This updates training to use the [`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) dataset.  It also includes improvements to text generation and example notebooks.

Key Changes:
* The `train_dolly.py` notebook now uses Pythia models as the input models and fine tunes using the [`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) dataset.
* Added `InstructionTextGenerationPipeline` for text generation.  This is derived from the code in the model repo, [instruct_pipeline.py](https://huggingface.co/databricks/dolly-v2-12b/blob/main/instruct_pipeline.py).  It has been improved so that it is compatible with the `TextGenerationPipeline` from the `transformers` library.  Some code, such as that in `_forward`, was copied from that pipeline to help with compatibility.  The biggest change relative to the current `instruct_pipeline.py` version is that it returns a list of dicts per instruction, rather than just a dict.  It also now has a `return_full_text` option.  Both of these contribute towards being usable with `langchain`.
* `generate_response` is now a wrapper around `InstructionTextGenerationPipeline`, as the code was all moved there.
* `trainer.py` now uses the local `databricks-dolly-15k.jsonl` dataset.  A `text` column has been constructed from the instruction, context, and response.

Minor Changes:
* Added an `experiment_id` widget to help keep track of different models that are fine tuned.
* Added more options to CLI for configuring training.

Additional Changes:
* Added a `generation.py` example notebook that uses `generate_response` on a couple instructions.
* Added a `langchain.py` example notebook that uses `HuggingFacePipeline ` from `langchain` and `InstructionTextGenerationPipeline ` to test instructions both with and without context.
* Added a `pipeline.py` example notebook that uses `InstructionTextGenerationPipeline` to generate multiple samples per instruction.