LLM et modèles de fondation

minimind

jingyaogong/minimind

A lightweight implementation for exploring small language model pretraining and fine-tuning.

★ 58,6KÉtoiles
⑂ 7,6KForks
60Problèmes ouverts
PythonLangue
Apache-2.0Licence
Q90Score éditorial

Vue d’ensemble

A lightweight implementation for exploring small language model pretraining and fine-tuning. The repository is maintained under jingyaogong on GitHub. Its primary language is Python. See the [project README](https://github.com/jingyaogong/minimind#readme) for the supported workflows.

Fonctionnalités clés

  • A lightweight implementation for exploring small language model pretraining and fine-tuning.

Prérequis, installation et démarrage rapide

Follow the repository quickstart to install dependencies and obtain the model or training data required by the selected example.

[Read the upstream installation and quickstart instructions](https://github.com/jingyaogong/minimind#readme).

Utilisation

Run a small inference or training example first, then adjust the documented configuration for your hardware and dataset.

[Usage examples and configuration reference](https://github.com/jingyaogong/minimind#readme).

Compatibilité des modèles et cas d’usage

La compatibilité des modèles n’est pas indiquée dans les métadonnées du dépôt.

Licence et notes sur les risques

GitHub reports Apache-2.0 for this repository. Review the upstream license file; model weights, datasets and dependencies may have separate terms.

Source review 2026-09-05: GitHub search metadata and repository README. No runtime benchmark performed. License metadata: Apache-2.0.

Publication et maintenance

[View upstream releases](https://github.com/jingyaogong/minimind/releases).

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