Add llms subdir + update README (#145)

* add llms subdir + update README

* nits

* use same pre-commit as mlx

* update readmes a bit

* format

README.md

@@ -5,18 +5,37 @@ framework](https://github.com/ml-explore/mlx).
 
 The [MNIST](mnist) example is a good starting point to learn how to use MLX.
 
-Some more useful examples include:
+Some more useful examples are listed below.
+
+### Text Models 
 
 - [Transformer language model](transformer_lm) training.
-- Large scale text generation with [LLaMA](llama), [Mistral](mistral) or [Phi](phi2).  
-- Mixture-of-experts (MoE) language model with [Mixtral 8x7B](mixtral)
+- Large scale text generation with [LLaMA](llms/llama),
+  [Mistral](llms/mistral), [Phi-2](llms/phi2), and more in the [LLMs](llms)
+  directory.
+- A mixture-of-experts (MoE) language model with [Mixtral 8x7B](llms/mixtral).
 - Parameter efficient fine-tuning with [LoRA](lora).
+- Text-to-text multi-task Transformers with [T5](t5).
+- Bidirectional language understanding with [BERT](bert).
+
+### Image Models 
+
 - Generating images with [Stable Diffusion](stable_diffusion).
+
+### Audio Models
+
 - Speech recognition with [OpenAI's Whisper](whisper).
-- Bidirectional language understanding with [BERT](bert)
+
+### Other Models 
+
 - Semi-supervised learning on graph-structured data with [GCN](gcn).
 
-Note: You can now directly download a few converted checkpoints from the [MLX Community](https://huggingface.co/mlx-community) organisation on Hugging Face.
+### Hugging Face
+
+Note: You can now directly download a few converted checkpoints from the [MLX
+Community](https://huggingface.co/mlx-community) organization on Hugging Face.
+We encourage you to join the community and [contribute new
+models](https://github.com/ml-explore/mlx-examples/issues/155).
 
 ## Contributing