Introduction to the LLM Interview Notes Project
The LLM Interview Notes project, a culmination of well-organized concepts related to large language models, serves as a valuable resource for individuals preparing for interviews. Compiled by using various online resources, this repository is a treasure trove for those interested in honing their knowledge and skills in the area of large pre-trained models. It is open for reading and contributions, with the creator actively encouraging feedback, denoting a clear willingness to collaborate and evolve with its users' input.
For those eager to delve into hands-on practice, especially under resource constraints, the creator has launched tiny-llm-zh. This project aims to build a small-scale Chinese large language model and is already available for exploration through the ModeScope Tiny LLM.
Additional Learning Resources
To further enhance understanding, the project recommends several related resources:
- llama3-from-scratch-zh: A guide to implementing the LLaMA3 model from scratch, with capabilities for loading official weights and running locally on a 16GB memory notebook.
- tiny-rag: A simple Retrieval-Augmented Generation (RAG) system that supports multi-recall and reranking, facilitating a quick grasp of search-related concepts.
- AI 工程师八股: Encompasses a broad spectrum of general knowledge including deep learning, machine learning, recommendation systems, and search engines.
Online Reading Platform
For convenient access, the LLM Interview Notes can be read online here: LLMs Interview Note.
Cautionary Note
It is important to mention that the responses within the resource have been personally authored by the creator. As such, any inaccuracies or assumptions should be perceived as opportunities for engagement and correction. The creator invites users to contribute their perspectives for enhancing the content.
Stay updated on new insights and practical interview experiences by following the creator’s WeChat public account. This platform will showcase periodic updates on LLM-related content:
Comprehensive Content Directory
The project spans an extensive range of topics divided into precise sections and chapters:
- Real Interview Questions
- Basics of Large Language Models
- History and Development
- Tokenization and Word Vectors
- Foundation Knowledge in Language Models
- Deep Learning Concepts
- Large Language Model Architectures
- Understanding the Transformer Model
- Attention Mechanisms and BERT Specifics
- Common Large Models Exploration
- Data Training and Distributed Training Techniques
- Supervised Fine-tuning
- Inference Techniques and Optimization Strategies
- Reinforcement Learning with Human Feedback (RLHF)
- Retrieval-Augmented Generation
- Evaluation and Application of Large Language Models
- Assessment and Mitigating Model Hallucinations
- Related Courses and Reference Materials
The directory provides a structured navigation path, making it easier to deep dive into specified topics of interest.
Conclusion
The LLM Interview Notes project stands as an essential guide and resource for enthusiasts in artificial intelligence and large language models. It not only supports interview preparation but also encourages practical implementation and learning. By integrating community contributions, the project continues to grow in relevance and usefulness within the field.