This repository provides a comprehensive collection of frequently asked questions and conceptual explanations for Large Model (LLM) algorithm roles in interviews. It serves as a valuable resource for job seekers preparing for technical interviews in the LLM space, offering structured content on foundational concepts, optimization techniques, and practical interview scenarios.

How It Works

The repository is organized into several directories, each covering a specific area of LLM knowledge. It includes foundational topics like CNNs, RNNs, and Transformer architectures, alongside advanced subjects such as model optimization, distributed training, and efficient fine-tuning methods (LoRA, P-Tuning, etc.). The content is presented in Markdown files, with some sections offering practical code examples and Jupyter notebooks for hands-on learning.

Quick Start & Requirements

• Install: conda create -n myPlot python=3.11 followed by conda activate myPlot and pip install -r requirements.txt --proxy=127.0.0.1:10809.
• Prerequisites: Python 3.11, Conda environment management.
• Resources: Requires a proxy for installation if specified.

Highlighted Details

• Covers foundational ML algorithms (CNN, RNN) and core LLM architectures (Transformer).
• Details various LLM optimization and fine-tuning techniques like LoRA, P-Tuning, and PEFT.
• Includes practical interview questions and answers from specific companies (e.g., ant, pdd, liantong).
• Explains distributed training concepts such as Data Parallel and Distributed Data Parallel.

Maintenance & Community

The repository welcomes contributions via Pull Requests (PRs). Specific contributors are acknowledged, with "张老师" and "赵老师" credited for initial ideas and assistance.

Licensing & Compatibility

The repository includes a LICENSE file, but its specific terms are not detailed in the README. Compatibility for commercial use or closed-source linking would require reviewing the LICENSE file.

Limitations & Caveats

The README notes the rapid pace of development in the LLM field, implying that the content may require frequent updates to remain current. The proxy requirement for installation might be a barrier for some users.