# lordog/dive-into-llms

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40,974 stars · 4,986 forks · Jupyter Notebook

## Links

- GitHub: https://github.com/Lordog/dive-into-llms
- awesome-repositories: https://awesome-repositories.com/repository/lordog-dive-into-llms.md

## Description

Dive into LLMs is a framework designed for fine-tuning large language models and constructing modular machine learning pipelines. It provides a structured environment for adjusting pre-trained models on custom datasets while optimizing computational efficiency and training time.

The project distinguishes itself by offering an interactive web interface that allows for the deployment and publication of trained models directly to a browser. This enables users to test and interact with model results through a standardized web-based environment.

The platform supports the creation of flexible workflows by separating data processing, model architecture, and evaluation into independent stages. These capabilities are delivered through a collection of Jupyter Notebooks that facilitate the development and maintenance of specialized artificial intelligence solutions.

## Tags

### Artificial Intelligence & ML

- [LLM Fine-Tuning Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/training-systems/model-training-engines/llm-fine-tuning-engines.md) — Provides a specialized engine optimized for the efficient fine-tuning of large language models on custom datasets.
- [Large Language Model Fine-Tuning Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/integrated-development-platforms/machine-learning-platforms/large-language-model-fine-tuning-frameworks.md) — Provides a framework for adapting pre-trained large language models to specific tasks or datasets.
- [Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning.md) — Provides procedures for adapting pre-trained models to specific datasets to improve performance. ([source](https://github.com/Lordog/dive-into-llms/tree/main/documents/chapter1/))
- [Modular Pipeline Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/pipelines-and-orchestration/modular-pipeline-orchestrators.md) — Structures machine learning workflows by separating data processing, training, and evaluation into independent, modular components.
- [Web-Based Model Deployment](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/deployment-pipelines-and-endpoints/web-based-model-deployment.md) — Publishes trained machine learning models to web interfaces for direct user testing and interaction. ([source](https://github.com/Lordog/dive-into-llms/tree/main/documents/chapter1/))
- [Parameter Efficient Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/parameter-efficient-fine-tuning.md) — Adapts large pre-trained models by updating only a small subset of weights to reduce memory and computational overhead.
- [Neural Network Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-layers.md) — Structures neural networks into discrete, interchangeable blocks for easier customization and optimization.

### Web Development

- [Interactive Model Interfaces](https://awesome-repositories.com/f/web-development/web-interfaces/interactive-model-interfaces.md) — Provides a browser-based platform for deploying and testing trained machine learning models.