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README.md

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 # LLM101n: Let's build a Storyteller
 
+---
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+**!!! NOTE: this course does not yet exist. It is current being developed by [Eureka Labs](https://eurekalabs.ai). Until it is ready I am archiving this repo !!!**
+
+---
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 ![LLM101n header image](llm101n.jpg)
 
 >  What I cannot create, I do not understand. -Richard Feynman
 
-In this course we will build a Storyteller AI Large Language Model (LLM). Hand in hand, you'll be able create, refine and illustrate little [stories](https://huggingface.co/datasets/roneneldan/TinyStories) with the AI. We are going to build everything end-to-end from basics to a functioning web app similar to ChatGPT, from scratch in Python, C and CUDA, and with minimal computer science prerequisits. By the end you should have a relatively deep understanding of AI, LLMs, and deep learning more generally.
+In this course we will build a Storyteller AI Large Language Model (LLM). Hand in hand, you'll be able to create, refine and illustrate little [stories](https://huggingface.co/datasets/roneneldan/TinyStories) with the AI. We are going to build everything end-to-end from basics to a functioning web app similar to ChatGPT, from scratch in Python, C and CUDA, and with minimal computer science prerequisites. By the end you should have a relatively deep understanding of AI, LLMs, and deep learning more generally.
 
 **Syllabus**
 
-- [Chapter 01](bigram/README.md) **Bigram Language Model** (language modeling)
-- [Chapter 02](micrograd/README.md) **Micrograd** (machine learning, backpropagation)
-- [Chapter 03](mlp/README.md) **N-gram model** (multi-layer perceptron, matmul, gelu)
-- [Chapter 04](attention/README.md) **Attention** (attention, softmax, positional encoder)
-- [Chapter 05](transformer/README.md) **Transformer** (transformer, residual, layernorm, GPT-2)
-- [Chapter 06](tokenization/README.md) **Tokenization** (minBPE, byte pair encoding)
-- [Chapter 07](optimization/README.md) **Optimization** (initialization, optimization, AdamW)
-- [Chapter 08](device/README.md) **Need for Speed I: Device** (device, CPU, GPU, ...)
-- [Chapter 09](precision/README.md) **Need for Speed II: Precision** (mixed precision training, fp16, bf16, fp8, ...)
-- [Chapter 10](distributed/README.md) **Need for Speed III: Distributed** (distributed optimization, DDP, ZeRO)
-- [Chapter 11](datasets/README.md) **Datasets** (datasets, data loading, synthetic data generation)
-- [Chapter 12](inference/README.md) **Inference I: kv-cache** (kv-cache)
-- [Chapter 13](quantization/README.md) **Inference II: Quantization** (quantization)
-- [Chapter 14](sft/README.md) **Finetuning I: SFT** (supervised finetuning SFT, PEFT, LoRA, chat)
-- [Chapter 15](rl/README.md) **Finetuning II: RL** (reinforcement learning, RLHF, PPO, DPO)
-- [Chapter 16](deployment/README.md) **Deployment** (API, web app)
-- [Chapter 17](multimodal/README.md) **Multimodal** (VQVAE, diffusion transformer)
+- Chapter 01 **Bigram Language Model** (language modeling)
+- Chapter 02 **Micrograd** (machine learning, backpropagation)
+- Chapter 03 **N-gram model** (multi-layer perceptron, matmul, gelu)
+- Chapter 04 **Attention** (attention, softmax, positional encoder)
+- Chapter 05 **Transformer** (transformer, residual, layernorm, GPT-2)
+- Chapter 06 **Tokenization** (minBPE, byte pair encoding)
+- Chapter 07 **Optimization** (initialization, optimization, AdamW)
+- Chapter 08 **Need for Speed I: Device** (device, CPU, GPU, ...)
+- Chapter 09 **Need for Speed II: Precision** (mixed precision training, fp16, bf16, fp8, ...)
+- Chapter 10 **Need for Speed III: Distributed** (distributed optimization, DDP, ZeRO)
+- Chapter 11 **Datasets** (datasets, data loading, synthetic data generation)
+- Chapter 12 **Inference I: kv-cache** (kv-cache)
+- Chapter 13 **Inference II: Quantization** (quantization)
+- Chapter 14 **Finetuning I: SFT** (supervised finetuning SFT, PEFT, LoRA, chat)
+- Chapter 15 **Finetuning II: RL** (reinforcement learning, RLHF, PPO, DPO)
+- Chapter 16 **Deployment** (API, web app)
+- Chapter 17 **Multimodal** (VQVAE, diffusion transformer)
 
 **Appendix**
 
@@ -33,10 +39,6 @@ Further topics to work into the progression above:
 - Programming languages: Assembly, C, Python
 - Data types: Integer, Float, String (ASCII, Unicode, UTF-8)
 - Tensor: shapes, views, strides, contiguous, ...
-- Deep Learning frameowrks: PyTorch, JAX
+- Deep Learning frameworks: PyTorch, JAX
 - Neural Net Architecture: GPT (1,2,3,4), Llama (RoPE, RMSNorm, GQA), MoE, ...
 - Multimodal: Images, Audio, Video, VQVAE, VQGAN, diffusion
-
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-
-**Update June 25.** To clarify, the course will take some time to build. There is no specific timeline. Thank you for your interest but please do not submit Issues/PRs.