AI Roadmap LLM Learning Roadmap
✨ Awesome-Anything
Awesome-AI-Tutorials
Awesome-AI-Researchs
Awesome-GitHub-Repos
Awesome-AI-Examples🖼️ Digital Image Processing
DIP I Image Demosaicing and Histogram Manipulation
DIP II Image Denoising
DIP III Edge Detection
DIP IV Digital Half-toning
DIP V Geometric Image Modification🍃 LLM Components
1.1 Tokenization
1.2 Embedding
1.3 Attention
1.4 FFN, Residual Addition, and LN
1.5 Positional Encoding
1.6 Architecture & Decoding Policy🌱 LLM Pre-training
2.1 Pre-training Definition
2.2 Pre-training Data
2.3 Pre-training Workflow
2.4 Continued Pre-training (CPT)
2.5 Pre-training Evaluation☘️ LLM Post-Training
3.1 Supervised Fine-Tuning (SFT)
3.2 Reinforcement Learning
3.3 RLHF: Reinforcement Learning from Human Feedback
3.4 Optimizing PPO-Based Algorithms
3.5 Direct Preference Optimization (DPO)🍀 LLM Popular Models
4.1 BERT & Variants
4.2 PaLM Series
4.3 GPT Series
4.4 LLaMA Series
4.5 GLM Series
4.6 Qwen Series
4.7 DeepSeek Series
4.8 MOE Series
4.9 Other🪴 LLM Applications
5.1 Prompt Engineering
5.2 LLM-based Agents
5.3 Retrieval-Augmented Generation (RAG)
5.4 Enterprise-level RAG Practice (I)
5.5 Enterprise-level RAG Practice (II)🌿 LLM Optimization
6.1 GPU Usage Analysis
6.2 Latency Analysis for Inference
6.3 FlashAttention
6.4 PagedAttention
6.5 Packing for Efficient Training
6.6 PEFT🌾 LLM Compression
7.1 Quantization
7.2 Pruning
7.3 Knowledge Distillation🌵 LLM Hands-on Practice
8.1 nanoGPT: Training a GPT from Scratch
8.2 Instruction Fine-Tuning Qwen3-0.6B: A Minimal Working Example
8.3 OpenRLHF: SFT Qwen3-0.6B and Code Analysis
8.4 OpenRLHF: Reward Model Training and Code Analysis
8.5 OpenRLHF: PPO Tutorial and Code Analysis🌴 LLM Must-read Papers
9.1 Technical Reports Explained
9.2 MedLLM
9.3 o1 Techinical Paper
9.4 Prompting
9.5 LLM-as-Judge🌳 LLM Q&A
10.1 Why GRPO training loss starts at 0
10.2 VLM-R1 Reproduction Results
10.3 KL(P||Q) or KL(Q||P)?
10.4 Left Padding or Right Padding?
10.5 What is QK-Norm?
10.6 Why LLM training rarely adopts Dropout?
10.7 Dropout and DropPath🐝 VLM Image Encoders
11.0 Introduction
11.1 Vision Transformer
11.2 CLIP
11.3 BLIP
11.4 EVA
11.6 BLIP-2
11.7 SigLIP📝 MISC.
OpenAI ChatGPT ProgressionAbout
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Study Guide
There’s a lot of content on this site, where should I start?
It’s recommended to start by building a foundation in machine learning and deep learning, for example, by taking Andrew Ng’s Neural Networks and Deep Learning course.
The Digital Image Processing section focuses on traditional image processing techniques and can be learned alongside your machine learning and deep learning journey.
As to LLM, It’s recommended to start from the sections on LLM Components, LLM Pre-Training, LLM Post-Training, and LLM Popular Models to learn the fundamentals of LLMs: how they are built and trained, and common models.
Then dive into the practical examples in the LLM Hands-on Practice and LLM Applications sections. As you run the code, you'll gain a better understanding of its ins and outs and explore more topics such as LLM optimization, model compression, and recent papers in the field.
ComfyAI compiles many great resources in the Awesome-Technology section, including
- AI Tutorial in general like Large Language Model, Diffusion model, Reinforcement Learning, etc.
- Research paper compiles like LLM, CV, GNN, Medical, etc.
- Trending AI tech examples like Nano-Banana-Pro, Gemini 3 Pro
More resources will be added in the future and it’ll be good to use it as a navigation.
LLM Highlight Content
✅ Tokenization: BBPE
✅ Normalization: RMS Norm
✅ FFN Block: SwiGLU
✅ Positional Encoding: RoPE
✅ LLM Pre-training: WSD Scheduler, Mixed Precision
✅ Applications: Prompt Engineering, LLM-based Agents
LLM Hands-On Practice
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