: package any training framework as a Truss, configure hardware, and run it on managed infrastructure.

Loops is a Tinker-compatible managed path for fine-tuning and RL: write import tinker Python, and Baseten provisions a paired trainer and sampling server with weights that move live between them. If your team already uses Tinker, or wants SFT and RL without managing a training container, start with Loops.

	Loops	truss train
Training code	Tinker-compatible Python (import tinker)	Any container image
Infrastructure setup	None. Baseten provisions trainer and sampling servers.	You define hardware in a Truss config
Checkpoint format	Paginated presigned URLs, streamed to sampling server live	Your container’s output artifacts
Inference path	Automatic: checkpoint deploys directly to a Baseten inference deployment	Manual: you move artifacts and deploy separately
Session lifetime	Open until you call truss loops deactivate	Job runs to completion and exits
Documentation	Loops	This section

Train and serve on one platform

The train-to-serve workflow is seamless:

1. Set up your training project: Bring any framework or start with a template.
2. Configure your training job: Define compute, runtime, and checkpointing settings.
3. Run on managed infrastructure: Use H200 or H100 GPUs, single-node or multi-node.
4. Checkpoints sync automatically: Baseten stores checkpoints as training progresses.
5. Deploy your fine-tuned model: Go from checkpoint to production endpoint in one command.

Baseten handles infrastructure management and file transfers. Bring any framework (Axolotl, TRL, VeRL, Megatron, or your own training code) and your trained model serves traffic within minutes of training completion.

Supported frameworks

Baseten Training is framework-agnostic. Use whatever framework fits your workflow.

Framework	Best for	Example
Axolotl	Configuration-driven fine-tuning with LoRA/QLoRA	oss-gpt-20b-axolotl
TRL	SFT, DPO, and GRPO with Hugging Face	oss-gpt-20b-lora-trl
TRL	LoRA DPO fine-tuning	qwen3-8b-lora-dpo-trl
VeRL	Reinforcement learning with custom rewards	qwen3-8b-lora-verl
MS-Swift	Long-context and multilingual training	qwen3-30b-mswift-multinode

Browse the ML Cookbook for more examples including multi-node training with FSDP and DeepSpeed.

Key features

Checkpoint management

Checkpoints sync automatically to Baseten storage during training. You can:

• Deploy any checkpoint as a production endpoint with truss train deploy_checkpoints.
• Download checkpoints for local evaluation and analysis.
• Resume from any checkpoint if a job fails or you want to train further.

Learn more about checkpointing.

BDN weight and data loading

Load model weights and training data through Baseten Delivery Network (BDN). Mount weights from Hugging Face, S3, GCS, Azure, R2, or any HTTPS URL directly into your training container with no download code needed. BDN mirrors weights before compute is provisioned, then caches them for faster mounting on subsequent jobs.

See storage and data ingestion for setup details.

Persistent caching

Speed up training iterations by caching models, datasets, and preprocessed data between jobs. The cache persists across training runs, so you don’t re-download 70B models every time.

See the training cache guide for configuration options.

Multi-node training

Scale training across multiple GPU nodes with InfiniBand networking. Baseten handles node orchestration, communication setup, and environment variables. You just set node_count in your configuration.

Learn more about multi-node training.

Remote access

Connect to running training containers to debug, inspect state, and iterate without resubmitting. Baseten offers two options:

• SSH: Connect from any OpenSSH client for terminal sessions and file transfer with scp or sftp.
• VS Code & Cursor: Connect from VS Code or Cursor Remote Tunnels for a full IDE experience.

See the Remote access overview to choose between them.

Next steps

Get started

Run your first training job and deploy the result.

Loops

Tinker-compatible managed training with paired trainer and sampling servers.

ML Cookbook

Production-ready examples for frameworks and models.

Reference

• CLI reference
• SDK reference
• API reference