A programming workshop for understanding language-model training. The kit implements character embeddings, a tanh hidden layer and a softmax output, with explicit backpropagation. Its educational corpus is split chronologically into training and validation data. This small network is neither a conversational assistant nor a foundation model.
Included capabilities
- Trainable CPU neural network
- Backpropagation and numerical gradient verification
- Temperature and seed controls
- JSON storage without pickle
- Original corpus and automated tests
Licence and getting started
Original code under the MIT licence, copyright 2026 STELLARR STUDIO. Personal and commercial use permitted with the licence notice retained. French and English guides included. Trademarks and dependencies retain their own rights.
Read the README for requirements, start commands, tests and deployment limits. Hosting, compute, external APIs and bespoke support are not included.
Included files
.gitignoreexamples/corpus.txtkit.jsonLICENSEREADME.en.mdREADME.mdrequirements.txtTEST_REPORT.mdtest_tiny_lm.pyTHIRD_PARTY_NOTICES.mdtiny_lm.pyverification.json