Add llms subdir + update README (#145)

* add llms subdir + update README

* nits

* use same pre-commit as mlx

* update readmes a bit

* format

llms/mixtral/README.md

@@ -0,0 +1,73 @@
+## Mixtral 8x7B
+
+Run the Mixtral[^mixtral] 8x7B mixture-of-experts (MoE) model in MLX on Apple silicon.
+
+This example also supports the instruction fine-tuned Mixtral model.[^instruct]
+
+Note, for 16-bit precision this model needs a machine with substantial RAM (~100GB) to run.
+
+### Setup
+
+Install [Git Large File
+Storage](https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage).
+For example with Homebrew:
+
+```
+brew install git-lfs
+```
+
+Download the models from Hugging Face:
+
+For the base model use:
+
+```
+export MIXTRAL_MODEL=Mixtral-8x7B-v0.1
+```
+
+For the instruction fine-tuned model use:
+
+```
+export MIXTRAL_MODEL=Mixtral-8x7B-Instruct-v0.1
+```
+
+Then run:
+
+```
+GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/mistralai/${MIXTRAL_MODEL}/
+cd $MIXTRAL_MODEL/ && \
+  git lfs pull --include "consolidated.*.pt" && \
+  git lfs pull --include "tokenizer.model"
+```
+
+Now from `mlx-exmaples/mixtral` convert and save the weights as NumPy arrays so
+MLX can read them:
+
+```
+python convert.py --model_path $MIXTRAL_MODEL/
+```
+
+The conversion script will save the converted weights in the same location.
+
+### Generate
+
+As easy as:
+
+```
+python mixtral.py --model_path $MIXTRAL_MODEL/
+```
+
+For more options including how to prompt the model, run:
+
+```
+python mixtral.py --help
+```
+
+For the Instruction model, make sure to follow the prompt format:
+
+```
+[INST] Instruction prompt [/INST]
+```
+
+[^mixtral]: Refer to Mistral's [blog post](https://mistral.ai/news/mixtral-of-experts/) and the [Hugging Face blog post](https://huggingface.co/blog/mixtral) for more details.
+[^instruc]: Refer to the [Hugging Face repo](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) for more
+details