implement LoRA / QLoRA (#46)

* implement LoRA / QLoRA

- example of using MLX to fine-tune an LLM with low rank adaptation (LoRA) for a target task
- see also https://arxiv.org/abs/2106.09685
- based on https://github.com/ml-explore/mlx-examples/tree/main/lora

* add some command line flags I found useful during use
- --quiet -- don't print decorator text, just the generated text
- --prompt @/tmp/file.txt -- load prompt from file

* user can specify path to model OR model identifier in huggingface

* update mlx-swift reference

Co-authored-by: Ashraful Islam <ashraful.meche@gmail.com>
Co-authored-by: JustinMeans <46542161+JustinMeans@users.noreply.github.com>

Applications/LoRATrainingExample/README.md

@@ -0,0 +1,21 @@
+#  LoRATrainingExample
+
+Example application that:
+
+- downloads the `mlx-community/Mistral-7B-v0.1-hf-4bit-mlx` model from huggingface
+- loads the train/valid/test data from `$SRCROOT/Data/lora` (this is copied into the build but you can imagine how it might be downloaded)
+- adds LoRA adapters and trains the model
+- let's you evaluate a prompt against the model
+
+This roughly equates to the command line example in [Tools/llm-tool](../../Tools/llm-tool) and
+you can read more about LoRA there.
+
+This evaluates the LoRA adapted model rather than a fused model.  This doesn't persist
+the LoRA weights or the fused model -- it will retrain it each time the program is launched.
+
+### Troubleshooting
+
+The `mlx-community/Mistral-7B-v0.1-hf-4bit-mlx` model requires a little over 4G of
+memory to load an train -- this may require ~6G of physical RAM.
+
+