Add: mlflow for experiment tracking (#1059) [skip ci]

* Update requirements.txt

adding mlflow

* Update __init__.py

Imports for mlflow

* Update README.md

* Create mlflow_.py (#1)

* Update README.md

* fix precommits

* Update README.md

Update mlflow_tracking_uri

* Update trainer_builder.py

update trainer building

* chore: lint

* make ternary a bit more readable

---------

Co-authored-by: Wing Lian <wing.lian@gmail.com>

README.md

@@ -10,7 +10,7 @@ Features:
 - Integrated with xformer, flash attention, rope scaling, and multipacking
 - Works with single GPU or multiple GPUs via FSDP or Deepspeed
 - Easily run with Docker locally or on the cloud
-- Log results and optionally checkpoints to wandb
+- Log results and optionally checkpoints to wandb or mlflow
 - And more!
 
 
@@ -695,6 +695,10 @@ wandb_name: # Set the name of your wandb run
 wandb_run_id: # Set the ID of your wandb run
 wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training
 
+# mlflow configuration if you're using it
+mlflow_tracking_uri: # URI to mlflow
+mlflow_experiment_name: # Your experiment name
+
 # Where to save the full-finetuned model to
 output_dir: ./completed-model