LLM Course Description
What is the project about?
The project is a comprehensive, free course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals (optional, covering math, Python, and neural networks), The LLM Scientist (building optimal LLMs), and The LLM Engineer (creating and deploying LLM-based applications). It includes a series of notebooks and articles. It also has an interactive version in the form of an LLM assistant on HuggingChat and ChatGPT.
What problem does it solve?
The course addresses the need for accessible, structured learning materials for understanding, building, and deploying LLMs. It bridges the gap between theoretical knowledge and practical application, covering everything from fundamental concepts to advanced techniques. It aims to democratize LLM knowledge.
What are the features of the project?
- Structured Curriculum: A three-part roadmap covering fundamentals, building LLMs, and engineering LLM applications.
- Notebooks and Articles: Hands-on Colab notebooks and accompanying articles covering various aspects of LLMs, including fine-tuning, quantization, merging, and evaluation.
- Interactive LLM Assistant: A personalized learning experience via an LLM assistant on HuggingChat and ChatGPT.
- Focus on Open Source: Emphasizes open-source tools, models, and datasets.
- Practical Examples: Provides practical, runnable code examples for many concepts.
- Comprehensive Coverage: Addresses a wide range of topics, from basic math to advanced deployment strategies.
- Roadmap Visualization: Presents a clear visual roadmap for each section of the course.
- Resource Links: Includes extensive links to external resources, papers, and tools.
What are the technologies used in the project?
- Python: The primary programming language.
- Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, PyTorch, Transformers, TRL, llama.cpp, mergekit, axolotl, AutoGPTQ, DeepSpeed, FSDP, Gradio, Streamlit, LangChain, LlamaIndex, and various vector databases (Chroma, Pinecone, Milvus, FAISS, Annoy).
- Platforms: Google Colab, Hugging Face Hub, HuggingChat, ChatGPT, RunPod, ZeroGPU.
- Models: Llama 2, Llama 3, Mistral-7b, CodeLlama, GPT-4, and various other LLMs.
- Formats: GGUF, GPTQ, EXL2, AWQ, HQQ.
What are the benefits of the project?
- Free and Accessible: The course is entirely free and available online.
- Comprehensive Learning: Covers a wide range of LLM topics in a structured manner.
- Hands-on Experience: Provides practical experience through Colab notebooks.
- Up-to-Date: Includes the latest techniques and tools in the LLM field.
- Community Support: Encourages community contributions and provides links to relevant communities.
- Career Advancement: Helps learners develop skills relevant to LLM research and engineering roles.
- Democratizes LLM Knowledge: Makes complex LLM concepts understandable to a wider audience.
What are the use cases of the project?
- Learning LLM Fundamentals: For individuals new to machine learning and LLMs.
- Building Custom LLMs: For researchers and developers who want to fine-tune or create their own LLMs.
- Developing LLM Applications: For engineers building applications that leverage LLMs.
- Staying Up-to-Date: For anyone who wants to keep up with the latest advancements in the LLM field.
- Research and Experimentation: For exploring different LLM techniques and architectures.
- Educational Resource: For educators and students in machine learning and AI courses.
- Personal Projects: Building chatbots, text summarizers, code generators, and other LLM-powered tools.
