Add llms subdir + update README (#145)

* add llms subdir + update README

* nits

* use same pre-commit as mlx

* update readmes a bit

* format

llms/llama/README.md

@@ -0,0 +1,57 @@
+# Llama
+
+An example of generating text with Llama (1 or 2) using MLX.
+
+Llama is a set of open source language models from Meta AI Research[^1][^2]
+ranging from 7B to 70B parameters. This example also supports Meta's Llama Chat
+and Code Llama models, as well as the 1.1B TinyLlama models from SUTD.[^3]
+
+### Setup
+
+Install the dependencies:
+
+```
+pip install -r requirements.txt
+```
+
+Next, download and convert the model. If you do not have access to the model
+weights you will need to request access from Meta:
+
+- [Request Llama v1](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform)
+- [Request Llama v2](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
+
+> [!TIP] Alternatively, you can also download a few converted checkpoints from
+> the [MLX Community](https://huggingface.co/mlx-community) organization on
+> Hugging Face and skip the conversion step.
+
+You can download the TinyLlama models directly from [Hugging
+Face](https://huggingface.co/TinyLlama).
+
+Convert the weights with:
+
+```
+python convert.py --model-path <path_to_torch_model>
+```
+
+For TinyLlama use
+
+```
+python convert.py --model-path <path_to_torch_model> --model-name tiny_llama
+```
+
+The conversion script will save the converted weights in the same location.
+
+### Run
+
+Once you've converted the weights to MLX format, you can interact with the
+LlaMA model:
+
+```
+python llama.py <path_to_model> <path_to_tokenizer.model> --prompt "hello"
+```
+
+Run `python llama.py --help` for more details.
+
+[^1]: For Llama v1 refer to the [arXiv paper](https://arxiv.org/abs/2302.13971) and [blog post](https://ai.meta.com/blog/large-language-model-llama-meta-ai/) for more details.
+[^2]: For Llama v2 refer to the [blob post](https://ai.meta.com/llama/)
+[^3]: For TinyLlama refer to the [gihub repository](https://github.com/jzhang38/TinyLlama?tab=readme-ov-file)