Announcing Spider: a lightweight tool to craft post-training data recipes
TL;DR
Spider is a single client interface that turns messy distillation and ablation experiments into a simple, configurable workflow. Set on_policy: false to generate clean distillation datasets, or flip on_policy: true to run online training with teacher guidance and KL supervision. It handles dataset prep, rollouts, supervision, and post-processing in a few lines of code.
Why we built Spider
“Tinker for training” exists. “Tinker for data” does not.
Our friends at Thinking Machines recently released Tinker that enables fine-tuning with full control in simple steps. However, most research time still disappears into preprocessing, rollout scripts, verifier glue code, and training integration. So, this Halloween, we spun a web around that problem and built Spider so you can define a production-grade distillation run with just a few lines, then iterate fast.
We hope Spider will help you test ideas in hours, not days; turn messy data work into a shareable recipe; and ship better models with less glue. Grab the repo, run a sample recipe, and tell us what to build next.
How it works
Spider turns post-training data work into a single client workflow. In off-policy mode it generates distilled datasets with high-throughput rollouts, then applies your preprocessing, filters, and verifiers. Flip on_policy: true to run the online loop with a teacher model and KL supervision. The same recipe drives both paths.
Run Spider on Collinear endpoints or your own GPUs. Each run records its recipe, parameters, and metrics, and can publish datasets or trained artifacts to the Hugging Face Hub. The result is faster loops, cleaner data, and fewer moving parts from idea to artifact.
Define a recipe
Write a short YAML that names your provider, models, dataset source, and any filters or verifiers. This is your data recipe. One client and one config cover both off policy and on policy paths.Generate or train
Run the recipe with on_policy: false to create an off policy distilled dataset from high-throughput rollouts. Flip on_policy: true to introduce a teacher model and KL supervision through the integrated Tinker client for online training.
Compose quality checks
Use built-in filters and verifiers for length, dedupe, syntax, structure, and safety, or register your own in one line. Spider applies them in the pipeline so your outputs are clean and auditable.
Run anywhere, ship anywhere
Point the client to a Collinear endpoint with an API key or to your own GPUs. Each run logs its recipe, parameters, and metrics, and can publish datasets or model artifacts to the Hugging Face Hub with lineage preserved.
Getting started is simple, and you can find quickstart instructions on the repo.
Roadmap
We’re building toward a world where post-training data is defined as code, portable across providers, and fast to turn into measurable model gains. Write a small recipe, verify quality with shared checks, and ship a distilled dataset or on-policy improvement in minutes.
To enable that, we are expanding Spider with the following roadmap features.
Cross-tokenizer on-policy distillation from any teacher model
Simple but powerful templates for generating multi-turn conversation data with simulated users
Highly configurable tool-use library to generate and train on-policy agentic tool-call rollouts
Resources
You can learn more about Spider on our GitHub repo here.
If you give Spider a try, let us know what you think!