Individual skill

tinker-training-inference

Train and run models on Tinker (Thinking Machines) — LoRA and full fine-tuning, SFT with correct loss masking, RL/RFT with group-relative advantages and importance sampling, agentic RL over multi-turn tool-using agents, inference via the SamplingClient or the OpenAI-compatible endpoint, checkpoint management and export, every hyperparameter, and cost estimation from the live price table. Use this skill whenever the user mentions Tinker, tinker://, TINKER_API_KEY, console.tinker.ai, or the tinker CLI; wants to fine-tune, SFT, RFT, or RL a model on Tinker; asks about Tinker models, context windows, LoRA rank, sampling params, loss functions (cross_entropy, importance_sampling, ppo, cispo, dro), Datum construction, forward_backward or optim_step; wants to sample from, download, resume, merge, or export a Tinker checkpoint; or asks what a Tinker run will cost. Whenever Tinker work is initialized in a project, this skill also creates a TINKER_PRICING.md there so prices sit next to the training code. Reach for it even on vague asks like "train a model on this data" or "get inference working" when Tinker is the platform in play.

skills/tinker-training-inference/SKILL.md

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Oct 7, 2026
License
MIT

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# Tinker: training and inference

Tinker gives you token-level control of hosted fine-tuning: you build the
`Datum`s, you own the chat template, you hold the exact prompt and completion
token ids on both sides. That is the reason to use it — a training run stays
auditable, because the tokens the policy was updated on are tokens you can point
at — and it is also why there is more to get right than with a hosted
fine-tuning button. This skill is the map.

## Do this first, in any new Tinker project

**Create the pricing file.** Prices next to the training script get read before a
550B RL run is launched; prices in a browser tab do not.