Fixes#513
Implement the Direct Preference Optimization (DPO) method as a Reinforcement Learning from Human Feedback (RLHF) example.
* **Add DPO Functions**: Add `get_batched_logps` and `dpo_loss` functions to `llms/mlx_lm/utils.py` for DPO implementation.
* **Update Training Logic**: Update `llms/mlx_lm/tuner/trainer.py` to include DPO-specific training logic, including a new `dpo_loss` function and condition to check for DPO loss in the training loop.
* **Add Configuration Options**: Add configuration options for DPO in `llms/mlx_lm/examples/lora_config.yaml`.
* **Update Documentation**: Update `llms/mlx_lm/README.md` to include instructions for using DPO.
* **Add Unit Tests**: Add `llms/tests/test_dpo.py` with unit tests for `get_batched_logps`, `dpo_loss`, and DPO-specific training logic.
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