This repository constitutes some of the resources which I will use to learn about Large Language Models. I will also try to come up with a roadmap as I go forward in this self-learning journey, since a clear roadmap with milestones will be one of the best ways to learn about LLMs in a proper manner.
For this, I will include a mix of theoretical and practical hands-on resources to learn.
- HuggingFace NLP + Transformers Course
- CS25: Transformers United V2, Stanford CS25, Fall 2021 Version
Industrial and Open-Source courses
1. [Activeloop Learn](https://learn.activeloop.ai/), this initiative GenAI360 provides 3 free courses on RAGs, fine-tuning LLMs, LangChain and VectorDBs. 2. [LLM Course by Maxime Labonne](https://github.com/mlabonne/llm-course), this repository hosts the complete roadmap, notebooks for getting into LLMs. 3. [Full Stack Deep Learning](https://fullstackdeeplearning.com/llm-bootcamp/), started out as a deep learning bootcamp and evolved into LLM bootcamp around April 2023, now is free to take up. 4. [LLM University by Cohere](https://docs.cohere.com/docs/llmu), this course consists of 8 modules taught by the famous Luis Serrano, who is known for teaching concepts in a easy and visually appealing manner. The course contains topics like fundamentals, deployment, semantic search and RAG. 5. [Applied LLMs Mastery 2024 Course by Aishwarya N Reganti](https://github.com/aishwaryanr/awesome-generative-ai-guide/tree/main/free_courses/Applied_LLMs_Mastery_2024), free 10 weeks course with a definite roadmap ranging from LLM Fundamentals, Tools and techniques, Deployment and evaluation to Challenges and future trends. 6. [Weights and Biases Courses](https://www.wandb.courses/collections), provides different courses on MLOps, LLM Powered Apps etc. 7. [LLM Models course, DataBricks x ed](https://www.edx.org/certificates/professional-certificate/databricks-large-language-models), professional certification by DataBricks. 8. [Deeplearning.ai](https://www.deeplearning.ai/short-courses/) offers various short courses on LLMs like LangChain for LLM App Development, Serverless LLMs with AWS Bedrock, Fine-tuning LLMs, LLMs with Semantic Search etc. 9. [Introduction to Generative AI Learning Path, Google Cloud](https://www.cloudskillsboost.google/paths/118). 10. [Arize University](https://courses.arize.com/courses/) hosts courses like llm-evaluation, llm agents tools and chains, llm-observability etc.University Courses
1. [CS 324, Stanford](https://stanford-cs324.github.io/winter2022/) 2. [COMP790-101: Large Language Models, UNC Chapel Hill](https://github.com/craffel/llm-seminar) 3. [COS 597G, Princeton](https://www.cs.princeton.edu/courses/archive/fall22/cos597G/) 4. [Large Language Models S-23, ETH Zurich](https://rycolab.io/classes/llm-s23/) 5. [Foundations of Large Language Models, University of Waterloo](https://uwaterloo.ca/watspeed/programs-and-courses/foundations-large-language-models)- AIMultiple's blog on Large Language Models: Complete Guide in 2023
- Cohere Docs
- FutureSmart AI Blog on Building Chatbots using LangChain and ChatGPT
- Task-driven Autonomous Agent Utilizing GPT-4, Pinecone, and LangChain for Diverse Applications
- A Survey of Large Language Models Also check out this Repo: https://github.com/RUCAIBox/LLMSurvey
- Understanding Large Language Models -- A Transformative Reading List, Sebastian Raschka
- Wiki CLSP, NLP Reading Group, a list of reading groups related to NLP which is updated frequently.
- The Inside Story of ChatGPT’s Astonishing Potential | Greg Brockman | TED
- Why AI Is Incredibly Smart — and Shockingly Stupid | Yejin Choi | TED
- H2O Organization, HuggingFaces
- OpenAssistant Organization, HuggingFaces
- DataBricks Organization, HuggingFaces
- BigScience Organization, HuggingFaces
- EleutherAI Organization, HuggingFaces
- NomicAI Organization, HuggingFaces
- Cerebras Organization, HuggingFaces
- LLMStudio, H2O AI
- LLamaIndex
- NeMo Guardrails, NVIDIA, to prevent hallucinations and add programmable guardrails
- MLC LLM, Develop optimize and deploy LLMs natively on everyone's devices)
- LaMini LLM
- ChatGPT, OpenAI, Released 30th November 2022
- Google Bard, Released 21st March 2023
- Tongyi Qianwen AI, Alibaba, Released 11th April 2023
- StableLM, Stability AI, Released 20th April 2023
- Amazon Titan
- HuggingChat, HuggingFaces, Released 25th April 2023
- H2OGPT
- Bloom Model, Commercial Use Allowed with RAIL
- GPT-J, EleutherAI, Apache 2.0
- GPT-NeoX, EleutherAI, Apache 2.0
- GPT4All, NomicAI, MIT License
- GPT4All-J, NomicAI, MIT License
- Pythia, EleutherAI, MIT License
- GLM-130B
- PaLM, Google
- OPT, Meta
- FLAN-T5
- LLaMA, Meta
- Alpaca, Stanford
- Vicuna, lm-sys
People you should definitely follow to keep updated about LLMs. Researchers/Founders/Developers/AI Content Creators involved in LLM production/research/development
- Sebastian Raschka, he is a legend and will burst your hype-up LLM bubble with his amazing tweets, blogs and tutorials. Subscribe to his newsletter Ahead of AI
- Andrej Karpathy, so this legend worked in Tesla, took a break, started his YouTube channel to teach the fundamentals and blew us all with his amazing video on implementing GPT from scratch and finally rejoined OpenAI. I guess you cannot lose a legend :D
- Jay Alammar, yup if you don't know about his ELI blog on Transformers go read that out first and be sure to follow him for updates.
- Tomaz Bratanic, he is the author of famous book Graph Algorithms for Data Science, and currently writes great blogs on Medium related to GPT, Langchain and stuff.