Small tweaks to Readme intro

README.md

@@ -4,9 +4,11 @@
   <img src="assets/llama_cute.jpg" width="300" height="300" alt="Cute Llama">
 </p>
 
-With the code in this repo you can train the Llama 2 LLM architecture from scratch in PyTorch, then export the weights to a binary file, and load that into one ~simple 500-line C file ([run.c](run.c)) that inferences the model. Alternatively, you can load, finetune, and inference Meta's Llama 2 (but this is still being actively fleshed out). Hence, this repo is a "fullstack" train + inference solution for Llama 2 LLM, with a focus on minimalism and simplicity. You might think that you need many billion parameter LLMs to do anything useful, but in fact very small LLMs can have surprisingly strong performance if you make the domain narrow enough. I recommend looking at the [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories) paper for inspiration.
+Train the Llama 2 LLM architecture in PyTorch then inference it with one simple 700-line C file ([run.c](run.c)). You might think that you need many billion parameter LLMs to do anything useful, but in fact very small LLMs can have surprisingly strong performance if you make the domain narrow enough (ref: [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories) paper). This repo is a "fullstack" train + inference solution for Llama 2 LLM, with focus on minimalism and simplicity.
 
-Please note that this started recently as just a fun weekend project: I took my earlier [nanoGPT](https://github.com/karpathy/nanoGPT), tuned it to implement the Llama-2 architecture instead of GPT-2, and the meat of it was writing the C inference engine in [run.c](run.c). So the project is young and moving quickly. Hat tip to the awesome [llama.cpp](https://github.com/ggerganov/llama.cpp) for inspiring this project. I wanted something super minimal so I chose to hard-code the Llama 2 architecture, stick to fp32, and just roll one inference file of pure C with no dependencies.
+As the architecture is identical, you can also load and inference Meta's Llama 2 models. However, the current code only inferences models in fp32, so you will most likely not be able to productively load models larger than 7B. Work on model quantization is currently ongoing.
+
+Please note that this repo started recently as a fun weekend project: I took my earlier [nanoGPT](https://github.com/karpathy/nanoGPT), tuned it to implement the Llama-2 architecture instead of GPT-2, and the meat of it was writing the C inference engine in [run.c](run.c). So the project is young and moving quickly. Hat tip to the awesome [llama.cpp](https://github.com/ggerganov/llama.cpp) for inspiring this project. Compred to llama.cpp, I wanted something super simple, minimal, and educational so I chose to hard-code the Llama 2 architecture and just roll one inference file of pure C with no dependencies.
 
 ## feel the magic