# jingyaogong/minimind

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39,663 stars · 4,794 forks · Python · apache-2.0

## Links

- GitHub: https://github.com/jingyaogong/minimind
- Homepage: https://jingyaogong.github.io/minimind
- awesome-repositories: https://awesome-repositories.com/repository/jingyaogong-minimind.md

## Topics

`artificial-intelligence` `large-language-model`

## Description

This project is a comprehensive framework for the entire lifecycle of transformer-based language models, supporting everything from foundational pretraining to specialized deployment. It provides a modular toolkit for defining neural network architectures, managing data preparation pipelines, and executing training routines across various scales. The framework is designed to handle the full model development process, including supervised fine-tuning, behavioral alignment, and the integration of agentic capabilities.

What distinguishes this framework is its focus on efficient training and advanced alignment methodologies. It incorporates techniques such as low-rank parameter adaptation and mixture-of-experts routing to optimize memory usage and computational efficiency. The system also features built-in support for direct preference optimization and automated feedback training, allowing users to refine model behavior and align outputs with human intent without requiring extensive manual labeling.

The platform covers a broad range of capabilities, including knowledge distillation for creating efficient student models, sequence length extrapolation for extended context processing, and robust tool-calling integration for agentic workflows. It includes utilities for benchmarking model performance, converting weights for cross-platform compatibility, and serving predictions through standardized network APIs or local command-line interfaces.

