Tinker
Python-first API & SDK for distributed fine-tuning
See: (company/product announcement)
Features
- Programmatic training control: custom training loops, callbacks, and loss functions
- Data pipeline integration: dataset versioning, preprocessing, batching, and augmentation hooks
- Distributed compute orchestration: scale training across clusters with managed infrastructure
- Observability & reproducibility: experiment tracking, metrics, checkpoints, and deterministic run options
- Safety & guardrails: built-in monitoring for divergence, undesirable behavior, and preflight checks before deployment
Superpowers
Tinker gives researchers and engineering teams deep programmatic control over model training without rebuilding distributed infrastructure. It balances flexibility (custom training regimes, loss-aware training) with managed infra and guardrails, making it ideal for experimental research and reproducible enterprise workflows.
Pricing
- Not publicly disclosed (early access / enterprise pricing reported)
API & Integrations
- Python SDK (primary)
- Integrations with dataset and experiment tracking systems (e.g., DVC, MLflow — reported)
- Compatible with common model frameworks (PyTorch, JAX — reported)