Tensorless

ML with maximum automation and minimum setup.

import tensorless as tl

tl.train("./data")

That's it. Tensorless inspects your dataset, figures out what kind of task you're trying to solve, builds and configures a model, trains it, validates it, checkpoints it, and saves a single portable model.tl file you can move anywhere.

model = tl.run("model.tl")   # interactive chat, if it's a text model
# or
model = tl.load("model.tl")
model.predict(...)

Simple by default. Powerful when you need it:

tl.train(
    "./data",
    d_model=512,
    layers=6,
    learning_rate=3e-4,
    batch_size=32,
)

Why Tensorless

Most ML frameworks assume you already know what model you want, how big it should be, which optimizer and learning rate to use, and how to wire up checkpointing and resumption yourself. Tensorless flips that: it makes a reasonable, working choice for all of that automatically, and lets you override exactly the parts you care about.

It also remembers what it already did. Run tl.train("./data") twice on the same dataset and it won't retrain — it'll just hand you back the model it already trained. Change the data, and it retrains. Get interrupted partway through a long run, and the next call resumes right where it left off. This is the Smart Auto Check, and it's the core idea the whole framework is built around.

Install

pip install -e .

See docs/installation.md for details and requirements.

Documentation

Supported today

• Text generation (language modeling) from .txt/.md files or JSON/JSONL with a text field
• Text classification from a directory of class subfolders (positive/, negative/, ...) or labeled JSON/JSONL
• Tabular classification and regression from CSV/TSV/JSON/JSONL with a target column

Project status

Tensorless is an early-stage, actively developed framework. The core loop — inspect, auto-configure, train, checkpoint, save, reload, infer — is real and tested end-to-end (see tests/). See docs/limitations.md for what's intentionally out of scope right now, and docs/roadmap.md for what's next.

License

MIT