## Tags

### Artificial Intelligence & ML

- [Model Training Toolkits](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training-toolkits.md) — A comprehensive toolkit for pretraining, fine-tuning, and aligning transformer-based models across various scales and hardware configurations.
- [Agentic Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-frameworks.md) — Training models to perform complex multi-turn tasks by integrating tool calling capabilities and structured reasoning steps into their generation process.
- [Agentic Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-training-frameworks.md) — The framework provides agentic model training to optimize trajectories for multi-turn tool use and reasoning by leveraging environment feedback and delayed rewards for autonomous tasks. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))
- [Decoder Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/decoder-architectures.md) — Models are constructed using stacked transformer blocks with causal attention mechanisms to predict subsequent tokens in a sequence.
- [Model Pretraining Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/model-pretraining-frameworks.md) — The framework supports full-scale pretraining on massive text corpora using unsupervised learning to acquire fundamental language patterns, statistical relationships, and broad world knowledge. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))
- [Parameter Efficient Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/parameter-efficient-fine-tuning.md) — Training updates are restricted to small, trainable matrices injected into frozen layers to reduce memory usage during fine-tuning.
- [Alignment Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/alignment-pipelines.md) — A structured workflow for optimizing model behavior through preference data, automated feedback, and multi-turn instruction tuning.
- [Alignment Techniques](https://awesome-repositories.com/f/artificial-intelligence-ml/alignment-techniques.md) — Model behavior is aligned with human intent by mathematically maximizing the probability of preferred responses over rejected ones.
- [Inference APIs](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-apis.md) — The framework exposes model capabilities through a standardized network API, supporting advanced features like tool calling and structured output for integration with external applications. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))
- [Inference Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-engines.md) — A collection of tools for converting model weights, serving network APIs, and executing real-time text generation in local environments.
- [Pretraining Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/pretraining-frameworks.md) — Building foundational language models from scratch by processing massive text corpora to learn fundamental patterns and broad world knowledge.
- [Supervised Fine-Tuning Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/supervised-fine-tuning-frameworks.md) — The framework provides supervised fine-tuning capabilities to align pretrained models with specific assistant roles, response styles, and instruction-following requirements using dialogue datasets. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))
- [Automated Feedback Training](https://awesome-repositories.com/f/artificial-intelligence-ml/automated-feedback-training.md) — The framework supports training with automated feedback, utilizing signals from reward models or rule-based validators to refine model behavior without relying on expensive human-labeled datasets. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))
- [Fine-Tuning Libraries](https://awesome-repositories.com/f/artificial-intelligence-ml/fine-tuning-libraries.md) — Adapting pretrained models to specific domains or tasks using parameter-efficient techniques that minimize computational costs and memory requirements.
- [Model Distillation Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/model-distillation-tools.md) — Transferring intelligence from large, resource-heavy teacher models into smaller, efficient student models to improve deployment speed and performance.
- [Neural Network Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-frameworks.md) — A modular codebase for defining, configuring, and scaling neural network layers including dense and mixture-of-experts components.
- [Preference Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/preference-optimization.md) — The framework enables preference optimization to align model responses with human preferences, improving output quality without the need for complex, separate reward models. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))
- [Reasoning Configuration Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/reasoning-configuration-tools.md) — The framework enables reasoning configuration by injecting specific formatting tags into the model output stream, forcing the generation of explicit reasoning steps before final answers. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))
- [Sparse Model Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/sparse-model-architectures.md) — Computational load is distributed across specialized sub-networks where only a subset of parameters is activated for each input token.
- [Supervised Fine-Tuning Datasets](https://awesome-repositories.com/f/artificial-intelligence-ml/supervised-fine-tuning-datasets.md) — The framework supports structuring supervised datasets into multi-turn conversation formats, including dialogue and tool-calling sequences to improve model instruction following and task performance. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))
- [Tokenization Strategies](https://awesome-repositories.com/f/artificial-intelligence-ml/tokenization-strategies.md) — Text is decomposed into subword units based on statistical frequency to balance vocabulary size and model parameter efficiency.
- [Alignment Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/alignment-tools.md) — Refining model behavior to match human expectations and safety standards by training on preference data or automated feedback signals.
- [Inference Runtimes](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-runtimes.md) — The framework enables integration with third-party inference engines, allowing models to serve predictions via standardized network interfaces or local runtime environments for production use. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))
- [Model Benchmarking Suites](https://awesome-repositories.com/f/artificial-intelligence-ml/model-benchmarking-suites.md) — The framework includes benchmarking tools to evaluate model accuracy, reasoning capabilities, and general knowledge against objective datasets across various architectures and parameter sizes. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))
- [Preference Alignment Datasets](https://awesome-repositories.com/f/artificial-intelligence-ml/preference-alignment-datasets.md) — The framework facilitates the creation of preference datasets by structuring pairs of chosen and rejected responses to align model outputs with human expectations. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))
- [Tool Calling Integration Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/tool-calling-integration-frameworks.md) — The framework supports tool calling integration by training models to recognize and execute external function calls through structured tool definitions within multi-turn conversation datasets. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))
- [Context Window Extrapolation](https://awesome-repositories.com/f/artificial-intelligence-ml/context-window-extrapolation.md) — The framework implements sequence length extrapolation algorithms, allowing models to process inputs significantly longer than their original training length without losing coherence or stability. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))
- [Model Conversion Utilities](https://awesome-repositories.com/f/artificial-intelligence-ml/model-conversion-utilities.md) — The framework provides weight conversion utilities to transform model files between formats, ensuring compatibility with diverse inference engines, deployment environments, and hardware acceleration tools. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))
- [Model Distillation Methods](https://awesome-repositories.com/f/artificial-intelligence-ml/model-distillation-methods.md) — Smaller student models learn to replicate the output distributions of larger teacher models to achieve high performance with fewer parameters.
- [Positional Embedding Techniques](https://awesome-repositories.com/f/artificial-intelligence-ml/positional-embedding-techniques.md) — Positional embedding techniques allow models to process input contexts significantly longer than those encountered during the initial training phase.
- [Architecture Definitions](https://awesome-repositories.com/f/artificial-intelligence-ml/architecture-definitions.md) — The framework supports the definition of transformer-based architectures, including dense or mixture-of-experts layers, with standard components like normalization, activation functions, and positional embeddings. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))
- [Data Preprocessing Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/data-preprocessing-pipelines.md) — A set of utilities for tokenizing, formatting, and structuring raw text and dialogue datasets for efficient model training.
- [Knowledge Distillation](https://awesome-repositories.com/f/artificial-intelligence-ml/knowledge-distillation.md) — The framework facilitates knowledge distillation by transferring capabilities from a large teacher model to a smaller student model using teacher-generated outputs to improve efficiency. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))
- [Pretraining Data Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/pretraining-data-pipelines.md) — The framework enables the organization of raw text corpora into text-to-text sequences, ensuring consistent data distribution and controlled lengths for foundational language model pretraining. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))
- [Tokenizers](https://awesome-repositories.com/f/artificial-intelligence-ml/tokenizers.md) — The framework provides tokenization utilities to map natural language into numerical identifiers, optimizing vocabulary size for efficient model training and inference memory consumption. ([source](https://cdn.jsdelivr.net/gh/jingyaogong/minimind@master/README.md))