# skindhu/build-a-large-language-model-cn

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## Links

- GitHub: https://github.com/skindhu/Build-A-Large-Language-Model-CN
- awesome-repositories: https://awesome-repositories.com/repository/skindhu-build-a-large-language-model-cn.md

## Description

This project is a generative AI educational resource and natural language processing course. It serves as a technical implementation guide for building, pre-training, and fine-tuning a large language model from scratch using PyTorch.

The curriculum provides a step-by-step tutorial on large language model development, focusing specifically on the design of transformer-based text generation models. It includes dedicated instruction on parameter-efficient fine-tuning to optimize training by updating only a small subset of model weights.

The material covers the end-to-end generative AI training pipeline, including the implementation of attention mechanisms and instruction tuning workflows. It details the process of adapting pre-trained models to follow specific user instructions or perform specialized text classification tasks.

## Tags

### Education & Learning Resources

- [Model Building Tutorials](https://awesome-repositories.com/f/education-learning-resources/educational-resources/systems-applied-computing/machine-learning-education/large-language-model-pre-training/model-building-tutorials.md) — Provides a comprehensive guide to designing and training a generative text model from scratch. ([source](https://cdn.jsdelivr.net/gh/skindhu/build-a-large-language-model-cn@main/README.md))
- [Architecture Implementation](https://awesome-repositories.com/f/education-learning-resources/educational-resources/systems-applied-computing/machine-learning-education/large-language-model-pre-training/architecture-implementation.md) — Guides the end-to-end development of a generative text model including attention mechanisms and structural design.
- [LLM Implementation Guides](https://awesome-repositories.com/f/education-learning-resources/llm-implementation-guides.md) — Offers a step-by-step PyTorch tutorial for building, pre-training, and fine-tuning an LLM from scratch.

### Artificial Intelligence & ML

- [Generative AI Training](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-training.md) — Serves as an educational resource for building and optimizing deep learning models for human-like text generation.
- [Instruction Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/instruction-tuning.md) — Implements workflows to adapt pre-trained models to follow specific user instructions.
- [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) — Teaches how to adapt pre-trained models for specific tasks such as text classification and instruction following. ([source](https://cdn.jsdelivr.net/gh/skindhu/build-a-large-language-model-cn@main/README.md))
- [Multi-Head Attention Mechanisms](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-head-attention-mechanisms.md) — Implements multi-head attention mechanisms to capture complex linguistic patterns and long-range dependencies in text.
- [Natural Language Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-processing.md) — Provides a comprehensive curriculum on natural language processing, covering attention and instruction tuning.
- [Parameter Efficient Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/parameter-efficient-fine-tuning.md) — Provides techniques for adapting pre-trained models by updating only a small subset of weights for efficiency.
- [Supervised Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/supervised-fine-tuning.md) — Implements a supervised fine-tuning pipeline using labeled instruction-response pairs to align model behavior.
- [Causal Masking](https://awesome-repositories.com/f/artificial-intelligence-ml/masked-language-modeling/causal-masking.md) — Implements unidirectional causal masks to prevent models from attending to future tokens during training.
- [Model Training Optimizers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training-optimizers.md) — Optimizes model training by reducing memory usage and time through parameter-efficient fine-tuning. ([source](https://cdn.jsdelivr.net/gh/skindhu/build-a-large-language-model-cn@main/README.md))
- [Cross-Entropy Loss Functions](https://awesome-repositories.com/f/artificial-intelligence-ml/prediction-visualization/loss-function-calculators/binary-cross-entropy-calculators/cross-entropy-loss-functions.md) — Uses cross-entropy loss functions to measure prediction error and guide weight updates during model training.
- [Token Embedding Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-embeddings/dense-embeddings/token-embedding-layers.md) — Implements token embedding layers that map discrete text tokens to high-dimensional semantic vectors.